data assignment meaning

Your Modern Business Guide To Data Analysis Methods And Techniques

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis. 

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include: 

• Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate. 
• Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes. 
• Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic. 
• Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

• Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
• Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 
• Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis. 

• Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. 
• Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others.  An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario. 
• Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 
• Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others. 
• Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of ​​application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches. 

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world: 

A. Quantitative Methods 

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods. 

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.  

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist. 

Here is an example of how you can use the predictive analysis tool from datapine:

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.  When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events. 

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.  

8. Decision Trees 

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision. 

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely.  Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision.  In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis 

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more. 

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments. 

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic. 

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example. 

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of. 

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all”  and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses.  When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all. 

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading. 

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best. 

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data. 

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next. 

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context. 

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question. 

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service. 

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore,  to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize. 

15. Narrative Analysis 

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others. 

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.  

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study. 

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on. 

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice. 

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data. 

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes. 

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.  

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

4. Think of governance 

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical. 

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole. 

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors. 

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations. 

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

• Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation. 
• Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
• Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

• Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
• Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
• SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
• Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly 

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving. 

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in. 

• Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low. 
• External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high. 
• Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now. 
• Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps. 

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource . 

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail. 

• Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions. 
• Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective. 
• Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them. 
• Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them. 
• Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.   
• Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy. 
• Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way. 
• Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data. 

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

• Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers. 
• Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate. 
• Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient. 
• SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis. 
• Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context. 

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

• By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
• 94% of enterprises say that analyzing data is important for their growth and digital transformation. 
• Companies that exploit the full potential of their data can increase their operating margins by 60% .
• We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis 

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

• Cluster analysis
• Cohort analysis
• Regression analysis
• Factor analysis
• Neural Networks
• Data Mining
• Text analysis
• Time series analysis
• Decision trees
• Conjoint analysis 
• Correspondence Analysis
• Multidimensional Scaling 
• Content analysis 
• Thematic analysis
• Narrative analysis 
• Grounded theory analysis
• Discourse analysis 

Top 17 Data Analysis Techniques:

• Collaborate your needs
• Establish your questions
• Data democratization
• Think of data governance 
• Clean your data
• Set your KPIs
• Omit useless data
• Build a data management roadmap
• Integrate technology
• Answer your questions
• Visualize your data
• Interpretation of data
• Consider autonomous technology
• Build a narrative
• Share the load
• Data Analysis tools
• Refine your process constantly 

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .

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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

• JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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Data science combines math and statistics, specialized programming, advanced  analytics , artificial intelligence (AI)  and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning.

The accelerating volume of data sources, and subsequently data, has made data science is one of the fastest growing field across every industry. As a result, it is no surprise that the role of the data scientist was dubbed the “sexiest job of the 21st century” by  Harvard Business Review  (link resides outside ibm.com). Organizations are increasingly reliant on them to interpret data and provide actionable recommendations to improve business outcomes.

The data science lifecycle involves various roles, tools, and processes, which enables analysts to glean actionable insights. Typically, a data science project undergoes the following stages:

• Data ingestion : The lifecycle begins with the data collection—both raw structured and unstructured data from all relevant sources using a variety of methods. These methods can include manual entry, web scraping, and real-time streaming data from systems and devices. Data sources can include structured data, such as customer data, along with unstructured data like log files, video, audio, pictures, the Internet of Things (IoT) , social media, and more.
• Data storage and data processing : Since data can have different formats and structures, companies need to consider different storage systems based on the type of data that needs to be captured. Data management teams help to set standards around data storage and structure, which facilitate workflows around analytics, machine learning and deep learning models. This stage includes cleaning data, deduplicating, transforming and combining the data using  ETL  (extract, transform, load) jobs or other data integration technologies. This data preparation is essential for promoting data quality before loading into a  data warehouse ,  data lake , or other repository.
• Data analysis : Here, data scientists conduct an exploratory data analysis to examine biases, patterns, ranges, and distributions of values within the data. This data analytics exploration drives hypothesis generation for a/b testing. It also allows analysts to determine the data’s relevance for use within modeling efforts for predictive analytics, machine learning, and/or deep learning. Depending on a model’s accuracy, organizations can become reliant on these insights for business decision making, allowing them to drive more scalability.
• Communicate : Finally, insights are presented as reports and other data visualizations that make the insights—and their impact on business—easier for business analysts and other decision-makers to understand. A data science programming language such as R or Python includes components for generating visualizations; alternately, data scientists can use dedicated visualization tools.

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Data science is considered a discipline, while data scientists are the practitioners within that field. Data scientists are not necessarily directly responsible for all the processes involved in the data science lifecycle. For example, data pipelines are typically handled by data engineers—but the data scientist may make recommendations about what sort of data is useful or required. While data scientists can build machine learning models, scaling these efforts at a larger level requires more software engineering skills to optimize a program to run more quickly. As a result, it’s common for a data scientist to partner with machine learning engineers to scale machine learning models.

Data scientist responsibilities can commonly overlap with a data analyst, particularly with exploratory data analysis and data visualization. However, a data scientist’s skillset is typically broader than the average data analyst. Comparatively speaking, data scientist leverage common programming languages, such as R and Python, to conduct more statistical inference and data visualization.

To perform these tasks, data scientists require computer science and pure science skills beyond those of a typical business analyst or data analyst. The data scientist must also understand the specifics of the business, such as automobile manufacturing, eCommerce, or healthcare.

In short, a data scientist must be able to:

• Know enough about the business to ask pertinent questions and identify business pain points.
• Apply statistics and computer science, along with business acumen, to data analysis.
• Use a wide range of tools and techniques for preparing and extracting data—everything from databases and SQL to data mining to data integration methods.
• Extract insights from big data using predictive analytics and  artificial intelligence  (AI), including  machine learning models ,  natural language processing , and  deep learning .
• Write programs that automate data processing and calculations.
• Tell—and illustrate—stories that clearly convey the meaning of results to decision-makers and stakeholders at every level of technical understanding.
• Explain how the results can be used to solve business problems.
• Collaborate with other data science team members, such as data and business analysts, IT architects, data engineers, and application developers.

These skills are in high demand, and as a result, many individuals that are breaking into a data science career, explore a variety of data science programs, such as certification programs, data science courses, and degree programs offered by educational institutions.

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It may be easy to confuse the terms “data science” and “business intelligence” (BI) because they both relate to an organization’s data and analysis of that data, but they do differ in focus.

Business intelligence (BI) is typically an umbrella term for the technology that enables data preparation, data mining, data management, and data visualization. Business intelligence tools and processes allow end users to identify actionable information from raw data, facilitating data-driven decision-making within organizations across various industries. While data science tools overlap in much of this regard, business intelligence focuses more on data from the past, and the insights from BI tools are more descriptive in nature. It uses data to understand what happened before to inform a course of action. BI is geared toward static (unchanging) data that is usually structured. While data science uses descriptive data, it typically utilizes it to determine predictive variables, which are then used to categorize data or to make forecasts.

Data science and BI are not mutually exclusive—digitally savvy organizations use both to fully understand and extract value from their data.

Data scientists rely on popular programming languages to conduct exploratory data analysis and statistical regression. These open source tools support pre-built statistical modeling, machine learning, and graphics capabilities. These languages include the following (read more at " Python vs. R: What's the Difference? "):

• R Studio:  An open source programming language and environment for developing statistical computing and graphics.
• Python:  It is a dynamic and flexible programming language. The Python includes numerous libraries, such as NumPy, Pandas, Matplotlib, for analyzing data quickly.

To facilitate sharing code and other information, data scientists may use GitHub and Jupyter notebooks.

Some data scientists may prefer a user interface, and two common enterprise tools for statistical analysis include:

• SAS:  A comprehensive tool suite, including visualizations and interactive dashboards, for analyzing, reporting, data mining, and predictive modeling.
• IBM SPSS : Offers advanced statistical analysis, a large library of machine learning algorithms, text analysis, open source extensibility, integration with big data, and seamless deployment into applications.

Data scientists also gain proficiency in using big data processing platforms, such as Apache Spark, the open source framework Apache Hadoop, and NoSQL databases. They are also skilled with a wide range of data visualization tools, including simple graphics tools included with business presentation and spreadsheet applications (like Microsoft Excel), built-for-purpose commercial visualization tools like Tableau and IBM Cognos, and open source tools like D3.js (a JavaScript library for creating interactive data visualizations) and RAW Graphs. For building machine learning models, data scientists frequently turn to several frameworks like PyTorch, TensorFlow, MXNet, and Spark MLib.

Given the steep learning curve in data science, many companies are seeking to accelerate their return on investment for AI projects; they often struggle to hire the talent needed to realize data science project’s full potential. To address this gap, they are turning to multipersona data science and machine learning (DSML) platforms, giving rise to the role of “citizen data scientist.”

Multipersona DSML platforms use automation, self-service portals, and low-code/no-code user interfaces so that people with little or no background in digital technology or expert data science can create business value using data science and machine learning. These platforms also support expert data scientists by also offering a more technical interface. Using a multipersona DSML platform encourages collaboration across the enterprise.

Cloud computing  scales data science by providing access to additional processing power, storage, and other tools required for data science projects.

Since data science frequently leverages large data sets, tools that can scale with the size of the data is incredibly important, particularly for time-sensitive projects. Cloud storage solutions, such as data lakes, provide access to storage infrastructure, which are capable of ingesting and processing large volumes of data with ease. These storage systems provide flexibility to end users, allowing them to spin up large clusters as needed. They can also add incremental compute nodes to expedite data processing jobs, allowing the business to make short-term tradeoffs for a larger long-term outcome. Cloud platforms typically have different pricing models, such a per-use or subscriptions, to meet the needs of their end user—whether they are a large enterprise or a small startup.

Open source technologies are widely used in data science tool sets. When they’re hosted in the cloud, teams don’t need to install, configure, maintain, or update them locally. Several cloud providers, including IBM Cloud®, also offer prepackaged tool kits that enable data scientists to build models without coding, further democratizing access to technology innovations and data insights. 

Enterprises can unlock numerous benefits from data science. Common use cases include process optimization through intelligent automation and enhanced targeting and personalization to improve the customer experience (CX). However, more specific examples include:

Here are a few representative use cases for data science and artificial intelligence:

• An international bank  delivers faster loan services with a mobile app  using machine learning-powered credit risk models and a  hybrid cloud computing  architecture that is both powerful and secure.
• An electronics firm is developing ultra-powerful 3D-printed sensors to guide tomorrow’s driverless vehicles. The solution relies on data science and analytics tools to enhance its real-time object detection capabilities.
• A robotic process automation (RPA) solution provider developed a  cognitive business process mining solution  that reduces incident handling times between 15% and 95% for its client companies. The solution is trained to understand the content and sentiment of customer emails, directing service teams to prioritize those that are most relevant and urgent.
• A digital media technology company created an audience analytics platform that enables its clients to see what’s engaging TV audiences as they’re offered a growing range of digital channels. The solution employs deep analytics and machine learning to gather real-time insights into viewer behavior.
• An  urban police department created statistical incident analysis tools  (link resides outside ibm.com) to help officers understand when and where to deploy resources in order to prevent crime. The data-driven solution creates reports and dashboards to augment situational awareness for field officers.
• Shanghai Changjiang Science and Technology Development used IBM® Watson® technology to build an  AI-based medical assessment platform  that can analyze existing medical records to categorize patients based on their risk of experiencing a stroke and that can predict the success rate of different treatment plans.

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Data Interpretation: Definition and Steps with Examples

A good data interpretation process is key to making your data usable. It will help you make sure you’re drawing the correct conclusions and acting on your information.

No matter what, data is everywhere in the modern world. There are two groups and organizations: those drowning in data or not using it appropriately and those benefiting.

In this blog, you will learn the definition of data interpretation and its primary steps and examples.

What is Data Interpretation

Data interpretation is the process of reviewing data and arriving at relevant conclusions using various analytical research methods. Data analysis assists researchers in categorizing, manipulating data , and summarizing data to answer critical questions.

LEARN ABOUT: Level of Analysis

In business terms, the interpretation of data is the execution of various processes. This process analyzes and revises data to gain insights and recognize emerging patterns and behaviors. These conclusions will assist you as a manager in making an informed decision based on numbers while having all of the facts at your disposal.

Importance of Data Interpretation

Raw data is useless unless it’s interpreted. Data interpretation is important to businesses and people. The collected data helps make informed decisions.

Make better decisions

Any decision is based on the information that is available at the time. People used to think that many diseases were caused by bad blood, which was one of the four humors. So, the solution was to get rid of the bad blood. We now know that things like viruses, bacteria, and immune responses can cause illness and can act accordingly.

In the same way, when you know how to collect and understand data well, you can make better decisions. You can confidently choose a path for your organization or even your life instead of working with assumptions.

The most important thing is to follow a transparent process to reduce mistakes and tiredness when making decisions.

Find trends and take action

Another practical use of data interpretation is to get ahead of trends before they reach their peak. Some people have made a living by researching industries, spotting trends, and then making big bets on them.

LEARN ABOUT: Action Research

With the proper data interpretations and a little bit of work, you can catch the start of trends and use them to help your business or yourself grow. 

Better resource allocation

The last importance of data interpretation we will discuss is the ability to use people, tools, money, etc., more efficiently. For example, If you know via strong data interpretation that a market is underserved, you’ll go after it with more energy and win.

In the same way, you may find out that a market you thought was a good fit is actually bad. This could be because the market is too big for your products to serve, there is too much competition, or something else.

No matter what, you can move the resources you need faster and better to get better results.

What are the steps in interpreting data?

Here are some steps to interpreting data correctly.

Gather the data

The very first step in data interpretation is gathering all relevant data. You can do this by first visualizing it in a bar, graph, or pie chart. This step aims to analyze the data accurately and without bias. Now is the time to recall how you conducted your research.

Here are two question patterns that will help you to understand better.

• Were there any flaws or changes that occurred during the data collection process?
• Have you saved any observatory notes or indicators?

You can proceed to the next stage when you have all of your data.

• Develop your discoveries

This is a summary of your findings. Here, you thoroughly examine the data to identify trends, patterns, or behavior. If you are researching a group of people using a sample population, this is the section where you examine behavioral patterns. You can compare these deductions to previous data sets, similar data sets, or general hypotheses in your industry. This step’s goal is to compare these deductions before drawing any conclusions.

• Draw Conclusions

After you’ve developed your findings from your data sets, you can draw conclusions based on your discovered trends. Your findings should address the questions that prompted your research. If they do not respond, inquire about why; it may produce additional research or questions.

LEARN ABOUT: Research Process Steps

• Give recommendations

The interpretation procedure of data comes to a close with this stage. Every research conclusion must include a recommendation. As recommendations are a summary of your findings and conclusions, they should be brief. There are only two options for recommendations; you can either recommend a course of action or suggest additional research.

Data interpretation examples

Here are two examples of data interpretations to help you understand it better:

Let’s say your users fall into four age groups. So a company can see which age group likes their content or product. Based on bar charts or pie charts, they can develop a marketing strategy to reach uninvolved groups or an outreach strategy to grow their core user base.

Another example of data analysis is the use of recruitment CRM by businesses. They utilize it to find candidates, track their progress, and manage their entire hiring process to determine how they can better automate their workflow.

Overall, data interpretation is an essential factor in data-driven decision-making. It should be performed on a regular basis as part of an iterative interpretation process. Investors, developers, and sales and acquisition professionals can benefit from routine data interpretation. It is what you do with those insights that determine the success of your business.

Contact QuestionPro experts if you need assistance conducting research or creating a data analysis. We can walk you through the process and help you make the most of your data.

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Quantitative Data Analysis: A Comprehensive Guide

By: Ofem Eteng | Published: May 18, 2022

Related Articles

A healthcare giant successfully introduces the most effective drug dosage through rigorous statistical modeling, saving countless lives. A marketing team predicts consumer trends with uncanny accuracy, tailoring campaigns for maximum impact.

Table of Contents

These trends and dosages are not just any numbers but are a result of meticulous quantitative data analysis. Quantitative data analysis offers a robust framework for understanding complex phenomena, evaluating hypotheses, and predicting future outcomes.

In this blog, we’ll walk through the concept of quantitative data analysis, the steps required, its advantages, and the methods and techniques that are used in this analysis. Read on!

What is Quantitative Data Analysis?

Quantitative data analysis is a systematic process of examining, interpreting, and drawing meaningful conclusions from numerical data. It involves the application of statistical methods, mathematical models, and computational techniques to understand patterns, relationships, and trends within datasets.

Quantitative data analysis methods typically work with algorithms, mathematical analysis tools, and software to gain insights from the data, answering questions such as how many, how often, and how much. Data for quantitative data analysis is usually collected from close-ended surveys, questionnaires, polls, etc. The data can also be obtained from sales figures, email click-through rates, number of website visitors, and percentage revenue increase. 

Quantitative Data Analysis vs Qualitative Data Analysis

When we talk about data, we directly think about the pattern, the relationship, and the connection between the datasets – analyzing the data in short. Therefore when it comes to data analysis, there are broadly two types – Quantitative Data Analysis and Qualitative Data Analysis.

Quantitative data analysis revolves around numerical data and statistics, which are suitable for functions that can be counted or measured. In contrast, qualitative data analysis includes description and subjective information – for things that can be observed but not measured.

Let us differentiate between Quantitative Data Analysis and Quantitative Data Analysis for a better understanding.

Data Preparation Steps for Quantitative Data Analysis

Quantitative data has to be gathered and cleaned before proceeding to the stage of analyzing it. Below are the steps to prepare a data before quantitative research analysis:

• Step 1: Data Collection

Before beginning the analysis process, you need data. Data can be collected through rigorous quantitative research, which includes methods such as interviews, focus groups, surveys, and questionnaires.

• Step 2: Data Cleaning

Once the data is collected, begin the data cleaning process by scanning through the entire data for duplicates, errors, and omissions. Keep a close eye for outliers (data points that are significantly different from the majority of the dataset) because they can skew your analysis results if they are not removed.

This data-cleaning process ensures data accuracy, consistency and relevancy before analysis.

• Step 3: Data Analysis and Interpretation

Now that you have collected and cleaned your data, it is now time to carry out the quantitative analysis. There are two methods of quantitative data analysis, which we will discuss in the next section.

However, if you have data from multiple sources, collecting and cleaning it can be a cumbersome task. This is where Hevo Data steps in. With Hevo, extracting, transforming, and loading data from source to destination becomes a seamless task, eliminating the need for manual coding. This not only saves valuable time but also enhances the overall efficiency of data analysis and visualization, empowering users to derive insights quickly and with precision

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Now that you are familiar with what quantitative data analysis is and how to prepare your data for analysis, the focus will shift to the purpose of this article, which is to describe the methods and techniques of quantitative data analysis.

Methods and Techniques of Quantitative Data Analysis

Quantitative data analysis employs two techniques to extract meaningful insights from datasets, broadly. The first method is descriptive statistics, which summarizes and portrays essential features of a dataset, such as mean, median, and standard deviation.

Inferential statistics, the second method, extrapolates insights and predictions from a sample dataset to make broader inferences about an entire population, such as hypothesis testing and regression analysis.

An in-depth explanation of both the methods is provided below:

• Descriptive Statistics
• Inferential Statistics

1) Descriptive Statistics

Descriptive statistics as the name implies is used to describe a dataset. It helps understand the details of your data by summarizing it and finding patterns from the specific data sample. They provide absolute numbers obtained from a sample but do not necessarily explain the rationale behind the numbers and are mostly used for analyzing single variables. The methods used in descriptive statistics include: 

• Mean:   This calculates the numerical average of a set of values.
• Median: This is used to get the midpoint of a set of values when the numbers are arranged in numerical order.
• Mode: This is used to find the most commonly occurring value in a dataset.
• Percentage: This is used to express how a value or group of respondents within the data relates to a larger group of respondents.
• Frequency: This indicates the number of times a value is found.
• Range: This shows the highest and lowest values in a dataset.
• Standard Deviation: This is used to indicate how dispersed a range of numbers is, meaning, it shows how close all the numbers are to the mean.
• Skewness: It indicates how symmetrical a range of numbers is, showing if they cluster into a smooth bell curve shape in the middle of the graph or if they skew towards the left or right.

2) Inferential Statistics

In quantitative analysis, the expectation is to turn raw numbers into meaningful insight using numerical values, and descriptive statistics is all about explaining details of a specific dataset using numbers, but it does not explain the motives behind the numbers; hence, a need for further analysis using inferential statistics.

Inferential statistics aim to make predictions or highlight possible outcomes from the analyzed data obtained from descriptive statistics. They are used to generalize results and make predictions between groups, show relationships that exist between multiple variables, and are used for hypothesis testing that predicts changes or differences.

There are various statistical analysis methods used within inferential statistics; a few are discussed below.

• Cross Tabulations: Cross tabulation or crosstab is used to show the relationship that exists between two variables and is often used to compare results by demographic groups. It uses a basic tabular form to draw inferences between different data sets and contains data that is mutually exclusive or has some connection with each other. Crosstabs help understand the nuances of a dataset and factors that may influence a data point.
• Regression Analysis: Regression analysis estimates the relationship between a set of variables. It shows the correlation between a dependent variable (the variable or outcome you want to measure or predict) and any number of independent variables (factors that may impact the dependent variable). Therefore, the purpose of the regression analysis is to estimate how one or more variables might affect a dependent variable to identify trends and patterns to make predictions and forecast possible future trends. There are many types of regression analysis, and the model you choose will be determined by the type of data you have for the dependent variable. The types of regression analysis include linear regression, non-linear regression, binary logistic regression, etc.
• Monte Carlo Simulation: Monte Carlo simulation, also known as the Monte Carlo method, is a computerized technique of generating models of possible outcomes and showing their probability distributions. It considers a range of possible outcomes and then tries to calculate how likely each outcome will occur. Data analysts use it to perform advanced risk analyses to help forecast future events and make decisions accordingly.
• Analysis of Variance (ANOVA): This is used to test the extent to which two or more groups differ from each other. It compares the mean of various groups and allows the analysis of multiple groups.
• Factor Analysis:   A large number of variables can be reduced into a smaller number of factors using the factor analysis technique. It works on the principle that multiple separate observable variables correlate with each other because they are all associated with an underlying construct. It helps in reducing large datasets into smaller, more manageable samples.
• Cohort Analysis: Cohort analysis can be defined as a subset of behavioral analytics that operates from data taken from a given dataset. Rather than looking at all users as one unit, cohort analysis breaks down data into related groups for analysis, where these groups or cohorts usually have common characteristics or similarities within a defined period.
• MaxDiff Analysis: This is a quantitative data analysis method that is used to gauge customers’ preferences for purchase and what parameters rank higher than the others in the process. 
• Cluster Analysis: Cluster analysis is a technique used to identify structures within a dataset. Cluster analysis aims to be able to sort different data points into groups that are internally similar and externally different; that is, data points within a cluster will look like each other and different from data points in other clusters.
• Time Series Analysis: This is a statistical analytic technique used to identify trends and cycles over time. It is simply the measurement of the same variables at different times, like weekly and monthly email sign-ups, to uncover trends, seasonality, and cyclic patterns. By doing this, the data analyst can forecast how variables of interest may fluctuate in the future. 
• SWOT analysis: This is a quantitative data analysis method that assigns numerical values to indicate strengths, weaknesses, opportunities, and threats of an organization, product, or service to show a clearer picture of competition to foster better business strategies

How to Choose the Right Method for your Analysis?

Choosing between Descriptive Statistics or Inferential Statistics can be often confusing. You should consider the following factors before choosing the right method for your quantitative data analysis:

1. Type of Data

The first consideration in data analysis is understanding the type of data you have. Different statistical methods have specific requirements based on these data types, and using the wrong method can render results meaningless. The choice of statistical method should align with the nature and distribution of your data to ensure meaningful and accurate analysis.

2. Your Research Questions

When deciding on statistical methods, it’s crucial to align them with your specific research questions and hypotheses. The nature of your questions will influence whether descriptive statistics alone, which reveal sample attributes, are sufficient or if you need both descriptive and inferential statistics to understand group differences or relationships between variables and make population inferences.

Pros and Cons of Quantitative Data Analysis

1. Objectivity and Generalizability:

• Quantitative data analysis offers objective, numerical measurements, minimizing bias and personal interpretation.
• Results can often be generalized to larger populations, making them applicable to broader contexts.

Example: A study using quantitative data analysis to measure student test scores can objectively compare performance across different schools and demographics, leading to generalizable insights about educational strategies.

2. Precision and Efficiency:

• Statistical methods provide precise numerical results, allowing for accurate comparisons and prediction.
• Large datasets can be analyzed efficiently with the help of computer software, saving time and resources.

Example: A marketing team can use quantitative data analysis to precisely track click-through rates and conversion rates on different ad campaigns, quickly identifying the most effective strategies for maximizing customer engagement.

3. Identification of Patterns and Relationships:

• Statistical techniques reveal hidden patterns and relationships between variables that might not be apparent through observation alone.
• This can lead to new insights and understanding of complex phenomena.

Example: A medical researcher can use quantitative analysis to pinpoint correlations between lifestyle factors and disease risk, aiding in the development of prevention strategies.

1. Limited Scope:

• Quantitative analysis focuses on quantifiable aspects of a phenomenon ,  potentially overlooking important qualitative nuances, such as emotions, motivations, or cultural contexts.

Example: A survey measuring customer satisfaction with numerical ratings might miss key insights about the underlying reasons for their satisfaction or dissatisfaction, which could be better captured through open-ended feedback.

2. Oversimplification:

• Reducing complex phenomena to numerical data can lead to oversimplification and a loss of richness in understanding.

Example: Analyzing employee productivity solely through quantitative metrics like hours worked or tasks completed might not account for factors like creativity, collaboration, or problem-solving skills, which are crucial for overall performance.

3. Potential for Misinterpretation:

• Statistical results can be misinterpreted if not analyzed carefully and with appropriate expertise.
• The choice of statistical methods and assumptions can significantly influence results.

This blog discusses the steps, methods, and techniques of quantitative data analysis. It also gives insights into the methods of data collection, the type of data one should work with, and the pros and cons of such analysis.

Gain a better understanding of data analysis with these essential reads:

• Data Analysis and Modeling: 4 Critical Differences
• Exploratory Data Analysis Simplified 101
• 25 Best Data Analysis Tools in 2024

Carrying out successful data analysis requires prepping the data and making it analysis-ready. That is where Hevo steps in.

Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing Hevo price , which will assist you in selecting the best plan for your requirements.

Share your experience of understanding Quantitative Data Analysis in the comment section below! We would love to hear your thoughts.

Ofem is a freelance writer specializing in data-related topics, who has expertise in translating complex concepts. With a focus on data science, analytics, and emerging technologies.

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Data Manipulation: Definition, Examples, and Uses

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Have you ever wondered how data enthusiasts turn raw, messy data into meaningful insights that can change the world (or at least, a business)? Imagine you’re given a huge, jumbled-up puzzle. Each piece is a data point, and the picture on the puzzle is the information you want to uncover. Data manipulation is like sorting, arranging, and connecting those puzzle pieces to reveal the bigger picture.

Data Manipulation is one of the initial processes done in Data Analysis . It involves arranging or rearranging data points to make it easier for users/ data analysts to perform necessary insights or business directives. Data Manipulation encompasses a broad range of tools and languages, which may include coding and non-coding techniques. It is not only used extensively by Data Analysts but also by business people and accountants to view the budget of a certain project.

It also has its programming language, DML ( Data Manipulation Language ) which is used to alter data in databases. Let’s know what exactly Data manipulation is.

Table of Content

What is data manipulation, steps required to perform data manipulation, tools used in data manipulation, operations of data manipulation, example of data manipulation, use of data manipulation, data manipulation faqs.

Data Manipulation is the process of manipulating (creating, arranging, deleting) data points in a given data to get insights much easier . We know that about 90% of the data we have are unstructured. Data manipulation is a fundamental step in data analysis , data mining , and data preparation for machine learning and is essential for making informed decisions and drawing conclusions from raw data.

To make use of these data points, we perform data manipulation. It involves:

• Creating a database
• SQL for structured data manipulation
• NoSQL languages like MongoDB for unstructured data manipulation.

The steps we perform in Data Manipulation are:

• Mine the data and create a database: The data is first mined from the internet, either with API requests or Web Scraping, and these data points are structured into a database for further processing.
• Perform data preprocessing: The Data acquired from mining is still a little rough and may have incorrect values, missing values, and some outliers. In this step, all these problems are taken care of, either by deleting the rows or, by adding the mean values in all missing areas (Note: This is only in the case of numerical data.)
• Arrange the data: After the data has been preprocessed, it is arranged accordingly to make analysis of data easier.
• Transform the data: The data in question is transformed, either by changing datatypes or transposing data in some cases.
• Perform Data Analysis: Work with the data to view the result. Create visualizations or an output column to view the output.

We’ll see more on each of these steps in detail below.

Many tools are used in Data Manipulation. Some most popularly known tools with no-code/code Data manipulation functionalities are:

• MS Excel – MS Excel is one of the most popular tools used for data manipulation. It provides a huge array/ variety for freedom/ manipulation of data.
• Power BI – It is a tool used to create interactive dashboards easily. It is provided by Microsoft and can be coded into it.
• Tableau – Tableau has a similar functionality as Power BI , but it is also a data analysis tool where you can manipulate data to create stunning visualizations.

Data Manipulation follows the 4 main operations, CRUD (Create, Read, Update and Delete) . It is used in many industries to improve the overall output.

In most DML , there is some version of the CRUD operations where:

• Create: To create a new data point or database.
• Read: Read the data to understand where we need to perform data manipulation.
• Update: Update missing/wrong data points with the correct ones to encourage data to be streamlined.
• Delete: Deletes the rows with missing data points/ erroneous/ misclassified data.

These 4 main operations are performed in different ways seen below:

• Data Preprocessing : Most of the raw data that is mined may contain errors, missing values and mislabeled data. This will hamper the final output if it is not dealt with in the initial stages.
• Structuring data (if it is unstructured): If there’s any sort of data available in the database which can be structured into a table to query them effectively, we sort those data into tables for greater efficiency.
• Reduce the number of features : As we know, data analysis is inherently computationally intensive. As a result, one of the reasons to perform data manipulation is to find out the optimum number of features needed for getting the result, while discarding the other features. Some techniques used here are, Principal Component Analysis (PCA), Discrete Wavelet Transform and so on.
• Clean the data : Delete unnecessary data points or outliers which may affect the final output. This is done to streamline the output.
• Transforming data : Some insights into data can be improved by transforming the data. This may involve transposing data, and arranging/rearranging them.

Let us see a basic example of Data manipulation in more detail. We can see that there are examples of Data Manipulation that can be used as a baseline. First of all, Import the data, load it and display it.

Considering you have a dataset, you’ll need to load it and display it.

The Iris dataset is viewed below:

Iris Dataset

This reads the Iris Dataset and prints the last 5 values of the Dataset.

Output of iris Dataset

In today’s world where every business has become competitive and undergoing digital transformation, the right data is paramount for all decision-making abilities. Hence, to achieve our results easier and faster, we implement data manipulation.

There are many reasons why we need to manipulate our data. They are:

• Increased Efficiency.
• Less Room for Error.
• Easier to Analyze data.
• Fewer chances for unexpected results.

Due to unrestricted globalization, and near-digitization of all industries, there is a greater need for correct data for good business insights. This calls for even more rigorous Data Manipulation Techniques in both the coding sphere and the lowcode/nocode spheres. Various programming languages and tools, such as Python with libraries like pandas, R , SQL , and Excel, are commonly used for data manipulation tasks. Data Manipulation may be hard if the data mined is unreliable. Hence there are even more regulations on data mining , Data Manipulation and Data Analysis .

1. What tasks can I perform with data manipulation?

Data manipulation allows you to perform tasks like filtering , sorting , aggregation , transformation , cleaning , joining , and data extraction . These operations help you prepare data for analysis, reporting, or visualization.

2. What is the role of SQL in data manipulation?

SQL is essential for working with databases . It allows you to perform operations like SELECT (retrieving data), WHERE (filtering data), GROUP BY (aggregating data), JOIN (combining data from multiple tables), and more, making it a powerful tool for data manipulation.

3. Can I use Excel for data manipulation?

Yes, Excel is a widely used tool for basic data manipulation tasks . It’s user-friendly and suitable for sorting, filtering, performing calculations, and basic data transformations.

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CSE 163, Summer 2020: Homework 3: Data Analysis

In this assignment, you will apply what you've learned so far in a more extensive "real-world" dataset using more powerful features of the Pandas library. As in HW2, this dataset is provided in CSV format. We have cleaned up the data some, but you will need to handle more edge cases common to real-world datasets, including null cells to represent unknown information.

Note that there is no graded testing portion of this assignment. We still recommend writing tests to verify the correctness of the methods that you write in Part 0, but it will be difficult to write tests for Part 1 and 2. We've provided tips in those sections to help you gain confidence about the correctness of your solutions without writing formal test functions!

This assignment is supposed to introduce you to various parts of the data science process involving being able to answer questions about your data, how to visualize your data, and how to use your data to make predictions for new data. To help prepare for your final project, this assignment has been designed to be wide in scope so you can get practice with many different aspects of data analysis. While this assignment might look large because there are many parts, each individual part is relatively small.

Learning Objectives

After this homework, students will be able to:

• Work with basic Python data structures.
• Handle edge cases appropriately, including addressing missing values/data.
• Practice user-friendly error-handling.
• Read plotting library documentation and use example plotting code to figure out how to create more complex Seaborn plots.
• Train a machine learning model and use it to make a prediction about the future using the scikit-learn library.

Expectations

Here are some baseline expectations we expect you to meet:

Follow the course collaboration policies

If you are developing on Ed, all the files are there. The files included are:

• hw3-nces-ed-attainment.csv : A CSV file that contains data from the National Center for Education Statistics. This is described in more detail below.
• hw3.py : The file for you to put solutions to Part 0, Part 1, and Part 2. You are required to add a main method that parses the provided dataset and calls all of the functions you are to write for this homework.
• hw3-written.txt : The file for you to put your answers to the questions in Part 3.
• cse163_utils.py : Provides utility functions for this assignment. You probably don't need to use anything inside this file except importing it if you have a Mac (see comment in hw3.py )

If you are developing locally, you should navigate to Ed and in the assignment view open the file explorer (on the left). Once there, you can right-click to select the option to "Download All" to download a zip and open it as the project in Visual Studio Code.

The dataset you will be processing comes from the National Center for Education Statistics. You can find the original dataset here . We have cleaned it a bit to make it easier to process in the context of this assignment. You must use our provided CSV file in this assignment.

The original dataset is titled: Percentage of persons 25 to 29 years old with selected levels of educational attainment, by race/ethnicity and sex: Selected years, 1920 through 2018 . The cleaned version you will be working with has columns for Year, Sex, Educational Attainment, and race/ethnicity categories considered in the dataset. Note that not all columns will have data starting at 1920.

Our provided hw3-nces-ed-attainment.csv looks like: (⋮ represents omitted rows):

Column Descriptions

• Year: The year this row represents. Note there may be more than one row for the same year to show the percent breakdowns by sex.
• Sex: The sex of the students this row pertains to, one of "F" for female, "M" for male, or "A" for all students.
• Min degree: The degree this row pertains to. One of "high school", "associate's", "bachelor's", or "master's".
• Total: The total percent of students of the specified gender to reach at least the minimum level of educational attainment in this year.
• White / Black / Hispanic / Asian / Pacific Islander / American Indian or Alaska Native / Two or more races: The percent of students of this race and the specified gender to reach at least the minimum level of educational attainment in this year.

Interactive Development

When using data science libraries like pandas , seaborn , or scikit-learn it's extremely helpful to actually interact with the tools your using so you can have a better idea about the shape of your data. The preferred practice by people in industry is to use a Jupyter Notebook, like we have been in lecture, to play around with the dataset to help figure out how to answer the questions you want to answer. This is incredibly helpful when you're first learning a tool as you can actually experiment and get real-time feedback if the code you wrote does what you want.

We recommend that you try figuring out how to solve these problems in a Jupyter Notebook so you can actually interact with the data. We have made a Playground Jupyter Notebook for you that has the data uploaded. At the top-right of this page in Ed is a "Fork" button (looks like a fork in the road). This will make your own copy of this Notebook so you can run the code and experiment with anything there! When you open the Workspace, you should see a list of notebooks and CSV files. You can always access this launch page by clicking the Jupyter logo.

Part 0: Statistical Functions with Pandas

In this part of the homework, you will write code to perform various analytical operations on data parsed from a file.

Part 0 Expectations

• All functions for this part of the assignment should be written in hw3.py .
• For this part of the assignment, you may import and use the math and pandas modules, but you may not use any other imports to solve these problems.
• For all of the problems below, you should not use ANY loops or list/dictionary comprehensions. The goal of this part of the assignment is to use pandas as a tool to help answer questions about your dataset.

Problem 0: Parse data

In your main method, parse the data from the CSV file using pandas. Note that the file uses '---' as the entry to represent missing data. You do NOT need to anything fancy like set a datetime index.

The function to read a CSV file in pandas takes a parameter called na_values that takes a str to specify which values are NaN values in the file. It will replace all occurrences of those characters with NaN. You should specify this parameter to make sure the data parses correctly.

Problem 1: compare_bachelors_1980

What were the percentages for women vs. men having earned a Bachelor's Degree in 1980? Call this method compare_bachelors_1980 and return the result as a DataFrame with a row for men and a row for women with the columns "Sex" and "Total".

The index of the DataFrame is shown as the left-most column above.

Problem 2: top_2_2000s

What were the two most commonly awarded levels of educational attainment awarded between 2000-2010 (inclusive)? Use the mean percent over the years to compare the education levels in order to find the two largest. For this computation, you should use the rows for the 'A' sex. Call this method top_2_2000s and return a Series with the top two values (the index should be the degree names and the values should be the percent).

For example, assuming we have parsed hw3-nces-ed-attainment.csv and stored it in a variable called data , then top_2_2000s(data) will return the following Series (shows the index on the left, then the value on the right)

Hint: The Series class also has a method nlargest that behaves similarly to the one for the DataFrame , but does not take a column parameter (as Series objects don't have columns).

Our assert_equals only checks that floating point numbers are within 0.001 of each other, so your floats do not have to match exactly.

Optional: Why 0.001?

Whenever you work with floating point numbers, it is very likely you will run into imprecision of floating point arithmetic . You have probably run into this with your every day calculator! If you take 1, divide by 3, and then multiply by 3 again you could get something like 0.99999999 instead of 1 like you would expect.

This is due to the fact that there is only a finite number of bits to represent floats so we will at some point lose some precision. Below, we show some example Python expressions that give imprecise results.

Because of this, you can never safely check if one float is == to another. Instead, we only check that the numbers match within some small delta that is permissible by the application. We kind of arbitrarily chose 0.001, and if you need really high accuracy you would want to only allow for smaller deviations, but equality is never guaranteed.

Problem 3: percent_change_bachelors_2000s

What is the difference between total percent of bachelor's degrees received in 2000 as compared to 2010? Take a sex parameter so the client can specify 'M', 'F', or 'A' for evaluating. If a call does not specify the sex to evaluate, you should evaluate the percent change for all students (sex = ‘A’). Call this method percent_change_bachelors_2000s and return the difference (the percent in 2010 minus the percent in 2000) as a float.

For example, assuming we have parsed hw3-nces-ed-attainment.csv and stored it in a variable called data , then the call percent_change_bachelors_2000s(data) will return 2.599999999999998 . Our assert_equals only checks that floating point numbers are within 0.001 of each other, so your floats do not have to match exactly.

Hint: For this problem you will need to use the squeeze() function on a Series to get a single value from a Series of length 1.

Part 1: Plotting with Seaborn

Next, you will write functions to generate data visualizations using the Seaborn library. For each of the functions save the generated graph with the specified name. These methods should only take the pandas DataFrame as a parameter. For each problem, only drop rows that have missing data in the columns that are necessary for plotting that problem ( do not drop any additional rows ).

Part 1 Expectations

• When submitting on Ed, you DO NOT need to specify the absolute path (e.g. /home/FILE_NAME ) for the output file name. If you specify absolute paths for this assignment your code will not pass the tests!
• You will want to pass the parameter value bbox_inches='tight' to the call to savefig to make sure edges of the image look correct!
• For this part of the assignment, you may import the math , pandas , seaborn , and matplotlib modules, but you may not use any other imports to solve these problems.
• For all of the problems below, you should not use ANY loops or list/dictionary comprehensions.
• Do not use any of the other seaborn plotting functions for this assignment besides the ones we showed in the reference box below. For example, even though the documentation for relplot links to another method called scatterplot , you should not call scatterplot . Instead use relplot(..., kind='scatter') like we showed in class. This is not an issue of stylistic preference, but these functions behave slightly differently. If you use these other functions, your output might look different than the expected picture. You don't yet have the tools necessary to use scatterplot correctly! We will see these extra tools later in the quarter.

Part 1 Development Strategy

• Print your filtered DataFrame before creating the graph to ensure you’re selecting the correct data.
• Call the DataFrame describe() method to see some statistical information about the data you've selected. This can sometimes help you determine what to expect in your generated graph.
• Re-read the problem statement to make sure your generated graph is answering the correct question.
• Compare the data on your graph to the values in hw3-nces-ed-attainment.csv. For example, for problem 0 you could check that the generated line goes through the point (2005, 28.8) because of this row in the dataset: 2005,A,bachelor's,28.8,34.5,17.6,11.2,62.1,17.0,16.4,28.0

Seaborn Reference

Of all the libraries we will learn this quarter, Seaborn is by far the best documented. We want to give you experience reading real world documentation to learn how to use a library so we will not be providing a specialized cheat-sheet for this assignment. What we will do to make sure you don't have to look through pages and pages of documentation is link you to some key pages you might find helpful for this assignment; you do not have to use every page we link, so part of the challenge here is figuring out which of these pages you need. As a data scientist, a huge part of solving a problem is learning how to skim lots of documentation for a tool that you might be able to leverage to solve your problem.

We recommend to read the documentation in the following order:

• Start by skimming the examples to see the possible things the function can do. Don't spend too much time trying to figure out what the code is doing yet, but you can quickly look at it to see how much work is involved.
• Then read the top paragraph(s) that give a general overview of what the function does.
• Now that you have a better idea of what the function is doing, go look back at the examples and look at the code much more carefully. When you see an example like the one you want to generate, look carefully at the parameters it passes and go check the parameter list near the top for documentation on those parameters.
• It sometimes (but not always), helps to skim the other parameters in the list just so you have an idea what this function is capable of doing

As a reminder, you will want to refer to the lecture/section material to see the additional matplotlib calls you might need in order to display/save the plots. You'll also need to call the set function on seaborn to get everything set up initially.

Here are the seaborn functions you might need for this assignment:

• Bar/Violin Plot ( catplot )
• Plot a Discrete Distribution ( distplot ) or Continuous Distribution ( kdeplot )
• Scatter/Line Plot ( relplot )
• Linear Regression Plot ( regplot )
• Compare Two Variables ( jointplot )
• Heatmap ( heatmap )

Make sure you read the bullet point at the top of the page warning you to only use these functions!

Problem 0: Line Chart

Plot the total percentages of all people of bachelor's degree as minimal completion with a line chart over years. To select all people, you should filter to rows where sex is 'A'. Label the x-axis "Year", the y-axis "Percentage", and title the plot "Percentage Earning Bachelor's over Time". Name your method line_plot_bachelors and save your generated graph as line_plot_bachelors.png .

Problem 1: Bar Chart

Plot the total percentages of women, men, and total people with a minimum education of high school degrees in the year 2009. Label the x-axis "Sex", the y-axis "Percentage", and title the plot "Percentage Completed High School by Sex". Name your method bar_chart_high_school and save your generated graph as bar_chart_high_school.png .

Do you think this bar chart is an effective data visualization? Include your reasoning in hw3-written.txt as described in Part 3.

Problem 2: Custom Plot

Plot the results of how the percent of Hispanic individuals with degrees has changed between 1990 and 2010 (inclusive) for high school and bachelor's degrees with a chart of your choice. Make sure you label your axes with descriptive names and give a title to the graph. Name your method plot_hispanic_min_degree and save your visualization as plot_hispanic_min_degree.png .

Include a justification of your choice of data visualization in hw3-written.txt , as described in Part 3.

Part 2: Machine Learning using scikit-learn

Now you will be making a simple machine learning model for the provided education data using scikit-learn . Complete this in a function called fit_and_predict_degrees that takes the data as a parameter and returns the test mean squared error as a float. This may sound like a lot, so we've broken it down into steps for you:

• Filter the DataFrame to only include the columns for year, degree type, sex, and total.
• Do the following pre-processing: Drop rows that have missing data for just the columns we are using; do not drop any additional rows . Convert string values to their one-hot encoding. Split the columns as needed into input features and labels.
• Randomly split the dataset into 80% for training and 20% for testing.
• Train a decision tree regressor model to take in year, degree type, and sex to predict the percent of individuals of the specified sex to achieve that degree type in the specified year.
• Use your model to predict on the test set. Calculate the accuracy of your predictions using the mean squared error of the test dataset.

You do not need to anything fancy like find the optimal settings for parameters to maximize performance. We just want you to start simple and train a model from scratch! The reference below has all the methods you will need for this section!

scikit-learn Reference

You can find our reference sheet for machine learning with scikit-learn ScikitLearnReference . This reference sheet has information about general scikit-learn calls that are helpful, as well as how to train the tree models we talked about in class. At the top-right of this page in Ed is a "Fork" button (looks like a fork in the road). This will make your own copy of this Notebook so you can run the code and experiment with anything there! When you open the Workspace, you should see a list of notebooks and CSV files. You can always access this launch page by clikcing the Jupyter logo.

Part 2 Development Strategy

Like in Part 1, it can be difficult to write tests for this section. Machine Learning is all about uncertainty, and it's often difficult to write tests to know what is right. This requires diligence and making sure you are very careful with the method calls you make. To help you with this, we've provided some alternative ways to gain confidence in your result:

• Print your test y values and your predictions to compare them manually. They won't be exactly the same, but you should notice that they have some correlation. For example, I might be concerned if my test y values were [2, 755, …] and my predicted values were [1022, 5...] because they seem to not correlate at all.
• Calculate your mean squared error on your training data as well as your test data. The error should be lower on your training data than on your testing data.

Optional: ML for Time Series

Since this is technically time series data, we should point out that our method for assessing the model's accuracy is slightly wrong (but we will keep it simple for our HW). When working with time series, it is common to use the last rows for your test set rather than random sampling (assuming your data is sorted chronologically). The reason is when working with time series data in machine learning, it's common that our goal is to make a model to help predict the future. By randomly sampling a test set, we are assessing the model on its ability to predict in the past! This is because it might have trained on rows that came after some rows in the test set chronologically. However, this is not a task we particularly care that the model does well at. Instead, by using the last section of the dataset (the most recent in terms of time), we are now assessing its ability to predict into the future from the perspective of its training set.

Even though it's not the best approach to randomly sample here, we ask you to do it anyways. This is because random sampling is the most common method for all other data types.

Part 3: Written Responses

Review the source of the dataset here . For the following reflection questions consider the accuracy of data collected, and how it's used as a public dataset (e.g. presentation of data, publishing in media, etc.). All of your answers should be complete sentences and show thoughtful responses. "No" or "I don't know" or any response like that are not valid responses for any questions. There is not one particularly right answer to these questions, instead, we are looking to see you use your critical thinking and justify your answers!

• Do you think the bar chart from part 1b is an effective data visualization? Explain in 1-2 sentences why or why not.
• Why did you choose the type of plot that you did in part 1c? Explain in a few sentences why you chose this type of plot.
• Datasets can be biased. Bias in data means it might be skewed away from or portray a wrong picture of reality. The data might contain inaccuracies or the methods used to collect the data may have been flawed. Describe a possible bias present in this dataset and why it might have occurred. Your answer should be about 2 or 3 sentences long.

Context : Later in the quarter we will talk about ethics and data science. This question is supposed to be a warm-up to get you thinking about our responsibilities having this power to process data. We are not trying to train to misuse your powers for evil here! Most misuses of data analysis that result in ethical concerns happen unintentionally. As preparation to understand these unintentional consequences, we thought it would be a good exercise to think about a theoretical world where you would willingly try to misuse data.

Congrats! You just got an internship at Evil Corp! Your first task is to come up with an application or analysis that uses this dataset to do something unethical or nefarious. Describe a way that this dataset could be misused in some application or an analysis (potentially using the bias you identified for the last question). Regardless of what nefarious act you choose, evil still has rules: You need to justify why using the data in this is a misuse and why a regular person who is not evil (like you in the real world outside of this problem) would think using the data in this way would be wrong. There are no right answers here of what defines something as unethical, this is why you need to justify your answer! Your response should be 2 to 4 sentences long.

Turn your answers to these question in by writing them in hw3-written.txt and submitting them on Ed

Your submission will be evaluated on the following dimensions:

• Your solution correctly implements the described behaviors. You will have access to some tests when you turn in your assignment, but we will withhold other tests to test your solution when grading. All behavior we test is completely described by the problem specification or shown in an example.
• No method should modify its input parameters.
• Your main method in hw3.py must call every one of the methods you implemented in this assignment. There are no requirements on the format of the output, besides that it should save the files for Part 1 with the proper names specified in Part 1.
• We can run your hw3.py without it crashing or causing any errors or warnings.
• When we run your code, it should produce no errors or warnings.
• All files submitted pass flake8
• All program files should be written with good programming style. This means your code should satisfy the requirements within the CSE 163 Code Quality Guide .
• Any expectations on this page or the sub-pages for the assignment are met as well as all requirements for each of the problems are met.

Make sure you carefully read the bullets above as they may or may not change from assignment to assignment!

A note on allowed material

A lot of students have been asking questions like "Can I use this method or can I use this language feature in this class?". The general answer to this question is it depends on what you want to use, what the problem is asking you to do and if there are any restrictions that problem places on your solution.

There is no automatic deduction for using some advanced feature or using material that we have not covered in class yet, but if it violates the restrictions of the assignment, it is possible you will lose points. It's not possible for us to list out every possible thing you can't use on the assignment, but we can say for sure that you are safe to use anything we have covered in class so far as long as it meets what the specification asks and you are appropriately using it as we showed in class.

For example, some things that are probably okay to use even though we didn't cover them:

• Using the update method on the set class even though I didn't show it in lecture. It was clear we talked about sets and that you are allowed to use them on future assignments and if you found a method on them that does what you need, it's probably fine as long as it isn't violating some explicit restriction on that assignment.
• Using something like a ternary operator in Python. This doesn't make a problem any easier, it's just syntax.

For example, some things that are probably not okay to use:

• Importing some random library that can solve the problem we ask you to solve in one line.
• If the problem says "don't use a loop" to solve it, it would not be appropriate to use some advanced programming concept like recursion to "get around" that restriction.

These are not allowed because they might make the problem trivially easy or violate what the learning objective of the problem is.

You should think about what the spec is asking you to do and as long as you are meeting those requirements, we will award credit. If you are concerned that an advanced feature you want to use falls in that second category above and might cost you points, then you should just not use it! These problems are designed to be solvable with the material we have learned so far so it's entirely not necessary to go look up a bunch of advanced material to solve them.

tl;dr; We will not be answering every question of "Can I use X" or "Will I lose points if I use Y" because the general answer is "You are not forbidden from using anything as long as it meets the spec requirements. If you're unsure if it violates a spec restriction, don't use it and just stick to what we learned before the assignment was released."

This assignment is due by Thursday, July 23 at 23:59 (PDT) .

You should submit your finished hw3.py , and hw3-written.txt on Ed .

You may submit your assignment as many times as you want before the late cutoff (remember submitting after the due date will cost late days). Recall on Ed, you submit by pressing the "Mark" button. You are welcome to develop the assignment on Ed or develop locally and then upload to Ed before marking.

• A Comprehensive Guide to Data Mining Assignments

A Guide to Data Mining Assignments: Important Topics and How to Solve Them

In this comprehensive guide, you'll find essential topics to solve your data mining assignment successfully. From data preprocessing and exploration to classification, clustering, and association rule mining, we cover all the key concepts you need to know. Learn how to approach your assignment step by step, including data preparation, algorithm implementation, and result interpretation. By following our tips and structured approach, you'll be well-equipped to tackle and complete your data mining assignment with confidence.

Introduction to Data Mining

Data mining is the process of discovering patterns, trends, correlations, or useful information from large datasets. It involves extracting knowledge from data and using various techniques to analyze and interpret the data. Before diving into data mining assignments, you should have a solid understanding of the fundamental concepts and techniques in this field.

Important Topics in Data Mining

Important topics in data mining encompass data preprocessing, exploration, classification, clustering, association rule mining, and regression analysis. These fundamental concepts form the backbone of effective data analysis and provide valuable insights from vast datasets.

1. Data Preprocessing

Data preprocessing involves cleaning the data to remove any inconsistencies, handling missing values, dealing with noisy data, and transforming the data into a more meaningful representation.

When faced with assignments related to data preprocessing, start by thoroughly understanding the dataset provided. Identify missing values and decide on appropriate methods to handle them, such as mean imputation or interpolation. Remove duplicate records and outliers that might negatively impact the analysis. Data normalization and scaling may be necessary to bring different features to a common scale.

In solving data preprocessing assignments, the key is to demonstrate your understanding of the various techniques and their appropriate applications. Explain the rationale behind your choices and discuss the implications of each preprocessing step on the final results. A well-prepared and clean dataset sets the foundation for accurate and insightful data mining, making data preprocessing a critical aspect of your overall assignment success.

2. Data Exploration and Visualization

Data Exploration and Visualization play a vital role in data mining assignments, allowing you to gain insights and make informed decisions from raw data. This topic involves examining the dataset's characteristics, distributions, patterns, and correlations between variables. It helps identify outliers, data trends, and potential relationships that aid in choosing appropriate data mining techniques.

To solve assignments related to data exploration and visualization, start by understanding the dataset and its attributes. Utilize descriptive statistics and data visualization techniques like scatter plots, histograms, and heatmaps to explore the data. Look for trends, anomalies, and interesting patterns that could be relevant to the assignment objectives.

Consider employing advanced visualization methods like line charts, bar charts, or geographic maps to present findings effectively. Pay attention to data scaling and normalization to ensure accurate representations. Additionally, interpret the visualizations, drawing meaningful conclusions to support your analysis and overall assignment objectives. Strong data exploration and visualization skills will enable you to present compelling insights and solve data mining assignments more effectively.

3. Classification

Classification is a fundamental topic in data mining, focusing on supervised learning algorithms that assign data instances to predefined classes or categories. In this context, solving assignments related to classification involves understanding and applying various algorithms effectively.

To excel in classification assignments, start by comprehending the data and the problem statement. Preprocess the data, handle missing values, and split it into training and testing sets. Next, explore the data to identify patterns and relevant features. Select suitable classification algorithms such as Decision Trees, Support Vector Machines (SVM), or Random Forests based on the data characteristics and task requirements.

Implement the chosen algorithms using programming languages or data mining tools, and fine-tune their parameters to optimize performance. Evaluate the models using metrics like accuracy, precision, recall, and F1-score. Finally, interpret the results to provide meaningful insights into the classification process. By mastering classification techniques and applying them diligently, you can confidently solve assignments, make informed predictions, and contribute to solving real-world problems with data mining.

4. Clustering

Clustering is a fundamental data mining topic that involves grouping similar data points together based on their intrinsic characteristics. In essence, it helps identify patterns and structures within data without any predefined labels. When facing assignments related to clustering, understanding the underlying principles is crucial.

To solve clustering assignments effectively, start by comprehending the various clustering algorithms such as K-Means, Hierarchical Clustering, and DBSCAN. Each algorithm has its strengths and weaknesses, which makes it essential to select the most suitable one for the given dataset and objectives. Preprocess the data to handle missing values and scale the features appropriately. Visualize the data to gain insights into its distribution and potential groupings.

Implement the chosen clustering algorithm and evaluate its performance using metrics like silhouette score or Davies-Bouldin index. Interpret the results to gain meaningful insights into the underlying patterns within the data. Mastering these techniques and understanding their applications will empower you to excel in clustering assignments and effectively extract valuable knowledge from unlabeled datasets.

5. Association Rule Mining

Association Rule Mining is a data mining technique that aims to discover interesting relationships, patterns, or associations among items in large datasets. It is widely used in market basket analysis, where the goal is to identify combinations of products frequently purchased together. To solve assignments related to Association Rule Mining, you'll need to follow these steps:

• Data Preprocessing: Clean and preprocess the dataset to eliminate noise and irrelevant information.
• Frequent Itemset Generation: Identify itemsets (combinations of items) that occur frequently in the data.
• Support and Confidence Calculation: Calculate support and confidence measures for each itemset to determine the significance of associations.
• Rule Generation: Generate association rules based on the support and confidence thresholds set by the assignment.
• Rule Evaluation: Evaluate the generated rules using additional metrics like lift or conviction to select the most meaningful and relevant associations.
• Interpretation: Interpret the results obtained from the analysis, explaining the discovered associations and their potential implications.

Mastering Association Rule Mining techniques and understanding how to apply them effectively will empower you to confidently approach assignments in this area and unearth valuable insights from transactional data.

6. Regression Analysis

Regression analysis is a crucial topic in data mining that involves modeling the relationship between a dependent variable and one or more independent variables. In simple terms, it helps us understand how changes in the independent variables affect the dependent variable. This technique is widely used for prediction and forecasting tasks.

When solving assignments related to regression analysis, start by understanding the assignment's context and the specific regression method required (e.g., linear regression, polynomial regression). Next, preprocess the data, handle outliers, and split it into training and testing sets. Implement the chosen regression algorithm using programming languages like Python or R. Interpret the results to draw meaningful conclusions and provide valuable insights.

By mastering regression analysis and its application, you can effectively solve assignments that involve predicting outcomes based on data patterns and uncover meaningful relationships between variables.

Solving Data Mining Assignments

To excel in solving data mining assignments, remember to start early, experiment with different techniques, and document your process. Seek help when needed, and focus on interpreting results to gain valuable insights from your analysis.

a) Understand the Assignment Requirements

Understanding the assignment requirements is the first crucial step in tackling data mining tasks. Carefully analyze the problem statement to identify the specific tasks, data, and techniques needed. Pay attention to any constraints or guidelines provided by the instructor. A clear understanding of the requirements ensures that you focus on the right aspects of the assignment and approach it with a well-defined plan, increasing your chances of success.

b) Data Preparation

Data preparation is a critical step in data mining assignments. Before applying any analysis technique, it is essential to preprocess the data to ensure accuracy and reliability. This phase involves cleaning the data, handling missing values, dealing with outliers, and transforming the data into a suitable format for analysis. Data that is well-prepared sets the foundation for successful data mining, leading to more accurate and meaningful results. Ignoring data preparation can lead to erroneous conclusions and unreliable insights.

c) Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) is a fundamental technique in data mining that involves visually and statistically exploring datasets to gain valuable insights. When conducting EDA, you'll use various plots, charts, and summary statistics to understand data distributions, identify patterns, and detect outliers. This crucial step helps you uncover hidden trends and relationships, guiding your subsequent data mining process. EDA ensures that you have a comprehensive understanding of the data, enabling more informed decisions and leading to successful data mining assignments.

d) Selecting Data Mining Techniques

Selecting the right data mining techniques is a critical step in any data mining assignment. Understanding the nature of the data and the objectives of the analysis will help guide your choices. Consider factors like the type of task (classification, clustering, etc.) and the complexity of the dataset. By carefully evaluating and choosing appropriate techniques, you can ensure accurate and insightful results. Remember to experiment with multiple algorithms to find the most suitable approach for extracting valuable knowledge from your data.

e) Implementing Algorithms

When it comes to implementing algorithms in data mining assignments, it's essential to select the appropriate technique based on the task at hand. Utilize programming languages or data mining software like Python's scikit-learn or Java's Weka to put the chosen algorithms into action. This step is where you translate theoretical knowledge into practical solutions, allowing you to analyze datasets and extract valuable patterns, associations, and insights to solve complex data mining problems effectively.

f) Model Evaluation

Model evaluation is a critical step in data mining assignments to assess the performance and effectiveness of the developed models. Through various metrics like accuracy, precision, recall, and F1-score, you can measure how well your model predicts or classifies data. By comparing different models and choosing the one with the highest performance, you ensure that your data mining assignment delivers reliable and meaningful results, providing valuable insights to the given problem or dataset.

g) Interpreting Results

Interpreting results is a critical aspect of data mining assignments, as it involves making sense of the outcomes generated by various techniques. This step enables you to draw meaningful insights from the data and understand the implications of your analysis. By carefully examining the patterns, trends, and relationships discovered during the data mining process, you can provide valuable conclusions and recommendations. Accurate interpretation is key to delivering a comprehensive and insightful report, ensuring the success of your data mining assignment.

Data mining assignments offer valuable opportunities to apply your knowledge and skills in analyzing real-world datasets. In conclusion, this comprehensive guide covers essential topics and techniques to help you successfully complete your data mining assignment. Understanding data preprocessing, exploration, classification, clustering, association rule mining, and regression analysis lays the foundation for effective problem-solving. By following a structured approach and leveraging various data mining algorithms, you can confidently tackle assignments, gain valuable insights from the data, and interpret the results accurately. With these newfound skills and knowledge, you are well-equipped to excel in your data mining endeavors and achieve success in your academic or professional pursuits.

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A comprehensive guide to data mining assignments submit your assignment, attached files.

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What Is Data Mining?

• How It Works
• Data Warehousing & Mining Software
• The Process
• Applications
• Advantages and Disadvantages

Data Mining and Social Media

The bottom line.

• Marketing Essentials

What Is Data Mining? How It Works, Benefits, Techniques, and Examples

Data mining is the process of searching and analyzing a large batch of raw data in order to identify patterns and extract useful information.

Companies use data mining software to learn more about their customers. It can help them to develop more effective marketing strategies, increase sales, and decrease costs. Data mining relies on effective data collection ,  warehousing , and computer processing.

Key Takeaways

• Data mining is the process of analyzing a large batch of information to discern trends and patterns.
• Data mining can be used by corporations for everything from learning about what customers are interested in or want to buy to fraud detection and spam filtering.
• Data mining programs break down patterns and connections in data based on what information users request or provide.
• Social media companies use data mining techniques to commodify their users in order to generate profit.
• This use of data mining has come under criticism as users are often unaware of the data mining happening with their personal information, especially when it is used to influence preferences.

Investopedia / Julie Bang

How Data Mining Works

Data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends. It is used in credit risk management, fraud detection , and spam filtering. It also is a market research tool that helps reveal the sentiment or opinions of a given group of people. The data mining process breaks down into four steps:

• Data is collected and loaded into data warehouses on site or on a cloud service.
• Business analysts, management teams, and information technology professionals access the data and determine how they want to organize it.
• Custom application software sorts and organizes the data.
• The end user presents the data in an easy-to-share format, such as a graph or table.

Data Warehousing and Mining Software

Data mining programs analyze relationships and patterns in data based on user requests. It organizes information into classes.

For example, a restaurant may want to use data mining to determine which specials it should offer and on what days. The data can be organized into classes based on when customers visit and what they order .

In other cases, data miners find clusters of information based on logical relationships or look at associations and sequential patterns to draw conclusions about trends in consumer behavior.

Warehousing is an important aspect of data mining. Warehousing is the centralization of an organization's data into one database or program. It allows the organization to spin off segments of data for specific users to analyze and use depending on their needs.

Cloud data warehouse solutions use the space and power of a cloud provider to store data. This allows smaller companies to leverage digital solutions for storage, security, and analytics.

Data Mining Techniques

Data mining uses algorithms and various other techniques to convert large collections of data into useful output. The most popular types of data mining techniques include association rules, classification, clustering, decision trees, K-Nearest Neighbor, neural networks, and predictive analysis.

• Association rules , also referred to as market basket analysis, search for relationships between variables. This relationship in itself creates additional value within the data set as it strives to link pieces of data. For example, association rules would search a company's sales history to see which products are most commonly purchased together; with this information, stores can plan, promote, and forecast.
• Classification uses predefined classes to assign to objects. These classes describe the characteristics of items or represent what the data points have in common with each other. This data mining technique allows the underlying data to be more neatly categorized and summarized across similar features or product lines.
• Clustering is similar to classification. However, clustering identifies similarities between objects, then groups those items based on what makes them different from other items. While classification may result in groups such as "shampoo," "conditioner," "soap," and "toothpaste," clustering may identify groups such as "hair care" and "dental health."
• Decision trees are used to classify or predict an outcome based on a set list of criteria or decisions. A decision tree is used to ask for the input of a series of cascading questions that sort the dataset based on the responses given. Sometimes depicted as a tree-like visual, a decision tree allows for specific direction and user input when drilling deeper into the data.
• K-Nearest Neighbor (KNN) is an algorithm that classifies data based on its proximity to other data. The basis for KNN is rooted in the assumption that data points that are close to each other are more similar to each other than other bits of data. This non-parametric, supervised technique is used to predict the features of a group based on individual data points.
• Neural networks process data through the use of nodes. These nodes are comprised of inputs, weights, and an output. Data is mapped through supervised learning, similar to how the human brain is interconnected. This model can be programmed to give threshold values to determine a model's accuracy.
• Predictive analysis strives to leverage historical information to build graphical or mathematical models to forecast future outcomes. Overlapping with regression analysis , this technique aims to support an unknown figure in the future based on current data on hand.

The Data Mining Process

To be most effective, data analysts generally follow a certain flow of tasks along the data mining process. Without this structure, an analyst may encounter an issue in the middle of their analysis that could have easily been prevented had they prepared for it earlier. The data mining process is usually broken into the following steps.

Step 1: Understand the Business

Before any data is touched, extracted, cleaned, or analyzed, it is important to understand the underlying entity and the project at hand. What are the goals the company is trying to achieve by mining data? What is their current business situation? What are the findings of a SWOT analysis ? Before looking at any data, the mining process starts by understanding what will define success at the end of the process.

Step 2: Understand the Data

Once the business problem has been clearly defined, it's time to start thinking about data. This includes what sources are available, how they will be secured and stored, how the information will be gathered, and what the final outcome or analysis may look like. This step also includes determining the limits of the data, storage, security, and collection and assesses how these constraints will affect the data mining process.

Step 3: Prepare the Data

Data is gathered, uploaded, extracted, or calculated. It is then cleaned, standardized, scrubbed for outliers, assessed for mistakes, and checked for reasonableness. During this stage of data mining, the data may also be checked for size as an oversized collection of information may unnecessarily slow computations and analysis.

Step 4: Build the Model

With a clean data set in hand, it's time to crunch the numbers. Data scientists use the types of data mining above to search for relationships, trends, associations, or sequential patterns. The data may also be fed into predictive models to assess how previous bits of information may translate into future outcomes.

Step 5: Evaluate the Results

The data-centered aspect of data mining concludes by assessing the findings of the data model or models. The outcomes from the analysis may be aggregated, interpreted, and presented to decision-makers that have largely been excluded from the data mining process to this point. In this step, organizations can choose to make decisions based on the findings.

Step 6: Implement Change and Monitor

The data mining process concludes with management taking steps in response to the findings of the analysis. The company may decide the information was not strong enough or the findings were not relevant, or the company may strategically pivot based on findings. In either case, management reviews the ultimate impacts of the business and recreates future data mining loops by identifying new business problems or opportunities.

Different data mining processing models will have different steps, though the general process is usually pretty similar. For example, the Knowledge Discovery Databases model has nine steps, the CRISP-DM model has six steps, and the SEMMA process model has five steps.

Applications of Data Mining

In today's age of information, almost any department, industry, sector , or company can make use of data mining.

Data mining encourages smarter, more efficient use of capital to drive revenue growth. Consider the point-of-sale register at your favorite local coffee shop. For every sale, that coffeehouse collects the time a purchase was made and what products were sold. Using this information, the shop can strategically craft its product line.

Once the coffeehouse knows its ideal line-up, it's time to implement the changes. However, to make its marketing efforts more effective, the store can use data mining to understand where its clients see ads, what demographics to target, where to place digital ads, and what marketing strategies most resonate with customers. This includes aligning marketing campaigns , promotional offers, cross-sell offers, and programs to the findings of data mining.

Manufacturing

For companies that produce their own goods, data mining plays an integral part in analyzing how much each raw material costs, what materials are being used most efficiently, how time is spent along the manufacturing process, and what bottlenecks negatively impact the process. Data mining helps ensure the flow of goods is uninterrupted.

Fraud Detection

The heart of data mining is finding patterns, trends, and correlations that link data points together. Therefore, a company can use data mining to identify outliers or correlations that should not exist. For example, a company may analyze its cash flow and find a reoccurring transaction to an unknown account. If this is unexpected, the company may wish to investigate whether funds are being mismanaged.

Human Resources

Human resources departments often have a wide range of data available for processing including data on retention, promotions, salary ranges, company benefits, use of those benefits, and employee satisfaction surveys. Data mining can correlate this data to get a better understanding of why employees leave and what entices new hires.

Customer Service

Customer satisfaction may be caused (or destroyed) by many events or interactions. Imagine a company that ships goods. A customer may be dissatisfied with shipping times, shipping quality, or communications. The same customer may be frustrated with long telephone wait times or slow e-mail responses. Data mining gathers operational information about customer interactions and summarizes the findings to pinpoint weak points and highlight what the company is doing right.

Advantages and Disadvantages of Data Mining

It drives profitability and efficiency

It can be applied to any type of data and business problem

It can reveal hidden information and trends

It is complex

Results and benefits are not guaranteed

It can be expensive

Pros Explained

• Profitability and efficiency : Data mining ensures a company is collecting and analyzing reliable data. It is often a more rigid, structured process that formally identifies a problem, gathers data related to the problem, and strives to formulate a solution. Therefore, data mining helps a business become more profitable , more efficient, or operationally stronger.
• Wide applications : Data mining can look very different across applications, but the overall process can be used with almost any new or legacy application. Essentially any type of data can be gathered and analyzed, and almost every business problem that relies on qualifiable evidence can be tackled using data mining.
• Hidden information and trends : The end goal of data mining is to take raw bits of information and determine if there is cohesion or correlation among the data. This benefit of data mining allows a company to create value with the information they have on hand that would otherwise not be overly apparent. Though data models can be complex, they can also yield fascinating results, unearth hidden trends, and suggest unique strategies.

Cons Explained

• Complexity : The complexity of data mining is one of its greatest disadvantages. Data analytics often requires technical skill sets and certain software tools. Smaller companies may find this to be a barrier of entry too difficult to overcome.
• No guarantees : Data mining doesn't always mean guaranteed results. A company may perform statistical analysis, make conclusions based on strong data, implement changes, and not reap any benefits. This may be due to inaccurate findings, market changes, model errors, or inappropriate data populations . Data mining can only guide decisions and not ensure outcomes.
• High cost : There is also a cost component to data mining. Data tools may require costly subscriptions, and some data may be expensive to obtain. Security and privacy concerns can be pacified, though additional IT infrastructure may be costly as well. Data mining may also be most effective when using huge data sets; however, these data sets must be stored and require heavy computational power to analyze.

Even large companies or government agencies have challenges with data mining. Consider the FDA's white paper on data mining that outlines the challenges of bad information, duplicate data, underreporting, or overreporting.

One of the most lucrative applications of data mining has been undertaken by social media companies. Platforms like Facebook, TikTok, Instagram, and X (formerly Twitter) gather reams of data about their users based on their online activities.

That data can be used to make inferences about their preferences. Advertisers can target their messages to the people who appear to be most likely to respond positively.

Data mining on social media has become a big point of contention, with several investigative reports and exposés showing just how intrusive mining users' data can be. At the heart of the issue is that users may agree to the terms and conditions of the sites not realizing how their personal information is being collected or to whom their information is being sold.

Examples of Data Mining

Data mining can be used for good, or it can be used illicitly. Here is an example of both.

eBay and e-Commerce

eBay collects countless bits of information every day from sellers and buyers. The company uses data mining to attribute relationships between products, assess desired price ranges, analyze prior purchase patterns, and form product categories.

eBay outlines the recommendation process as:

• Raw item metadata and user historical data are aggregated.
• Scripts are run on a trained model to generate and predict the item and user.
• A KNN search is performed.
• The results are written to a database.
• The real-time recommendation takes the user ID, calls the database results, and displays them to the user.

Facebook-Cambridge Analytica Scandal

A cautionary example of data mining is the Facebook-Cambridge Analytica data scandal. During the 2010s, the British consulting firm Cambridge Analytica Ltd. collected personal data from millions of Facebook users. This information was later analyzed for use in the 2016 presidential campaigns of Ted Cruz and Donald Trump. It is suspected that Cambridge Analytica interfered with other notable events such as the Brexit referendum.

In light of this inappropriate data mining and misuse of user data, Facebook agreed to pay $100 million for misleading investors about its uses of consumer data. The Securities and Exchange Commission claimed Facebook discovered the misuse in 2015 but did not correct its disclosures for more than two years.

What Are the Types of Data Mining?

There are two main types of data mining: predictive data mining and descriptive data mining. Predictive data mining extracts data that may be helpful in determining an outcome. Description data mining informs users of a given outcome.

How Is Data Mining Done?

Data mining relies on big data and advanced computing processes including machine learning and other forms of artificial intelligence (AI). The goal is to find patterns that can lead to inferences or predictions from large and unstructured data sets.

What Is Another Term for Data Mining?

Data mining also goes by the less-used term "knowledge discovery in data," or KDD.

Where Is Data Mining Used?

Data mining applications have been designed to take on just about any endeavor that relies on big data. Companies in the financial sector look for patterns in the markets. Governments try to identify potential security threats. Corporations, especially online and social media companies, use data mining to create profitable advertising and marketing campaigns that target specific sets of users.

Modern businesses have the ability to gather information on their customers, products, manufacturing lines, employees, and storefronts. These random pieces of information may not tell a story, but the use of data mining techniques, applications, and tools helps piece together information .

The ultimate goal of the data mining process is to compile data, analyze the results, and execute operational strategies based on data mining results.

Shafique, Umair, and Qaiser, Haseeb. " A Comparative Study of Data Mining Process Models (KDD, CRISP-DM and SEMMA) ." International Journal of Innovation and Scientific Research . vol. 12, no. 1, November 2014, pp. 217-222.

Food and Drug Administration. " Data Mining at FDA – White Paper ."

eBay. " Building a Deep Learning Based Retrieval System for Personalized Recommendations ."

Federal Trade Commission. " FTC Issues Opinion and Order Against Cambridge Analytica for Deceiving Consumers About Collection of Facebook Data, Compliance With EU-U.S. Privacy Shield ."

U.S. Security and Exchange Commission. " Facebook to Pay $100 Million for Misleading Investors About the Risks It Faced From Misuse of User Data ."

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Now That We Are Disaggregating Race and Ethnicity Data, We Need to Start Understanding What They Mean

• 1 Papa Ola Lokahi, Honolulu, Hawaiʻi
• 2 Department of Native Hawaiian Health, John A. Burns School of Medicine, University of Hawaiʻi at Manoa, Honolulu
• Original Investigation COVID-19 Hospitalization by Patterns of Insurance Coverage, Race and Ethnicity, and Vaccination Brock M. Santi, MD; Philip A. Verhoef, MD, PhD JAMA Network Open

The study by Santi and Verhoef 1 examined racial and ethnic disparities for in-hospital COVID-19 mortality in Hawaiʻi using disaggregated data. The study by Santi and Verhoef 1 highlights the importance of disaggregating Asian, Native Hawaiian, and Pacific Islander individuals in clinical research. It also provides an opportunity to discuss some of the nuances and complexities involved with data disaggregation. 1

Data Disaggregation Standards

Representing some of the fastest growing race and ethnicity groups and with a population of more than 25 million in the United States, there is an increasing need for disaggregated Asian, Native Hawaiian, and Pacific Islander data. Since 1997, federal data standards have separated Asian individuals and Native Hawaiian and Pacific Islander individuals into distinct categories. 2 The US Department of Health and Human Services recommended the collection of detailed data for Asian and Native Hawaiian and Pacific Islander populations in 2011, adopting the detailed list used in the 2010 Census. Yet, many studies continue to combine Asian, Native Hawaiian, and Pacific Islander data into a single group, and very few studies use more detailed categories. Aggregating diverse race and ethnicity groups can conceal underlying disparities and is a barrier to health equity. 3 When aggregate statistics are reported, heterogeneity in the experiences of smaller groups become invisible, which can inhibit the ability of these communities to advocate for resources.

The value of disaggregated data is readily apparent in Hawaiʻi, where more than two-thirds of the population identifies as Asian, Native Hawaiian, or Pacific Islander. The distinct cultural, historical, and socioeconomic backgrounds of these populations contribute to varying life expectancies and other health disparities. 4 Specificity (ie, level of detail) and context (eg, social determinants of health) are important factors to consider when disaggregating data to identify and address health inequities. Social epidemiology and causal inference methods are valuable tools when going beyond mere descriptions of health disparities to identify root causes and understand the social determinants of health.

On March 28, 2024, the US Office of Management and Budget published a historic revision to the 1997 Statistical Policy Directive, aimed at improving federal race and ethnicity statistics and ensuring that data more accurately reflect the racial and ethnic diversity of the US population. The standard now includes a requirement to collect more detailed categories and for tabulation procedures to result in the production of as much information as possible. With regards to persons who select multiple race or ethnicity categories, the standard describes the nonmutually exclusive alone or in combination approach for tabulation and reporting. Since we advocate here for the use of more detailed categories and the alone or in combination approach, this commentary may serve as a timely and useful resource for agencies and researchers seeking to implement the new standards.

Collecting Granular Race and Ethnicity Data

The study by Santi and Verhoef 1 goes well beyond the minimum standard by further disaggregating among Asian, Native Hawaiian, and Pacific Islander groups (eg, Chinese, Filipino, Japanese, Native Hawaiian, and Samoan) and by including multiracial persons in each race and ethnicity category. The hospital facilitated this level of detail by providing 20 options for patient race and ethnicity at enrollment and presentation for clinical care. The collection of such granular race and ethnicity data facilitates more nuanced approaches to health disparities and the use of race and ethnicity as proxies for some of the unmeasured drivers of health disparities.

Analyzing Multiracial Data

The classification of persons with more than 1 racial or ethnic identity represents a challenge in statistical analysis. Santi and Verhoef 1 refer to the single–race and ethnicity statistic of 11% for Native Hawaiian or Pacific Islander individuals when describing the demographic characteristics of the state population, when 27% of the state population identifies as Native Hawaiian or Pacific Islander. 4 By comparing the race and ethnicity subgroup counts with the total study population number, we can infer that the race and ethnicity groupings were not mutually exclusive and that some patients were represented more than once in some analyses.

Using nonmutually exclusive race and ethnicity categories is appropriate and preferrable in many instances. For example, the Census Bureau provides data for both alone and alone or in combination for detailed race and ethnicity categories, which can also serve as population denominators for disparity estimates. This inclusive approach to race and ethnicity is also consistent with the federal legislation defining Native Hawaiian individuals as anyone with ancestral origins in the Hawaiian Islands prior to 1778. 4 Attempts to create mutually exclusive groupings that include a multiracial category should be weighed against the resulting loss of information, and in many cases, using an alone or in combination category may be preferrable to the single–race and ethnicity approach. Researchers can avoid potential misinterpretations by clearly explaining how multiracial persons are classified in the analysis.

Hospital-Based vs Population-Based Mortality Rates

While hospital-based mortality rates provide valuable insights into inpatient outcomes, they may not fully capture broader population-level trends in COVID-19 mortality. For instance, Pacific Islander populations consistently experienced the highest age-adjusted mortality rates during the pandemic in Hawaiʻi. However, these disparities were not fully reflected in the study by Santi and Verhoef, 1 in which Pacific Islander populations surprisingly experienced lower mortality rates across several strata of analysis. 5 Conversely, findings by Santi and Verhoef 1 regarding Filipino, Native Hawaiian, and Japanese populations largely aligned with external data, albeit with notable exceptions observed among Native Hawaiian populations during the Delta wave. According to state vital statistic data, Native Hawaiian populations had age-adjusted mortality rates lower than those of the general population in 2020 (14 deaths per 100 000 population), but rates subsequently increased in 2021 during the Delta wave (61 deaths per 100 000 population). The population-based COVID-19 mortality rates among Native Hawaiian populations in 2021 were therefore higher than those among the overall population of Hawaiʻi (35 deaths per 100 000 population) and that of Filipino populations (54 deaths per 100 000 population), trailing only Pacific Islander populations (284 deaths per 100 000 population), which was not reflected in the population from the study by Santi and Verhoef. 1 , 5 The apparent inconsistencies between the findings by Santi and Verhoef 1 and population-based Native Hawaiian and Pacific Islander mortality data are not surprising when considering the unique setting of the study, the restriction to a specific component of mortality risk, and the impacts of statistical adjustment.

Nonrepresentativeness and Health Care Factors

There are many complex factors influencing COVID-19 mortality that extend beyond the hospital setting, making hospital-based studies an incomplete representation of overall mortality trends. This study by Santi and Verhoef, 1 focused on patients seeking care for complications within a single health care facility, inherently captures only a subset of mortality risk over a limited timeframe. Moreover, statistical adjustments can inadvertently produce associations that have less relevance for the actual population if they cannot modify for other factors, like body mass index, age, sex, type of insurance, comorbidities, and neighborhood conditions. These concerns can be addressed by adhering to established epidemiologic guidelines, such as reporting unadjusted outcome statistics for cohort studies. 6 Additionally, specifying the reference group and the hypothesized causal model can further increase the interpretability of adjusted race and ethnicity statistics while also mitigating concerns surrounding nonrepresentativeness.

In-hospital mortality can be an indicator of the quality of medical care provided, especially if patients have similar age and health status at the time of admission. Race and ethnicity disparities for in-hospital mortality that remain after adjustment for other potential risk factors may then reflect individual and process-level quality of care factors that are influenced by a patient’s race and ethnicity. 7 For example, race and ethnicity discordance between patients and clinicians may interact with implicit bias among health care workers to create disparities in hospital-based outcomes. Santi and Verhoef 1 point to the absence of an association between insurance type and mortality as encouraging evidence of equitable care delivery for people with different insurance types but miss the opportunity to discuss how the presence of associations might point to the provision of inequitable hospital care on the basis of race and ethnicity.

Conclusions

Disaggregation is a crucial step in the journey toward collecting and reporting data that better reflect the social and cultural contexts associated with health disparities. Santi and Verhoef 1 contribute to this discourse by using detailed and inclusive race and ethnicity categories in their hospital-based study of COVID-19 mortality. Once data have been disaggregated, interdisciplinary collaboration across relevant fields, including social sciences and epidemiology, can provide helpful tools to address the complexities and nuances of detailed race and ethnicity data. With disaggregated data properly placed into social contexts, we will be better equipped to develop health care policies and resource allocation strategies aimed at addressing health inequities and promoting equitable access to care.

Published: May 1, 2024. doi:10.1001/jamanetworkopen.2024.3674

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Quint JJ et al. JAMA Network Open .

Corresponding Author: Joshua J. Quint, PhD, MPH, Papa Ola Lokahi, 677 Ala Moana Blvd, Ste 720 Honolulu, HI 96813 ( [email protected] ).

Conflict of Interest Disclosures: None reported.

Funding/Support: Dr Keawe‘aimoku Kaholokula’s contribution was supported by grant No. U54GM138062 from the National Institute of General Medical Sciences of the National Institutes of Health.

Role of the Funder/Sponsor: The funder had no role in the analysis or interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Disclaimer: Views expressed in this commentary are those of the authors and not necessarily those of the National Institute of General Medical Sciences or the National Institutes of Health.

See More About

Quint JJ , Keawe‘aimoku Kaholokula J. Now That We Are Disaggregating Race and Ethnicity Data, We Need to Start Understanding What They Mean. JAMA Netw Open. 2024;7(5):e243674. doi:10.1001/jamanetworkopen.2024.3674

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Data Collection Sequence

This topic explores the sequence that you should follow for data collection.

Data collection involves collecting entities in a predefined sequence. The collected entities form the basis for supply planning calculations. To have accurate data, you must ensure to collect the entities in a proper sequence. You cannot collect some entities without collecting their precursor entities. The data collection sequence is very crucial when you collect data from an external source system using CSV files.

If you run targeted collections for all entities, you can ignore the sequence for collections because targeted collections automate the collection sequence for all entities within a single collections request. If you collect many entities in a single request, collections will process them according to the sequences shown in this topic. If you collect only a few entities, then you must be aware of the collections sequence information. For example, you should not collect work orders before you collect items or resources.

To make the workflow simple, the collection sequence is divided into two parts - Part A and Part B. The collection entities in Part B are dependent on the collection entities in Part A. You must collect the entities in Part A before you collect the entities in Part B. Also, the collection entities are grouped together for easier presentation. The data groups in Part A are:

Collections Sequence Part A for Item Data

Collections sequence part a for region, location, and customer data, collections sequence part a for currency, calendar, demand class, and uom data.

The data groups in Part B are:

Collections Sequence Part B for Sales Order and Assignment Sets

Collection Sequence Part B for Work Orders, Work Definition, and Item Structure

Every collection sequence in Part A starts with defining a source system where the collected data will reside. If you are collecting data to the same source system, you define the source system only once. Then, use the same source system to collect all the entities.

The following figure provides an overview of the data collection sequence. The overview shows how Part A and Part B fit together to form a complete data collection flow.

The following image shows the collections sequence to follow while collecting Item data from external source systems. This image represents only half of the entities for collecting Item data.

When you collect the data described here, continue to the collection sequence Part B described in the following subsections.

Collection Sequence Part B for Sales and Order and Assignment Sets

The following image shows the collections sequence to follow while collecting Regions and Customers data from external source systems. This image represents only half of the entities for Item data.

The following image shows the collections sequence to follow while collecting Currency, Calendar, Demand Class, and UOM data from external source systems. Also, ensure that you collect Location before collecting Supplier Site.

Collection Sequence for Calendar Data

The following image shows the collections sequence to follow for collecting the Calendar data. Calendar data is a part of the data collection in Part A. You collect the Calendar data in the following subsection: Collection Sequence Part A for Currency, Calendar, Demand Class, and UOM Data.

The following image shows the collections sequence to follow while collecting Sales Order and Assignment Sets data from external source systems. The data entities in Part B are dependent on Part A. So, you must collect entities listed in Part A before you collect the entities in Part B.

Collection Sequence Part B for Work Orders, Work Definition, and Item Structures

The following image shows the collections sequence to follow while collecting Work Orders, Work Definition, and Item Structure data from external source systems. The data entities in Part B are dependent on Part A. So, you must collect entities listed in Part A before you collect the entities in Part B.

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Net neutrality is back: U.S. promises fast, safe and reliable internet for all

Emma Bowman

The Federal Communications Commission has restored net neutrality rules that ban content providers from restricting bandwidth to customers. Michael Bocchieri/Getty Images hide caption

The Federal Communications Commission has restored net neutrality rules that ban content providers from restricting bandwidth to customers.

Consumers can look forward to faster, safer and more reliable internet connections under the promises of newly reinstated government regulations.

The Federal Communications Commission voted 3-2 on Thursday to reclassify broadband as a public utility, such as water and electricity — to regulate access to the internet. The move to expand government oversight of internet service providers comes after the COVID-19 pandemic exposed the magnitude of the digital divide , forcing consumers to rely on high-speed internet for school and work, as well as social and health support.

The Indicator from Planet Money

What happened to the internet without net neutrality.

Because the government deems internet access an essential service, the FCC is promising oversight as if broadband were a public utility. In doing so, the government aims to make providers more accountable for outages, require more robust network security, protect fast speeds, and require greater protections for consumer data.

The decision effectively restores so-called net neutrality rules that were first introduced during the Obama administration in 2015 and repealed two years later under President Trump.

The rules are sure to invite legal challenges from the telecoms industry — not for the first time. And a future administration could always undo the rules.

Meanwhile, net neutrality regulations are set to go into effect 60 days after their publication in the Federal Register.

But much has yet to be clarified about the rules: The 400-page draft order to restore the regulations has not been publicly released.

Here's what we do know.

What's net neutrality?

Net neutrality is a wonky term for the idea that the flow of information on the internet should be treated equally and that internet service providers can't interfere with what consumers do online.

Also referred to as an "open internet," net neutrality aims to level the digital marketplace, prohibiting internet service providers (ISPs) like Comcast and AT&T from running fast lanes and slow lanes — speeding up or slowing down internet speeds — for online services like Netflix and Spotify.

What's this latest battle about?

Without the net neutrality regulations in place, phone and internet companies have the power to block or favor some content over others. The issue has pit telecom companies against Big Tech. Net neutrality advocates — tech companies, consumer watchdogs and free speech activists among them — warn that without such regulations, broadband providers are incentivized to charge customers more to use internet fast lanes or else risk being stuck with slower speeds.

In recent years, the issue has largely become a partisan one. In 2015, the President Obama-appointed FCC chair ushered in the approval of net neutrality rules . Those rules were repealed two years later under President Trump after his pick to run the FCC called them "heavy-handed" in his pledge to end them.

Now, the return of FCC regulations has reinvigorated the net neutrality debate.

"Every consumer deserves internet access that is fast, open and fair," FCC chair Jessica Rosenworcel said ahead of Thursday's vote. "This is common sense."

As in 2015, the rules classify broadband as a utility service under Title II of the Communications Act of 1934.

The measure passed along party lines, with Democratic commissioners in favor of net neutrality and Republicans opposed.

What critics are saying

Opponents say the net neutrality rules are government overreach and interfere with commerce. In a letter to FCC chair Rosenworcel this week, a group of Republican lawmakers said the draft order to restore net neutrality regulations would chill innovation and investment in the broadband industry.

Dissenting FCC Commissioner Brendan Carr, a Republican, said that fears of a sluggish or pricey internet without the rules were overblown — that consumers benefited from faster speeds and lower prices since the repeal. Net neutrality advocates dispute the argument that broadband rates dropped when net neutrality went away, saying the numbers are misleading .

"There will be lots of talk about 'net neutrality' and virtually none about the core issue before the agency: namely, whether the FCC should claim for itself the freewheeling power to micromanage nearly every aspect of how the Internet functions — from the services that consumers can access to the prices that can be charged," Carr said in October, when the Biden administration proposed restoring net neutrality.

Some telecom companies argue that the FCC is trying to solve a nonexistent problem in its stated aim to preserve equal internet access for consumers.

"This is a nonissue for broadband consumers, who have enjoyed an open internet for decades," said Jonathan Spalter, the CEO of USTelecom, a trade group that represents ISPs such as AT&T and Verizon, in a statement following the vote to hand regulatory authority back to the FCC.

"We plan to pursue all available options, including in the courts," the group said.

What's happened when net neutrality went away?

What ended up happening in the years after the rollback went into effect in 2018 was so discreet that most people unlikely noticed its effects, says Stanford Law professor Barbara van Schewick, who directs the school's Center for Internet and Society and supports net neutrality.

For the past six years, she says, "a lot of public scrutiny on the ISPs and then the attempts to bring back net neutrality in Congress basically kept the ISPs on their best behavior."

Still, there were changes. Some ISPs implemented zero-rating plans, the practice of excluding some apps from data charges, she notes, or were caught throttling — intentionally slowing down consumer internet speeds.

Absent heightened federal regulation, tough net neutrality rules that sprang up in several states, including California , Washington and Oregon, also have continued to keep internet service providers in check.

"It's still being litigated," van Schewick says. "And so, it is fair to say we haven't seen a world without net neutrality."

Judges Clash Over Definition of ‘Liquidation’ in Fourth Circuit

By Martina Barash

Preferred shareholders in a property trust who say a group of common holders sold the company out from under them can benefit from contractual liquidation rights, a Fourth Circuit judge said at oral argument Thursday.

Judge Paul V. Niemeyer recast the preferred holders’ case to revive their suit in simpler terms than they presented. “They alleged there was a liquidation,” he said. “How can the district court reject that without a trial?”

Yet the definition of “liquidation” proved thorny for the judges and attorneys alike, and one of the other judges on the panel countered Niemeyer’s perspective.

That’s a novel ...

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Inside the effort to land an $800M Meta data center, and what it could mean for Montgomery

"Game changer."

That's how Montgomery leaders described the announcement that Meta, parent company of Facebook, will build an $800 million data center here. According to Merriam-Webster, game changer is defined as “A newly introduced element or factor that changes an existing situation or activity in a significant way.” And they expect Meta's project to lead to a significant shift in the way the region will now be presented.

The 710,000-square-foot center will be built on 1,500 acres just off Interstate 65 near the Hyundai Motors Manufacturing Alabama site. It will bring 100 well-paying, high-tech jobs, and at peak the construction effort will employ about 1,000.

But those numbers are just the beginning, not only for Montgomery and the River Region, but for the state as a whole, said Mayor Steven Reed. Where Meta goes, “others tend to follow,” he said.

More: Meta is building an $800 million data center in Montgomery

“These developments mark a promising step toward diversifying our region toward a knowledge-based economy,” Reed said. “And so, when we see some of our best and brightest high school students, college students, go to other cities, we want them to now know that they can come right here.

“They can bring their talents to Montgomery. They don’t have to take them to another city to be recognized and to be rewarded for the talent that they have and the hard work that they are putting in.”

The Montgomery site marks the second data center Meta will have in Alabama. In 2018, the social media company announced a data center in Huntsville.

If you want a glimpse at what Montgomery’s future with Meta may be like, look 190 miles or so up I-65 to The Rocket City. In 2018, Meta announced a data center in Huntsville that would be in two buildings.

In 2022, an expansion grew that initial investment to seven buildings covering 3.5 million square feet for a total investment of $1.5 billion and 300 jobs.

Since 2019, the company has awarded 85 grants to area school and educational organizations totaling about $4.2 million, according to Gov. Kay Ivey’s office. And it shouldn’t surprise you that the company prioritizes science, technology, engineering and math (STEM) education programs.

There are 24 data centers serving Meta across the globe, with 20 being in the United States.

Local officials compare the Meta move to the 1993 announcement that Mercedes would build a manufacturing plant in Vance, near Tuscaloosa. The plant began operations in 1997. Before that, Alabama had no automotive manufacturing sector. After that, Honda, Hyundai and Toyota built manufacturing plants in the state.

Employment in Alabama’s automotive manufacturing sector now totals about 47,000, surging from just a few thousand in the days before Mercedes, according to the Alabama Department of Commerce. About 26,000 of these jobs are in Alabama’s growing automotive supplier network, which now counts 150 companies.

The output of Alabama’s auto industry is a powerful driver of economic growth for the state. Vehicles have become Alabama’s No. 1 export, with shipments to over 70 nations around the world every year, figures from the state Commerce Department show. In 2023, exports of Alabama-made vehicles topped $11 billion, led by shipments to Germany, China and Canada.

“(Meta’s announcement) Allows us to position Montgomery and the River Region in a different way both nationally and internationally,” Reed said. “And I think having Meta be here is just one example of that. I think this marks a shift in Montgomery.

“Because it puts us now in the center of the conversation for knowledge-based economy jobs. It puts us now in the position to talk about what Montgomery has to offer in a way that we have not been able to.”

Landing 'Project Slate'

Known as Project Slate, the effort to land Meta took three years.

The announcement the social media giant had picked Montgomery was made at Montgomery Whitewater Park, a venue not picked by chance. Construction of the $70 million recreational park in West Montgomery was publicly funded, with backers calling it an investment for future economic development efforts .

The park offers a quality of life attraction for companies looking to build, or expand, in the region, said Montgomery County Commission Chairman Doug Singleton. Ellen McNair began the recruitment of Meta when she was with the Montgomery Area Chamber of Commerce, before being appointed by Ivey as Alabama Commerce Secretary Jan. 1.

“In 2022, Ellen McNair called and said ‘Hey, I want you to meet some people out at the Whitewater construction site,” Singleton said, at the announcement news conference. “I said, 'Good, who are they?' She said, ‘I can’t tell you.’ I said, 'Well, where are they from?' She said, ‘I can’t tell you.’ ... I thought she was setting me up for a hit out here.

“But I met this busload of young people, most of them younger than my kids. All young, professional, smart, smart folks. And they looked at the construction site. And I told them ... ‘If I have one wish, it’s that I will see you again one day.'”

During the recruitment process, only first names were used, McNair said. That would derail any Google searches of other efforts to find out who they represented.

“But we didn’t even know their last name, never mind the company that they represented,” McNair said. “But what we did know is that they were incredibly knowledgeable, highly professional and great people who we really, truly enjoyed working with.”

And the future? When other major tech companies see Montgomery on the potential site list?

“Be assured, we have many more irons in the fire,” Reed said.

Contact Montgomery Advertiser reporter Marty Roney at [email protected].