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.


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 .

result of line_plot_bachelors

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.

result of bar_chart_high_school

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.

data analytics assignment example

6.894 : Interactive Data Visualization

Assignment 2: exploratory data analysis.

In this assignment, you will identify a dataset of interest and perform an exploratory analysis to better understand the shape & structure of the data, investigate initial questions, and develop preliminary insights & hypotheses. Your final submission will take the form of a report consisting of captioned visualizations that convey key insights gained during your analysis.

Step 1: Data Selection

First, you will pick a topic area of interest to you and find a dataset that can provide insights into that topic. To streamline the assignment, we've pre-selected a number of datasets for you to choose from.

However, if you would like to investigate a different topic and dataset, you are free to do so. If working with a self-selected dataset, please check with the course staff to ensure it is appropriate for the course. Be advised that data collection and preparation (also known as data wrangling ) can be a very tedious and time-consuming process. Be sure you have sufficient time to conduct exploratory analysis, after preparing the data.

After selecting a topic and dataset – but prior to analysis – you should write down an initial set of at least three questions you'd like to investigate.

Part 2: Exploratory Visual Analysis

Next, you will perform an exploratory analysis of your dataset using a visualization tool such as Tableau. You should consider two different phases of exploration.

In the first phase, you should seek to gain an overview of the shape & stucture of your dataset. What variables does the dataset contain? How are they distributed? Are there any notable data quality issues? Are there any surprising relationships among the variables? Be sure to also perform "sanity checks" for patterns you expect to see!

In the second phase, you should investigate your initial questions, as well as any new questions that arise during your exploration. For each question, start by creating a visualization that might provide a useful answer. Then refine the visualization (by adding additional variables, changing sorting or axis scales, filtering or subsetting data, etc. ) to develop better perspectives, explore unexpected observations, or sanity check your assumptions. You should repeat this process for each of your questions, but feel free to revise your questions or branch off to explore new questions if the data warrants.

  • Final Deliverable

Your final submission should take the form of a Google Docs report – similar to a slide show or comic book – that consists of 10 or more captioned visualizations detailing your most important insights. Your "insights" can include important surprises or issues (such as data quality problems affecting your analysis) as well as responses to your analysis questions. To help you gauge the scope of this assignment, see this example report analyzing data about motion pictures . We've annotated and graded this example to help you calibrate for the breadth and depth of exploration we're looking for.

Each visualization image should be a screenshot exported from a visualization tool, accompanied with a title and descriptive caption (1-4 sentences long) describing the insight(s) learned from that view. Provide sufficient detail for each caption such that anyone could read through your report and understand what you've learned. You are free, but not required, to annotate your images to draw attention to specific features of the data. You may perform highlighting within the visualization tool itself, or draw annotations on the exported image. To easily export images from Tableau, use the Worksheet > Export > Image... menu item.

The end of your report should include a brief summary of main lessons learned.

Recommended Data Sources

To get up and running quickly with this assignment, we recommend exploring one of the following provided datasets:

World Bank Indicators, 1960–2017 . The World Bank has tracked global human developed by indicators such as climate change, economy, education, environment, gender equality, health, and science and technology since 1960. The linked repository contains indicators that have been formatted to facilitate use with Tableau and other data visualization tools. However, you're also welcome to browse and use the original data by indicator or by country . Click on an indicator category or country to download the CSV file.

Chicago Crimes, 2001–present (click Export to download a CSV file). This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system.

Daily Weather in the U.S., 2017 . This dataset contains daily U.S. weather measurements in 2017, provided by the NOAA Daily Global Historical Climatology Network . This data has been transformed: some weather stations with only sparse measurements have been filtered out. See the accompanying weather.txt for descriptions of each column .

Social mobility in the U.S. . Raj Chetty's group at Harvard studies the factors that contribute to (or hinder) upward mobility in the United States (i.e., will our children earn more than we will). Their work has been extensively featured in The New York Times. This page lists data from all of their papers, broken down by geographic level or by topic. We recommend downloading data in the CSV/Excel format, and encourage you to consider joining multiple datasets from the same paper (under the same heading on the page) for a sufficiently rich exploratory process.

The Yelp Open Dataset provides information about businesses, user reviews, and more from Yelp's database. The data is split into separate files ( business , checkin , photos , review , tip , and user ), and is available in either JSON or SQL format. You might use this to investigate the distributions of scores on Yelp, look at how many reviews users typically leave, or look for regional trends about restaurants. Note that this is a large, structured dataset and you don't need to look at all of the data to answer interesting questions. In order to download the data you will need to enter your email and agree to Yelp's Dataset License .

Additional Data Sources

If you want to investigate datasets other than those recommended above, here are some possible sources to consider. You are also free to use data from a source different from those included here. If you have any questions on whether your dataset is appropriate, please ask the course staff ASAP!

  • data.boston.gov - City of Boston Open Data
  • MassData - State of Masachussets Open Data
  • data.gov - U.S. Government Open Datasets
  • U.S. Census Bureau - Census Datasets
  • IPUMS.org - Integrated Census & Survey Data from around the World
  • Federal Elections Commission - Campaign Finance & Expenditures
  • Federal Aviation Administration - FAA Data & Research
  • fivethirtyeight.com - Data and Code behind the Stories and Interactives
  • Buzzfeed News
  • Socrata Open Data
  • 17 places to find datasets for data science projects

Visualization Tools

You are free to use one or more visualization tools in this assignment. However, in the interest of time and for a friendlier learning curve, we strongly encourage you to use Tableau . Tableau provides a graphical interface focused on the task of visual data exploration. You will (with rare exceptions) be able to complete an initial data exploration more quickly and comprehensively than with a programming-based tool.

  • Tableau - Desktop visual analysis software . Available for both Windows and MacOS; register for a free student license.
  • Data Transforms in Vega-Lite . A tutorial on the various built-in data transformation operators available in Vega-Lite.
  • Data Voyager , a research prototype from the UW Interactive Data Lab, combines a Tableau-style interface with visualization recommendations. Use at your own risk!
  • R , using the ggplot2 library or with R's built-in plotting functions.
  • Jupyter Notebooks (Python) , using libraries such as Altair or Matplotlib .

Data Wrangling Tools

The data you choose may require reformatting, transformation or cleaning prior to visualization. Here are tools you can use for data preparation. We recommend first trying to import and process your data in the same tool you intend to use for visualization. If that fails, pick the most appropriate option among the tools below. Contact the course staff if you are unsure what might be the best option for your data!

Graphical Tools

  • Tableau Prep - Tableau provides basic facilities for data import, transformation & blending. Tableau prep is a more sophisticated data preparation tool
  • Trifacta Wrangler - Interactive tool for data transformation & visual profiling.
  • OpenRefine - A free, open source tool for working with messy data.

Programming Tools

  • JavaScript data utilities and/or the Datalib JS library .
  • Pandas - Data table and manipulation utilites for Python.
  • dplyr - A library for data manipulation in R.
  • Or, the programming language and tools of your choice...

The assignment score is out of a maximum of 10 points. Submissions that squarely meet the requirements will receive a score of 8. We will determine scores by judging the breadth and depth of your analysis, whether visualizations meet the expressivenes and effectiveness principles, and how well-written and synthesized your insights are.

We will use the following rubric to grade your assignment. Note, rubric cells may not map exactly to specific point scores.

Submission Details

This is an individual assignment. You may not work in groups.

Your completed exploratory analysis report is due by noon on Wednesday 2/19 . Submit a link to your Google Doc report using this submission form . Please double check your link to ensure it is viewable by others (e.g., try it in an incognito window).

Resubmissions. Resubmissions will be regraded by teaching staff, and you may earn back up to 50% of the points lost in the original submission. To resubmit this assignment, please use this form and follow the same submission process described above. Include a short 1 paragraph description summarizing the changes from the initial submission. Resubmissions without this summary will not be regraded. Resubmissions will be due by 11:59pm on Saturday, 3/14. Slack days may not be applied to extend the resubmission deadline. The teaching staff will only begin to regrade assignments once the Final Project phase begins, so please be patient.

  • Due: 12pm, Wed 2/19
  • Recommended Datasets
  • Example Report
  • Visualization & Data Wrangling Tools
  • Submission form

DATA 275 Introduction to Data Analytics

  • Getting Started with SPSS
  • Variable View
  • Option Suggestions
  • SPSS Viewer
  • Entering Data
  • Cleaning & Checking Your SPSS Database
  • Recoding Data: Collapsing Continuous Data
  • Constructing Scales and Checking Their Reliability
  • Formatting Tables in APA style
  • Creating a syntax
  • Public Data Sources

Data Analytics Project Assignment

  • Literature Review This link opens in a new window

For your research project you will conduct data analysis and right a report summarizing your analysis and the findings from your analysis. You will accomplish this by completing a series of assignments. 

Data 275 Research Project Assignment

In this week’s assignment, you are required accomplish the following tasks:

1. Propose a topic for you project

The topic you select for your capstone depends on your interest and the data problem you want to address. Try to pick a topic that you would enjoy researching and writing about.

Your topic selection will also be influenced by data availability. Because, this is a data analytics project, you will need to have access to data. If you have access to your organization’s data, you are free to use it. If you choose to do so, all information presented must be in secure form because Davenport University does not assume any responsibility for the security of corporate data. Otherwise, you can select a topic that is amenable to publicly available data.

Click the link for some useful suggestions: Project Proposal Suggestions 

2. Find a data set of your interest and download it

There are many publicly available data sets that you can use for your project. The library has compiled a list of many possible sources of data. Click on the link below to explore these sources. 

Public Data Sources 

The data set you select must have:

At least 50 observations (50 rows) and at least 4 variables (columns) excluding identification variables At least one dependent variable

You must provide:

A proper citation of the data source using APA style format A discussion on how the data was collected and by whom The number of variables in the data set The number of observations/subjects in the data set A description of each variable together with an explanation of how it is measured (e.g. the unit of measurement).


A minimum of one page description of your data analytics project which must include the following:

A title for your project A brief description of the project Major stakeholders who would use the information that would be generated from your analysis and how they would use/benefit from that information A description of the dataset you will use for your project

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36 Data Analytics Project Ideas and Datasets (2023 UPDATE))

36 Data Analytics Project Ideas and Datasets (2023 UPDATE)

Data analytics projects help you to build a portfolio and land interviews. It is not enough to just do a novel analytics project however, you will also have to market your project to ensure it gets found.

The first step for any data analytics project is to come up with a compelling problem to investigate. Then, you need to find a dataset to analyze the problem. Some of the strongest categories for data analytics project ideas include:

  • Beginner Analytics Projects  - For early-career data analysts, beginner projects help you practice new skills.
  • Python Analytics Projects - Python allows you to scrape relevant data and perform analysis with pandas dataframes and SciPy libraries.
  • Rental and Housing Data Analytics Projects - Housing data is readily available from public sources, or can be simple enough to create your own dataset. Housing is related to many other societal forces, and because we all need some form of it, the topic will always be of interest to many people.
  • Sports and NBA Analytics Projects - Sports data can be easily scraped, and by using player and game stats you can analyze strategies and performance.
  • Data Visualization Projects - Visualizations allow you to create graphs and charts to tell a story about the data.
  • Music Analytics Projects - Contains datasets for music-related data and identifying music trends.
  • Economics and Current Trends - From exploring GDPs of respective countries to the spread of the COVID-19 virus, these datasets will allow you to explore a wide variety of time-relevant data.
  • Advanced Analytics Projects - For data analysts looking for a stack-filled project.

A data analytics portfolio is a powerful tool for landing an interview. But how can you build one effectively?

Start with a data analytics project and build your portfolio around it. A data analytics project involves taking a dataset and analyzing it in a specific way to showcase results. Not only do they help you build your portfolio, but analytics projects also help you:

  • Learn new tools and techniques.
  • Work with complex datasets.
  • Practice packaging your work and results.
  • Prep for a case study and take-home interviews.
  • Give you inbound interviews from hiring managers that have read your blog post!

Beginner Data Analytics Projects

Projects are one of the best ways for beginners to practice data science skills, including visualization, data cleaning, and working with tools like Python and pandas.

1. Relax Predicting User Adoption Take-Home

Relax Take-Home Assignment

This data analytics take-home assignment, which has been given to data analysts and data scientists at Relax Inc., asks you to dig into user engagement data. Specifically, you’re asked to determine who an “adopted user” is, which is a user who has logged into the product on three separate days in at least one seven-day period.

Once you’ve identified adopted users, you’re asked to surface factors that predict future user adoption.

How you can do it: Jump into the Relax take-home data. This is an intensive data analytics take-home challenge, which the company suggests you spend 12 hours on (although you’re welcome to spend more or less). This is a great project for practicing your data analytics EDA skills, as well as surfacing predictive insights from a dataset.

2. Salary Analysis

Are you in some sort of slump, or do you find the other projects a tad too challenging? Here’s something that’s really easy; this is a salary dataset from Kaggle that is easy to read and clean, and yet still has many dimensions to interpret.

This salary dataset is a good candidate for descriptive analysis , and we can identify which demographics experience reduced or increased salaries. For example, we could explore the salary variations by gender, age, industry, and even years of prior work.

How you can do it: The first step is to grab the dataset from Kaggle. You can either use it as-is and use spreadsheet tools such as Excel to analyze the data, or you can load it into a local SQL server and design a database around the available data. You can then use visualization tools such as Tableau to visualize the data; either through Tableau MySQL Connector, or Tableau’s CSV import feature.

3. Skilledup Messy Product Data Analysis Take-Home

SkilledUp Take-Home Challenge

This data analytics take-home from Skilledup, asks participants to perform analysis on a dataset of product details that is formatted inconveniently. This challenge provides an opportunity to show your data cleaning skills, as well as your ability to perform EDA and surface insights from an unfamiliar dataset. Specifically, the assignment asks you to consider one product group, named Books.

Each product in the group is associated with categories. Of course, there are tradeoffs to categorization, and you’re asked to consider these questions:

  • Is there redundancy in the categorization?
  • How can redundancy be identified and removed?
  • Is it possible to reduce the number of categories dramatically by sacrificing relatively few category entries?

How you can do it: You can access this EDA takehome on Interview Query. Open the dataset and perform some EDA to familiarize yourself with the categories. Then, you can begin to consider the questions that are posed.

4. Marketing Analytics Exploratory Data Analysis

This  marketing analytics dataset  on Kaggle includes customer profiles, campaign successes and failures, channel performance, and product preferences. It’s a great tool for diving into marketing analytics, and there are a number of questions you can answer from the data like:

  • What factors are significantly related to the number of store purchases?
  • Is there a significant relationship between the region the campaign is run in and that campaign’s success?
  • How does the U.S. compare to the rest of the world in terms of total purchases?

How you can do it:  This  Kaggle Notebook from user Jennifer Crockett  is a good place to start, and includes quite a few visualizations and analyses.

If you want to take it a step further, there is quite a bit of statistical analysis you can perform as well.

5. UFO Sightings Data Analysis

The UFO Sightings dataset is a fun one to dive into, and it contains data from more than 80,000 sightings over the last 100 years. This is a robust source for a beginner EDA project, and you can create insights into where sightings are reported most frequently sightings in the U.S. vs the rest of the world, and more.

How you can do it:  Jump into the dataset on Kaggle. There are a number of notebooks you can check out with helpful code snippets. If you’re looking for a challenge, one user created an  interactive map with sighting data .

6. Data Cleaning Practice

This  Kaggle Challenge asks you to clean data as well as perform a variety of data cleaning tasks. This is a perfect beginner data analytics project, which will provide hands-on experience performing techniques like handling missing values, scaling and normalization, and parsing dates.

How you can do it:  You can work through this Kaggle Challenge, which includes data. Another option, however, would be to choose your own dataset that needs to be cleaned, and then work through the challenge and adapt the techniques to your own dataset.

Python Data Analytics Projects

Python is a powerful tool for data analysis projects. Whether you are web scraping data - on sites like the New York Times and Craigslist - or you’re conducting EDA on Uber trips, here are three Python data analytics project ideas to try:

7. Enigma Transforming CSV file Take-Home

Enigma Take-Home Challenge

This take-home challenge - which requires 1-2.5 hours to complete - is a Python script writing task. You’re asked to write a script to transform input CSV data to desired output CSV data. A take-home like this is good practice for the type of Python take-homes that are asked of data analysts, data scientists, and data engineers.

As you work through this practice challenge, focus specifically on the grading criteria, which include:

  • How well you solve the problems.
  • The logic and approach you take to solving them.
  • Your ability to produce, document, and comment on code.
  • Ultimately, the ability to write clear and clean scripts for data preparation.

8. Wedding Crunchers

Todd W. Schneider’s  Wedding Crunchers  is a prime example of a data analysis project using Python. Todd  scraped wedding announcements  from the New York Times, performed analysis on the data, and found intriguing tidbits like:

  • Distribution of common phrases.
  • Average age trends of brides and grooms.
  • Demographic trends.

Using the data and his analysis Schneider created a lot of cool visuals, like this one on Ivy League representation in the wedding announcements:

data analytics assignment example

How you can do it:  Follow the example of Wedding Crunchers. Choose a news or media source, scrape titles and text, and analyze the data for trends. Here’s a tutorial for scraping news APIs with Python.

9. Scraping Craigslist

Craigslist is a classic data source for an analytics project, and there is a wide range of things you can analyze. One of the most common listings is for apartments.

Riley Predum created a handy tutorial  that walks you through the steps of using Python and Beautiful Soup to scrape the data to pull apartment listings, and then was able to do some interesting analysis of pricing when segmented by neighborhood and price distributions. When graphed, his analysis looked like this:

data analytics assignment example

How you can do it: Follow the tutorial to learn how to scrape the data using Python. Some analysis ideas: Look at apartment listings for another area, analyze used car prices for your market, or check out what used items sell on Craigslist.

10. Uber Trip Analysis

Here’s a cool project from Aman Kharwal: An  analysis of Uber trip data from NYC.  The project used this  Kaggle dataset from FiveThirtyEight , containing nearly 20 million Uber pickups. There are a lot of angles to analyze this dataset, like popular pickup times or the busiest days of the week.

Here’s a data visualization on pickup times by hour of the day from Aman:

data analytics assignment example

How you can do it:  This is a data analysis project idea if you’re prepping for a case study interview. You can emulate this one, using the dataset on Kaggle, or you can use these similar taxies and  Uber datasets on data.world,  including one for Austin, TX.

11. Twitter Sentiment Analysis

Twitter (now X) is the perfect data source for an analytics project, and you can perform a wide range of analyses based on Twitter datasets. Sentiment analysis projects are great for practicing beginner NLP techniques.

One option would be to measure sentiment in your dataset over time like this:

data analytics assignment example

How you can do it:  This tutorial from Natassha Selvaraj  provides step-by-step instructions to do sentiment analysis on Twitter data. Or see this tutorial from the Twitter developer forum . For data, you can scrape your own or pull some from these free datasets.

12. Home Pricing Predictions

This project has been featured in our list of  Python data science projects . With this project, you can take the classic  California Census dataset , and use it to predict home prices by region, zip code, or details about the house.

Python can be used to produce some stunning visualizations, like this heat map of price by location.

data analytics assignment example

How you can do it: Because this dataset is so well known, there are a lot of helpful tutorials to learn how to predict price in Python. Then, once you’ve learned the technique, you can start practicing it on a variety of datasets like stock prices, used car prices, or airfare.

Rental and Housing Data Analytics Project Ideas

There’s a ton of accessible housing data online, e.g. sites like Zillow and Airbnb, and these datasets are perfect for analytics and EDA projects.

If you’re interested in price trends in housing, market predictions, or just want to analyze the average home prices for a specific city or state, jump into these projects:

13. Airbnb Data Analytics Take-Home Assignment

Airbnb Data Analytics Take-Home

  • Overview:  Analyze the provided data and make product recommendations to help increase bookings in Rio de Janeiro.
  • Time Required:  6 hours
  • Skills Tested:  Analytics, EDA, growth marketing, data visualization
  • Deliverable:  Summarize your recommendations in response to the questions above in a Jupyter Notebook intended for the Head of Product and VP of Operations (who is not technical).

This take-home is a classic product case study. You have booking data for Rio de Janeiro, and you must define metrics for analyzing matching performance and make recommendations to help increase the number of bookings.

This take-home includes grading criteria, which can help direct your work. Assignments are judged on the following:

  • Analytical approach and clarity of visualizations.
  • Your data sense and decision-making, as well as the reproducibility of the analysis.
  • Strength of your recommendations
  • Your ability to communicate insights in your presentation.
  • Your ability to follow directions.

14. Zillow Housing Prices

Check out  Zillow’s free datasets.  The Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted average of housing market values by region and housing type. There are also datasets on rentals, housing inventories, and price forecasts.

Here’s an  analytics project based in R  that might give you some direction. The author analyzes Zillow data for Seattle, looking at things like the age of inventory (days since listing), % of homes that sell for a loss or gain, and list price vs. sale price for homes in the region:

data analytics assignment example

How you can do it:  There are a ton of different ways you can use the Zillow dataset. Examine listings by region, explore individual list price vs. sale price, or take a look at the average sale price over the average list price by city.

15. Inside Airbnb

On  Inside Airbnb , you’ll find data from Airbnb that has been analyzed, cleaned, and aggregated. There is data for dozens of cities around the world, including number of listings, calendars for listings, and reviews for listings.

Agratama Arfiano has extensively examined Airbnb data for Singapore. There are a lot of different analyses you can do, including finding the number of listings by host or listings by neighborhood. Arfiano has produced some really striking visualizations for this project, including the following:

data analytics assignment example

How you can do it:  Download the data from Inside Airbnb, then choose a city for analysis. You can look at the price, listings by area, listings by the host, the average number of days a listing is rented, and much more.

16. Car Rentals

Have you ever wondered which cars are the most rented? Curious how fares change by make and model? Check out the Cornell Car Rental Dataset on Kaggle. Kushlesh Kumar created the dataset, which features records on 6,000+ rental cars. There are a lot of questions you can answer with this dataset: Fares by make and model, fares by city, inventory by city, and much more. Here’s a cool visualization from Kushlesh:

data analytics assignment example

How you can do it: Using the dataset, you could analyze rental cars by make and model, a particular location, or analyze specific car manufacturers. Another option: Try a similar project with these datasets:  Cash for Clunkers cars ,  Carvana sales data or used cars on eBay .

17. Analyzing NYC Property Sales

This  real estate dataset  shows every property that sold in New York City between September 2016 and September 2017. You can use this data (or a similar dataset you create) for a number of projects, including EDA, price predictions, regression analysis, and data cleaning.

A beginner analytics project you can try with this data would be a missing values analysis project like:

data analytics assignment example

How you can do it: There are a ton of  helpful Kaggle notebooks  you can browse to learn how to: perform price predictions, do data cleaning tasks, or do some interesting EDA with this dataset.

Sports and NBA Data Analytics Projects

Sports data analytics projects are fun if you’re a fan, and also, because there are quite a few free data sources available like Pro-Football-Reference and Basketball-Reference. These sources allow you to pull a wide range of statistics and build your own unique dataset to investigate a problem.

18. NBA Data Analytics Project

Check out this  NBA data analytics project  from Jay at Interview Query. Jay analyzed data from  Basketball Reference  to determine the impact of the 2-for-1 play in the NBA. The idea: In basketball, the 2-for-1 play refers to an end-of-quarter strategy where a team aims to shoot the ball with between 25 and 36 seconds on the clock. That way the team that shoots first has time for an additional play while the opposing team only gets one response. (You can see the  source code on GitHub).

The main metric he was looking for was the differential gain between the score just before the 2-for-1 shot and the score at the end of the quarter. Here’s a look at a differential gain:

NBA Data Analytics Project

How you can do it: Read this tutorial on  scraping Basketball Reference data . You can analyze in-game statistics, career statistics, playoff performance, and much more. An idea could be to analyze a player’s high school ranking  vs. their success in the NBA. Or you could visualize a player’s career.

19. Olympic Medals Analysis

This is a great dataset for a sports analytics project. Featuring 35,000 medals awarded since 1896, there is plenty of data to analyze, and it’s useful for identifying performance trends by country and sport. Here’s a visualization from Didem Erkan :

Olympic Medals Analysis

How you can do it: Check out the  Olympics medals dataset . Angles you might take for analysis include: Medal count by country (as in this visualization ), medal trends by country, e.g., how U.S. performance evolved during the 1900s, or even grouping countries by region to see how fortunes have risen or faded over time.

20. Soccer Power Rankings

FiveThirtyEight is a wonderful source of sports data; they have NBA datasets, as well as data for the NFL and NHL. The site uses its Soccer Power Index (SPI) ratings for predictions and forecasts, but it’s also a good source for analysis and analytics projects. To get started, check out Gideon Karasek’s breakdown of  working with the SPI data .

Soccer Power Rankings

How you can do it:  Check out the  SPI data . Questions you might try to answer include: How has a team’s SPI changed over time, comparisons of SPI amongst various soccer leagues, and goals scored vs. goals predicted?

21. Home Field Advantage Analysis

Does home-field advantage matter in the NFL? Can you quantify how much it matters? First, gather data from  Pro-Football-Reference.com . Then you can perform a simple linear regression model to measure the impact.

Home Field Advantage Analysis

There are a ton of projects you can do with NFL data. One would be to  determine WR rankings, based on season performance .

How you can do it:  See this Github repository on performing a  linear regression to quantify home field advantage .

22. Daily Fantasy Sports

Creating a model to perform in daily fantasy sports requires you to:

  • Predict which players will perform best based on matchups, locations, and other indicators.
  • Build a roster based on a “salary cap” budget.
  • Determine which players will have the top ROI during the given week.

If you’re interested in fantasy football, basketball, or baseball, this would be a strong project.

Daily Fantasy Sports

How you can do it: Check out the  Daily Fantasy Data Science course , if you want a step-by-step look.

Data Visualization Projects

All of the datasets we’ve mentioned would make for amazing data visualization projects. To cap things off we are highlighting three more ideas for you to use as inspiration that potentially draws from your own experiences or interests!

23. Supercell Data Scientist Pre-Test

Supercell Take-Home Challenge

This is a classic SQL/data analytics take-home. You’re asked to explore, analyze, visualize and model Supercell’s revenue data. Specifically, the dataset contains user data and transactions tied to user accounts.

You must answer questions about the data, like which countries produce the most revenue. Then, you’re asked to create a visualization of the data, as well as apply machine learning techniques to it.

24. Visualizing Pollution

This project by Jamie Kettle visualizes plastic pollution by country, and it does a scarily good job of showing just how much plastic waste enters the ocean each year. Take a look for inspiration:

data analytics assignment example

How you can do it: There are dozens of pollution datasets on data.world . Choose one and create a visualization that shows the true impact of pollution on our natural environments.

26. Visualizing Top Movies

There are a ton of movie and media datasets on Kaggle:  The Movie Database 5000 ,  Netflix Movies and TV Shows ,  Box Office Mojo data , etc. And just like their big-screen debuts, movie data makes for fantastic visualizations.

Take a look at this  visualization of the Top 100 movies by Katie Silver , which features top movies based on box office gross and the Oscars each received:

data analytics assignment example

How you can do it: Take a Kaggle movie dataset, and create a visualization that shows one of the following: gross earnings vs. average IMDB rating, Netflix shows by rating, or visualization of top movies by the studio.

27. Gender Pay Gap Analysis

Salary is a subject everyone is interested in, and it makes it a relevant subject for visualization. One idea: Take this dataset from the  U.S. Bureau of Labor Statistics , and create a visualization looking at the gap in pay by industry.

You can see an example of a gender pay gap visualization on InformationIsBeautiful.net:

data analytics assignment example

How you can do it: You can re-create the gender pay visualization, and add your own spin. Or use salary data to visualize, fields with the fastest growing salaries, salary differences by cities, or  data science salaries by the company .

27. Visualize Your Favorite Book

Books are full of data, and you can create some really amazing visualizations using the patterns from them. Take a look at this project by Hanna Piotrowska, turning an  Italo Calvo book into cool visualizations . The project features visualizations of word distributions, themes and motifs by chapter, and a visualization of the distribution of themes throughout the book:

data analytics assignment example

How you can do it: This  Shakespeare dataset , which features all of the lines from his plays, would be ripe for recreating this type of project. Another option: Create a visualization of your favorite Star Wars script.

Music Analytics Projects

If you’re a music fan, music analytics projects are a good way to jumpstart your portfolio. Of course, analyzing music through digital signal processing is out of our scope, so the best way to go around music-related projects is through exploring trends and charts. Here are some resources that you may use.

28. Popular Music Analysis

Here’s one way to analyze music features without explicit feature extraction. This dataset from Kaggle contains a list of popular music from the 1960s. A feature of this dataset is that it is currently being maintained. Here are a few approaches you can use.

How you can do it: You can grab this dataset from Kaggle. This dataset has classifications for popularity, release date, album name, and even genre. You can also use pre-extracted features such as time signature, liveness, valence, acoustic-ness, and even tempo.

Load this dataset into a Pandas DataFrame and do your appropriate processes there. You can analyze how the features move over time (i.e., did songs over time get a bit more mellow, livelier, or louder), or you can even explore the rise and fall of artists over time.

29. KPOP Melon Music Charts Analysis

If you’re interested in creating a KPOP-related analytics project, here’s one for you. While this is not a dataset, what we have here is a data source that scrapes data from the Melon charts and shows you the top 100 songs in the weekly, daily, rising, monthly, and LIVE charts.

How you can do it: The problem with this data source is that it is scraped, so gathering previous data might be a bit problematic. In order to do historical analysis, you will need to compile and store the data yourself.

So for this approach, we will prefer a locally hosted infrastructure. Knowing how to use cloud services to automate and store data might introduce additional layers of complexity for you to show off to a recruiter. Here’s a local approach to conducting this project.

The first step is to decide which database solution to use. We recommend XAMPP’s toolkit with MySQL Server and PHPMyAdmin as it provides an easy-to-use frontend while also providing a query builder that allows you to construct table schemas, so learning DDL (Data Definition Language) is not as much of a necessity.

The second step is to create a Python script that scrapes data from Melon’s music charts. Thankfully, we have a module that scrapes data from the charts. First, install the melonapi module. Then, you can gather the data and store it in your database. Here’s a step-by-step guide to loading the data from the site.

Of course, running this script over a period of time manually opens the door to human forgetfulness or boredom. To avoid this, you can use an automation service to automate your processes. For Windows systems, you can use the built-in Windows Task Scheduler. If you’re using Mac, you can use Automator.

When you have the appropriate data, you can then perform analytics, such as examining how songs move over time, classifying songs by album, and so on.

Economic and Current Trends Analytics Projects

One of the most valuable analytics projects is those that delve into economic and current trends. These projects, which make use of data from financial market trends, public demographic data, and social media behavior, are powerful tools not only for businesses and policymakers but also for individuals who aim to better understand the world around them.

When discussing current trends, COVID-19 is a significant phenomenon that continues to profoundly impact the status quo. An in-depth analysis of COVID-19 datasets can provide valuable insights into public health, global economies, and societal behavior.

How you can do it: These datasets, readily available for download, focus on different geographical areas. Here are a few:

  • EU COVID-19 Dataset - dataset from the European Centre for Disease Prevention and Control, contains COVID-19 data for EU territories.
  • US COVID-19 Dataset - US COVID-19 data provided by the New York Times. However, data might be outdated.
  • Mexico COVID-19 Dataset - A COVID-19 dataset provided by the Mexican government.

These datasets provide opportunities to develop predictive algorithms and to create visualizations depicting the virus’s spread over time. Despite COVID-19 being less deadly today, it has become more contagious , and insights derived from these datasets can be crucial for understanding and combating future pandemics. For instance, a time-series analysis could identify key periods of infection rates’ acceleration and slow-down, highlighting effective and ineffective public health measures.

31. News Media Dataset

The News Media Dataset provides valuable information about the top 43 English media channels on YouTube, including each of their top 50 videos. This dataset, although limited in its scope, can offer intriguing insights into viewer preferences and trends in news consumption.

How you can do it: Grab the dataset from Kaggle and use the dataset which contains the top 50 viewed videos per channel. There are a lot of insights you can gain here, such as using a basic sentiment analysis tool to determine whether the top-performing headlines were positive or negative.

For sentiment analysis, you don’t necessarily need to train a model. You can load the CSV file and loop through all the tags. Use the TextBlob module to conduct sentiment analysis. Here’s how you can go about doing it:

Then, by using the subjectivity and polarity metrics, you can create visualizations that reflect your findings.

32. The Big Mac Index Analytics

The Big Mac Index offers an intriguing approach to comparing purchasing power parity (PPP) between different countries. The index shows how the U.S. dollar compares to other currencies, through a standardized, identical product, the McDonald’s Big Mac. The dataset, provided by Andrii Samoshyn, contains a lot of missing data, offering a real-world exercise in data cleaning. The data goes back to April 2000 up until January 2020.

How you can do it: You can download the dataset from Kaggle here . One common strategy for handling missing data is by using measures of central tendency like mean or median to fill in gaps. More advanced techniques, such as regression imputation, could also be applicable depending on the nature of the missing data.

Using this cleaned dataset, you can compare values over time or between regions. Introducing a “geographical proximity” column could provide additional layers of analysis, allowing comparisons between neighboring countries. Machine Learning techniques like clustering or classification could reveal novel groupings or patterns within the data, providing a richer interpretation of global economic trends.

When conducting these analyses, it’s important to keep in mind methods for evaluating the effectiveness of your work. This might involve statistical tests for significance, accuracy measures for predictive models, or even visual inspection of plotted data to ensure trends and patterns have been accurately captured. Remember, any analytics project is incomplete without a robust method of evaluation.

33. Global Country Information Dataset

This dataset offers a wealth of information about various countries, encompassing factors such as population density, birth rate, land area, agricultural land, Consumer Price Index (CPI), Gross Domestic Product (GDP), and much more. This data provides ample opportunity for comprehensive analysis and correlation studies among different aspects of countries.

How you can do it : Download this dataset from Kaggle. This dataset includes diverse attributes, ranging from economic to geographic factors, creating an array of opportunities for analysis. Here are some project ideas:

  • Correlation Analysis: Investigate the correlations between different attributes, such as GDP and education enrollment, population density and CO2 emissions, birth rate, and life expectancy. You can use libraries like pandas and seaborn in Python for these tasks.
  • Geospatial Analysis: With latitude and longitude data available, you could visualize data on a world map to understand global patterns better. Libraries such as geopandas and folium can be helpful here.
  • Predictive Modeling: Try to predict an attribute based on others. For instance, could you predict a country’s GDP based on factors like population, education enrollment, and CO2 emissions?
  • Cluster Analysis: Group countries based on various features to identify patterns. Are there groups of countries with similar characteristics, and if so, why?

Remember to perform EDA before diving into modeling or advanced analysis, as this will help you understand your data better and could reveal insights or trends to explore further.

34. College Rankings and Tuition Costs Dataset

This dataset offers valuable information regarding various universities, including their rankings and tuition fees. It allows for a comprehensive analysis of the relationship between a university’s prestige, represented by its ranking, and its cost.

How you can do it: First, download the dataset from Kaggle . You can then use Python’s pandas for data handling, and matplotlib or seaborn for visualization.

Possible analyses include exploring the correlation between college rankings and tuition costs, comparing tuition costs of private versus public universities, and studying trends in tuition costs over time. For a more advanced task, try predicting college rankings based on tuition and other variables.

Advanced Data Analytics Project

Ready to take your data skills to the next level? Advanced projects are a way to do just that. They’re all about handling larger datasets, really digging into data cleaning and preprocessing, and getting your hands dirty with a range of tech stacks. It’s a two-in-one deal – you’ll dip your toes inside the roles of both a data engineer and a data scientist. Here are some project ideas to consider.

35. Analyzing Google Trends Data

Google Trends, a free service provided by Google, can serve as a treasure trove for data analysts, offering insights into popular trends worldwide. But there’s a hitch. Google Trends does not support any official API, making direct data acquisition a bit challenging. However, there’s a workaround — web scraping. This guide will walk you through the process of using a Python module for scraping Google Trends data.

How you can do it: Of course, we would not want to implement a web scraper ourselves. Simply put, it’s too much work. For this project, we will utilize a Python module that will help us scrape the data. Let’s view an example:

This code should print out the data in the following format:

You should use an automation service to automate scraping at least once per hour (see: KPOP Melon Music Charts Analysis) . Then, you should store the results in a CSV file that you can query later. There are many points of analysis, such as keyword rankings, website rankings for articles, and more.

Taking it a step further:

If you want to make an even more robust project that’s bound to wow your recruiters, here are some ideas to make the scraping process easier to maintain, albeit with a higher difficulty in setting up.

The first problem in our previous approach is the hardware issue. Simply put, the automation service we used earlier is moot if our device is off or if it was not instantiated during device startup. To solve this, we can utilize the cloud.

Using a function service (i.e., GCP Cloud Functions, AWS Lambda), you can execute Python scripts. Now, you will need to orchestrate this service, and you can use a Pub/Sub service such as GCP Pub/Sub and AWS SNS. These will alert your cloud functions to run, and you can modify the Pub/Sub service to run at a specified time gap.

Then, when your script successfully scrapes the data, you will need a SQL server instance. The flavor of SQL does not really matter, but you can use the available databases provided by your cloud provider. For example, AWS offers RDS, while GCP offers Cloud SQL.

Once your data is pulled together, you can then start analyzing your data and employing analysis techniques to visualize and interpret data.

36. New York Times (NYT) Movie Reviews Sentiment Analysis

Sentiment Analysis is a critical tool in gauging public opinion and emotional responses towards various subjects, and in this case, movies. With a substantial number of movie reviews published daily in well-circulated publications like the NYT, proper sentiment analysis can provide valuable insights into the perceived quality of films and their reception among critics.

How you can do it: As a data source, NYT has an API service that allows you to query their databases. Create an account at this link and enable the ‘Movie Reviews’ service. Then, using your API key, you can start querying using the following script:

The query looks up the titles and returns movie reviews matching those in the query. You can then use the review summaries to do sentiment analysis.

Other NY Times APIs you can explore include the Most Popular API , and the Top Stories API .

More Analytics Project Resources

If you are still looking for inspiration, see our compiled list of free datasets which features sites to search for free data, datasets for EDA projects and visualizations, as well as datasets for machine learning projects.

You should also read our guide on the data analyst career path , how to become a data analyst without a degree , how to build a data science project from scratch and list of 30 data science project ideas .

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4 Examples of Business Analytics in Action

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Data is a valuable resource in today’s ever-changing marketplace. For business professionals, knowing how to interpret and communicate data is an indispensable skill that can inform sound decision-making.

“The ability to bring data-driven insights into decision-making is extremely powerful—all the more so given all the companies that can’t hire enough people who have these capabilities,” says Harvard Business School Professor Jan Hammond , who teaches the online course Business Analytics . “It’s the way the world is going.”

Before taking a look at how some companies are harnessing the power of data, it’s important to have a baseline understanding of what the term “business analytics” means.

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What Is Business Analytics?

Business analytics is the use of math and statistics to collect, analyze, and interpret data to make better business decisions.

There are four key types of business analytics: descriptive, predictive, diagnostic, and prescriptive. Descriptive analytics is the interpretation of historical data to identify trends and patterns, while predictive analytics centers on taking that information and using it to forecast future outcomes. Diagnostic analytics can be used to identify the root cause of a problem. In the case of prescriptive analytics , testing and other techniques are employed to determine which outcome will yield the best result in a given scenario.

Related : 4 Types of Data Analytics to Improve Decision-Making

Across industries, these data-driven approaches have been employed by professionals to make informed business decisions and attain organizational success.

Check out the video below to learn more about business analytics, and subscribe to our YouTube channel for more explainer content!

Business Analytics vs. Data Science

It’s important to highlight the difference between business analytics and data science . While both processes use big data to solve business problems they’re separate fields.

The main goal of business analytics is to extract meaningful insights from data to guide organizational decisions, while data science is focused on turning raw data into meaningful conclusions through using algorithms and statistical models. Business analysts participate in tasks such as budgeting, forecasting, and product development, while data scientists focus on data wrangling , programming, and statistical modeling.

While they consist of different functions and processes, business analytics and data science are both vital to today’s organizations. Here are four examples of how organizations are using business analytics to their benefit.

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Business Analytics Examples

According to a recent survey by McKinsey , an increasing share of organizations report using analytics to generate growth. Here’s a look at how four companies are aligning with that trend and applying data insights to their decision-making processes.

1. Improving Productivity and Collaboration at Microsoft

At technology giant Microsoft , collaboration is key to a productive, innovative work environment. Following a 2015 move of its engineering group's offices, the company sought to understand how fostering face-to-face interactions among staff could boost employee performance and save money.

Microsoft’s Workplace Analytics team hypothesized that moving the 1,200-person group from five buildings to four could improve collaboration by increasing the number of employees per building and reducing the distance that staff needed to travel for meetings. This assumption was partially based on an earlier study by Microsoft , which found that people are more likely to collaborate when they’re more closely located to one another.

In an article for the Harvard Business Review , the company’s analytics team shared the outcomes they observed as a result of the relocation. Through looking at metadata attached to employee calendars, the team found that the move resulted in a 46 percent decrease in meeting travel time. This translated into a combined 100 hours saved per week across all relocated staff members and an estimated savings of $520,000 per year in employee time.

The results also showed that teams were meeting more often due to being in closer proximity, with the average number of weekly meetings per person increasing from 14 to 18. In addition, the average duration of meetings slightly declined, from 0.85 hours to 0.77 hours. These findings signaled that the relocation both improved collaboration among employees and increased operational efficiency.

For Microsoft, the insights gleaned from this analysis underscored the importance of in-person interactions and helped the company understand how thoughtful planning of employee workspaces could lead to significant time and cost savings.

2. Enhancing Customer Support at Uber

Ensuring a quality user experience is a top priority for ride-hailing company Uber. To streamline its customer service capabilities, the company developed a Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve their speed and accuracy when responding to support tickets.

COTA’s implementation delivered positive results. The tool reduced ticket resolution time by 10 percent, and its success prompted the Uber Engineering team to explore how it could be improved.

For the second iteration of the product, COTA v2, the team focused on integrating a deep learning architecture that could scale as the company grew. Before rolling out the update, Uber turned to A/B testing —a method of comparing the outcomes of two different choices (in this case, COTA v1 and COTA v2)—to validate the upgraded tool’s performance.

Preceding the A/B test was an A/A test, during which both a control group and a treatment group used the first version of COTA for one week. The treatment group was then given access to COTA v2 to kick off the A/B testing phase, which lasted for one month.

At the conclusion of testing, it was found that there was a nearly seven percent relative reduction in average handle time per ticket for the treatment group during the A/B phase, indicating that the use of COTA v2 led to faster service and more accurate resolution recommendations. The results also showed that customer satisfaction scores slightly improved as a result of using COTA v2.

With the use of A/B testing, Uber determined that implementing COTA v2 would not only improve customer service, but save millions of dollars by streamlining its ticket resolution process.

Related : How to Analyze a Dataset: 6 Steps

3. Forecasting Orders and Recipes at Blue Apron

For meal kit delivery service Blue Apron, understanding customer behavior and preferences is vitally important to its success. Each week, the company presents subscribers with a fixed menu of meals available for purchase and employs predictive analytics to forecast demand , with the aim of using data to avoid product spoilage and fulfill orders.

To arrive at these predictions, Blue Apron uses algorithms that take several variables into account, which typically fall into three categories: customer-related features, recipe-related features, and seasonality features. Customer-related features describe historical data that depicts a given user’s order frequency, while recipe-related features focus on a subscriber’s past recipe preferences, allowing the company to infer which upcoming meals they’re likely to order. In the case of seasonality features, purchasing patterns are examined to determine when order rates may be higher or lower, depending on the time of year.

Through regression analysis—a statistical method used to examine the relationship between variables—Blue Apron’s engineering team has successfully measured the precision of its forecasting models. The team reports that, overall, the root-mean-square error—the difference between predicted and observed values—of their projection of future orders is consistently less than six percent, indicating a high level of forecasting accuracy.

By employing predictive analytics to better understand customers, Blue Apron has improved its user experience, identified how subscriber tastes change over time, and recognized how shifting preferences are impacted by recipe offerings.

Related : 5 Business Analytics Skills for Professionals

4. Targeting Consumers at PepsiCo

Consumers are crucial to the success of multinational food and beverage company PepsiCo. The company supplies retailers in more than 200 countries worldwide , serving a billion customers every day. To ensure the right quantities and types of products are available to consumers in certain locations, PepsiCo uses big data and predictive analytics.

PepsiCo created a cloud-based data and analytics platform called Pep Worx to make more informed decisions regarding product merchandising. With Pep Worx, the company identifies shoppers in the United States who are likely to be highly interested in a specific PepsiCo brand or product.

For example, Pep Worx enabled PepsiCo to distinguish 24 million households from its dataset of 110 million US households that would be most likely to be interested in Quaker Overnight Oats. The company then identified specific retailers that these households might shop at and targeted their unique audiences. Ultimately, these customers drove 80 percent of the product’s sales growth in its first 12 months after launch.

PepsiCo’s analysis of consumer data is a prime example of how data-driven decision-making can help today’s organizations maximize profits.

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Developing a Data Mindset

As these companies illustrate, analytics can be a powerful tool for organizations seeking to grow and improve their services and operations. At the individual level, a deep understanding of data can not only lead to better decision-making, but career advancement and recognition in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” Hammond says . “If you’re able to go into a meeting, and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”

Do you want to leverage the power of data within your organization? Explore Business Analytics —one of our online business essentials courses —to learn how to use data analysis to solve business problems.

This post was updated on March 24, 2023. It was originally published on January 15, 2019.

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How to Write a Business Analysis Report [Examples and Templates]

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Table of contents

Business analysis reports are a lot like preparing a delicious meal.

Sometimes, the recipe is simple enough that you only need to use the basic ingredients. Other times, you will have to follow specific instructions to ensure those tasty delicacies turn out just right.

Want to make sure your business report never turns out like a chewy piece of meat? You’ve come to the right place.

Stay tuned until the end of this blog post, and we promise you won’t be hungry… for business knowledge!

What Is a Business Analysis Report?

Why is analytical reporting important, what should be included in a business analysis report, how do you write a business analysis report, business data analysis report examples and templates.

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A business analysis report provides information about the current situation of your company. This report is usually created by the management to help in the decision-making process and is usually used by other departments within a company.

Business analysis reports can either focus your research on the effectiveness of an existing business process or a proposed new process. Besides, an effective business analysis report should also assess the results to determine if the process changes had a positive or negative effect on the company’s goals. In fact, according to Databox’s State of business reporting , an overwhelming majority of companies said that reporting improved their performance.

Analytical reports are the bridge that connects your company to an effective, data-driven business intelligence strategy . By leveraging analytical reports , you can make informed decisions about your organization’s most critical issues. You will no longer need to rely on gut instinct or anecdotal evidence when assessing risks, threats, and opportunities. Instead, you will have access to a wealth of reliable data to inform your decisions.

Here are some essential benefits of analytical reporting:

  • Improve communication and foster collaboration – The most obvious benefit of business analysis report writing is an improvement in communication between all stakeholders involved in the project. Also, analytical business reports can help you to generate more trust and foster better collaboration among your employees and colleagues. By using data analytics reporting tools , you will be able to monitor your employees’ performance on a day-to-day basis. This will allow you to hold them accountable for their actions and give them greater freedom within the business as they know that their superiors have faith in their decision-making capabilities.
  • Increase productivity – Without this level of shared insight, businesses struggle to stay on top of their most important tasks and can become less efficient. An effective analytical business report provides the information needed for more efficient internal processes and helps you find more time for strategic activities such as improving your business strategy or working on long-term goals .
  • Innovation – In today’s digital age, the pressure to innovate was never greater. When consumers basically have everything they want at their fingertips, stepping up to the plate with a new and improved product or service has never been more important. With an accessible dashboard in place, you will be able to create data-driven narratives for each of your business’ critical functions. For example, if you are a software company, you can use the insights gained from report analysis done with your dashboard software to tailor your product development efforts to the actual needs of your customers. By doing so, you will be able to develop products that are better tailored to specific customer groups. You can also use the same information for developing new marketing strategies and campaigns.
  • Continuous business evolution – When it comes to digital businesses, data is everything. No model lasts forever, so having access to a business dashboard software that allows you to constantly keep tabs on your business’ performance will help you refine it as time goes on. If there are any glitches in your business model, or if something isn’t panning out as expected, the insight offered by a business analysis report can help you improve upon what works while scrapping what doesn’t.

A business analysis report has several components that need to be included to give a thorough description of the topic at hand. The structure and length of business analysis reports can vary depending on the needs of the project or task.

They can be broken down into different sections that include an:

  • Executive summary
  • Study introduction
  • Methodology
  • Review of statistics

Reports of this nature may also include case studies or examples in their discussion section.

A report can be written in a formal or informal tone, depending on the audience and purpose of the document. While a formal tone is best for executives , an informal tone is more appropriate for technical audiences . It is also a good idea to use something like an executive summary template to report on the results repeatedly with ease.

A good business analysis report is detailed and provides recommendations in the form of actionable steps. Here we have listed some simple steps that you need to follow to write a good business analysis report. Report writing is a major part of the business analysis process. In this section, you will learn how to write a report for your company:



Obtain an overview of what you want to analyze in the business report . For example, if you are writing a business analysis report on how to improve customer service at an insurance company, you will want to look through all the customer service processes to determine where the problems lie. The more prepared you are when starting a project, the easier it will be to get results. Here is what your preparation should look like:

Set your goals

The first step in writing this document is to set your goals . What do you hope to accomplish with this paper? Do you need to assess the company’s finances? Are you looking for ways to make improvements? Or do you have outside investors who want to know if they should buy into the company? Once you know what your goal is, then you can begin setting up your project.

PRO TIP: How Well Are Your Marketing KPIs Performing?

Like most marketers and marketing managers, you want to know how well your efforts are translating into results each month. How much traffic and new contact conversions do you get? How many new contacts do you get from organic sessions? How are your email campaigns performing? How well are your landing pages converting? You might have to scramble to put all of this together in a single report, but now you can have it all at your fingertips in a single Databox dashboard.

Our Marketing Overview Dashboard includes data from Google Analytics 4 and HubSpot Marketing with key performance metrics like:

  • Sessions . The number of sessions can tell you how many times people are returning to your website. Obviously, the higher the better.
  • New Contacts from Sessions . How well is your campaign driving new contacts and customers?
  • Marketing Performance KPIs . Tracking the number of MQLs, SQLs, New Contacts and similar will help you identify how your marketing efforts contribute to sales.
  • Email Performance . Measure the success of your email campaigns from HubSpot. Keep an eye on your most important email marketing metrics such as number of sent emails, number of opened emails, open rate, email click-through rate, and more.
  • Blog Posts and Landing Pages . How many people have viewed your blog recently? How well are your landing pages performing?

Now you can benefit from the experience of our Google Analytics and HubSpot Marketing experts, who have put together a plug-and-play Databox template that contains all the essential metrics for monitoring your leads. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!


You can easily set it up in just a few clicks – no coding required.

To set up the dashboard, follow these 3 simple steps:

Step 1: Get the template 

Step 2: Connect your HubSpot and Google Analytics 4 accounts with Databox. 

Step 3: Watch your dashboard populate in seconds.

Assess the Company’s Mission

It’s almost impossible to write a business analysis report without access to the company’s mission statement. Even if you don’t plan on using the mission statement as part of your business analysis summary, it can help you understand the company’s culture and goals. Mission statements are typically short and easy to read, but they may not include every area of focus that you want to include in your report.

Thus, it is important to use other sources when possible. For example, if you are writing a business analysis report for a small start-up company that is just beginning to market its product or service, review the company website or talk directly with management to learn what they believe will be most crucial in growing the company from the ground up.

Stakeholder Analysis

Who is your audience? Create the reader’s persona and tailor all information to their perspective. Create a stakeholder map that identifies all the groups, departments, functions, and individuals involved in this project (and any other projects related to this one). Your stakeholder map should include a description of each group’s role.

Review Financial Performance

Review the financing of the business and determine whether there are any potential threats to the company’s ability to meet its future financial obligations. This includes reviewing debt payments and ownership equity compared with other types of financing such as accounts receivable, cash reserves, and working capital. Determine whether there have been any changes in the funding over time, such as an increase in long-term debt or a decrease in owners’ equity.

Apart from reviewing your debt payments and ownership equity with other types of financing, wouldn’t it be great if you could compare your financial performance to companies that are exactly like yours? With Databox, this can be done in less than 3 minutes.

For example, by  joining this benchmark group , you can better understand your gross profit margin performance and see how metrics like income, gross profit, net income, net operating increase, etc compare against businesses like yours.

One piece of data that you would be able to discover is the average gross profit a month for B2B, B2C, SaaS and eCommerce. Knowing that you perform better than the median may help you evaluate your current business strategy and identify the neccessary steps towards improving it.

Instantly and Anonymously Benchmark Your Company’s Performance Against Others Just Like You

If you ever asked yourself:

  • How does our marketing stack up against our competitors?
  • Are our salespeople as productive as reps from similar companies?
  • Are our profit margins as high as our peers?

Databox Benchmark Groups can finally help you answer these questions and discover how your company measures up against similar companies based on your KPIs.

When you join Benchmark Groups, you will:

  • Get instant, up-to-date data on how your company stacks up against similar companies based on the metrics most important to you. Explore benchmarks for dozens of metrics, built on anonymized data from thousands of companies and get a full 360° view of your company’s KPIs across sales, marketing, finance, and more.
  • Understand where your business excels and where you may be falling behind so you can shift to what will make the biggest impact. Leverage industry insights to set more effective, competitive business strategies. Explore where exactly you have room for growth within your business based on objective market data.
  • Keep your clients happy by using data to back up your expertise. Show your clients where you’re helping them overperform against similar companies. Use the data to show prospects where they really are… and the potential of where they could be.
  • Get a valuable asset for improving yearly and quarterly planning . Get valuable insights into areas that need more work. Gain more context for strategic planning.

The best part?

  • Benchmark Groups are free to access.
  • The data is 100% anonymized. No other company will be able to see your performance, and you won’t be able to see the performance of individual companies either.

When it comes to showing you how your performance compares to others, here is what it might look like for the metric Average Session Duration:

data analytics assignment example

And here is an example of an open group you could join:

data analytics assignment example

And this is just a fraction of what you’ll get. With Databox Benchmarks, you will need only one spot to see how all of your teams stack up — marketing, sales, customer service, product development, finance, and more. 

  • Choose criteria so that the Benchmark is calculated using only companies like yours
  • Narrow the benchmark sample using criteria that describe your company
  • Display benchmarks right on your Databox dashboards

Sounds like something you want to try out? Join a Databox Benchmark Group today!

Examine the “Four P’s”

“Four P’s” — product , price , place, and promotion . Here’s how they work:

  • Product — What is the product? How does it compare with those of competitors? Is it in a position to gain market share?
  • Price — What is the price of the product? Is it what customers perceive as a good value?
  • Place — Where will the product be sold? Will existing distribution channels suffice or should new channels be considered?
  • Promotion — Are there marketing communications efforts already in place or needed to support the product launch or existing products?

Evaluate the Company Structure

A business analysis report examines the structure of a company, including its management, staff, departments, divisions, and supply chain. It also evaluates how well-managed the company is and how efficient its supply chain is. In order to develop a strong strategy, you need to be able to analyze your business structure.

When writing a business analysis report, it’s important to make sure you structure your work properly. You want to impress your readers with a clear and logical layout, so they will be able to see the strengths of your recommendations for improving certain areas of the business. A badly written report can completely ruin an impression, so follow these steps to ensure you get it right the first time.

A typical business analysis report is formatted as a cover page , an executive summary , information sections, and a summary .

  • A cover page contains the title and author of the report, the date, a contact person, and reference numbers.
  • The information section is backed up by data from the work you’ve done to support your findings, including charts and tables. Also, includes all the information that will help you make decisions about your project. Experience has shown that the use of reputable study materials, such as  StuDocu  and others, might serve you as a great assistant in your findings and project tasks.
  • A summary is a short overview of the main points that you’ve made in the report. It should be written so someone who hasn’t read your entire document can understand exactly what you’re saying. Use it to highlight your main recommendations for how to change your project or organization in order to achieve its goals.
  • The last section of a business analysis report is a short list of references that include any websites or documents that you used in your research. Be sure to note if you created or modified any of these documents — it’s important to give credit where credit is due.

The Process of Investigation

Explain the problem – Clearly identify the issue and determine who is affected by it. You should include a detailed description of the problem you are analyzing, as well as an in-depth analysis of its components and effects. If you’re analyzing a small issue on a local scale, make sure that your report reflects this scale. That way, if someone else reads your work who had no idea about its context or scope, they would still be able to understand it.

Explain research methods – There are two ways to do this. Firstly, you can list the methods you’ve used in the report to determine your actions’ success and failure. Secondly, you should add one or two new methods to try instead. Always tell readers how you came up with your answer or what data you used for your report. If you simply tell them that the company needs to improve customer service training then they won’t know what kind of data led you to that conclusion. Also, if there were several ways of addressing a problem, discuss each one and why it might not work or why it may not be appropriate for the company at this time.

Analyze data – Analyzing data is an integral part of any business decision, whether it’s related to the costs of manufacturing a product or predicting consumer behavior. Business analysis reports typically focus on one aspect of an organization and break down that aspect into several parts — all of which must be analyzed in order to come to a conclusion about the original topic.

The Outcome of Each Investigation Stage

The recommendations and actions will usually follow from the business objectives not being met. For example, if one of your goals was to decrease costs then your recommendations would include optimization strategies for cost reduction . If you have more than one suggestion you should make a list of the pros and cons of each one. You can make several recommendations in one report if they are related. In addition, make sure that every recommendation has supporting arguments to back them up.

Report Summary

Every business analysis report should start with a summary. It’s the first thing people see and it needs to capture their attention and interest. The report summary can be created in two ways, depending on the nature of the report:

  • If the report is a brief one, that simply gives a summary of the findings, then it can be created as part of the executive summary.
  • But if it’s a long report, it could be too wordy to summarise. In this case, you can create a more detailed overview that covers all the main aspects of the project from both an internal and external point of view.

Everything comes down to this section. A presentation is designed to inform, persuade and influence decision-makers to take the next action steps.

Sometimes a slide or two can make them change their mind or open new horizons. These days, digital dashboards are becoming increasingly popular when it comes to presenting data in business reports. Dashboards combine different visualizations into one place, allowing users to get an overview of the information they need at a glance rather than searching through a bunch of documents or spreadsheets trying.

Databox offers dynamic and accessible digital dashboards that will help you to convert raw data into a meaningful story. And the best part is that you can do it with a ‘blink of an eye’ even if you don’t have any coding or designs skills. There is also an option of individual report customization so that you can tailor any dashboard to your own needs.

Pre-made dashboard templates can be extremely useful when creating your own business analysis report. While examples serve as inspiration, templates allow you to create reports quickly and easily without having to spend time (and money) developing the underlying data models.

Databox dashboard templates come with some of the most common pre-built metrics and KPIs different types of businesses track across different departments. In order to create powerful business insights within minutes, all you need to do is download any of our free templates and connect your data source — the metrics will populate automatically.

Business Report Examples and Templates

Databox business dashboard examples are simple and powerful tools for tracking your business KPIs and performance. These dashboards can be used by executive teams and managers as well as by senior management, marketing, sales, customer support, IT, accounting, and other departments. If you are new to this kind of reporting, you may not know how to set up a dashboard or what metrics should be displayed on it. This is where a premade template for business dashboards comes in handy.

For example, this Google Ads Report Template is designed to give you a simple way to keep track of your campaigns’ performance over time, and it’s a great resource for anyone who uses Google’s advertising platform, regardless of whether they’re an SMB, an SME or an enterprise.

Google ads dashboard

KPI Report Examples and Templates

KPIs are the foundation of any business analysis, and they can come in a multitude of forms. While we’ve defined KPIs as metrics or measurements that allow you to assess the effectiveness of a given process, department, or team, there are a number of ways to evaluate your KPIs. Through the use of color-coding, user-friendly graphs and charts, and an intuitive layout, your KPIs should be easy for anyone to understand. A good way to do this is by having a dedicated business analyst on your team who can take on the task of gathering data, analyzing it, and presenting it in a way that will drive actionable insights. However, if you don’t have a dedicated analyst or don’t want to spend money on one, you can still create KPI reporting dashboards using free KPI Databox templates and examples .

For example, this Sales Overview template is a great resource for managers who want to get an overview of their sales team’s performance and KPIs. It’s perfect for getting started with business analysis, as it is relatively easy to understand and put together.

sales overview dashboard

Performance Report Examples and Templates

All businesses, regardless of size or industry, need to know how well they are performing in order to make the best decisions for their company and improve overall ROI. A performance dashboard is a strategic tool used to track key metrics across different departments and provide insight into the health of a business. Databox has a collection of 50+ Performance Dashboard Examples and Templates which are available for free download.

For example, if your business is investing a lot into customer support, we recommend tracking your customer service performance with this Helpscout Mailbox Dashboard which will give you insights into conversations, your team’s productivity, customer happiness score, and more.

Helpscout dashboard example

Executive Report Examples and Templates

An executive dashboard is a visual representation of the current state of a business. The main purpose of an executive dashboard is to enable business leaders to quickly identify opportunities, identify areas for improvement, pinpoint issues, and make data-informed decisions for driving sales growth, new product launches, and overall business growth. When an executive dashboard is fully developed, as one of these 50+ Databox Free Executive Examples and Templates , it offers a single view of the most important metrics for a business at a glance.

For example, you probably have more than one set of financial data tracked using an executive dashboard software : invoices, revenue reports (for accounting), income statements, to mention a few. If you want to view all this data in one convenient place, or even create a custom report that gives you a better picture of your business’s financial health, this Stripe Dashboard Template is a perfect solution for you.

Stripe dashboard

Metrics Report Examples and Templates

Choosing the right metrics for your business dashboard can be crucial to helping you meet your business objectives, evaluate your performance, and get insights into how your business is operating. Metrics dashboards are used by senior management to measure the performance of their company on a day-to-day basis. They are also used by mid-level managers to determine how their teams are performing against individual goals and objectives. Databox provides 50+ Free Metrics Dashboard Examples and Templates that you can use to create your company’s own dashboards. Each is unique and will depend on your business needs.

For example, if you are looking for ways to track the performance of your DevOps team, and get the latest updates on projects quickly – from commits, and repository status, to top contributors to your software development projects, this GitHub Overview Dashboard is for you.

GitHub overview dashboard

Small Business Report Examples and Templates

A lot of small business owners don’t realize how important it is to have a proper dashboard in place until they actually use one. A dashboard can help you track and compare different metrics, benchmark your performance against industry averages, evaluate the effectiveness of your marketing and sales strategies, track financials, and much more. So if you’re looking for a tool to help you measure and manage your small business’ performance, try some of these 50+ Free Small Business Dashboard Examples and Templates .

For example, this Quickbooks Dashboard template can help you get a clear understanding of your business’s financial performance, ultimately allowing you to make better-informed decisions that will drive growth and profitability.

Quickbooks dashboard

Agency Report Examples and Templates

Agency dashboards are not a new concept. They have been around for years and are used by companies all over the world. Agency dashboards can be powerful tools for improving your marketing performance, increasing client loyalty, and landing new clients. There is no single correct way to create an agency dashboard. Everyone has their own goals and objectives, which will ultimately determine which data points you choose to include or track using a client dashboard software , but with these Databox 100+ Free Agency Dashboard Examples and Templates you have plenty of options to start with.

For example, you can use this Harvest Clients Time Report to easily see how much time your employees spend working on projects for a particular client, including billable hours and billable amount split by projects.

Harvest Clients Time Report dashboard

Better Business Reporting with Databox

Business analysis is all about finding smart ways to evaluate your organization’s performance and future potential. And that’s where Databox comes in.

Databox can be a helpful tool for business leaders who are required to analyze data, hold frequent meetings, and generate change in their organizations. From improving the quality and accessibility of your reporting to tracking critical performance metrics in one place, and sharing performance metrics with your peers and team members in a cohesive, presentable way, allow Databox to be your personal assistant in these processes, minimize the burdens of reporting and ensure you always stay on top of your metrics game.

Sign up today for free to start streamlining your business reporting process.

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Top 10 Data Analysis Templates with Samples and Examples

Top 10 Data Analysis Templates with Samples and Examples

Mohammed Sameer


If people could eat data instead of food, we could end world hunger with enough spare data left over to tackle 3 famines.

This startling but obvious statement underscores the abundance of data available to the human race today and the humungous rate at which it has grown in our digital age. Just as sustenance nourishes our bodies, data fuels our intellect, satiating the hunger for insights and understanding. 

Data is the foundation upon which the structure of information stands tall. Imagine gazing at a puzzle's scattered pieces – each is important, might be beautiful and vital, but the true picture emerges only when the pieces interlock. Similarly, data is the root of knowledge for today’s businesses. Our new Data Analysis Templates are the masterful hands that bring all that scattered knowledge and wisdom together.

These PPT Presentations emerge as essential companions in a landscape where accurate decision-making means the difference between thriving and surviving. Understanding data is pivotal in the symphony of business strategies, marketing endeavors, and research pursuits. 

The 100% customizable nature of the templates provides you with the desired flexibility to edit your presentations. The content-ready slides give you the much-needed structure.

Let’s explore!

Template 1: Data Analysis Process PPT Set

Use this PPT Set to help stakeholders understand difficulties that mar the data analysis process and gain valuable insights. Explore the crucial stages of data analysis, from establishing data requirements and efficient data collection to thorough data processing and cleaning. This PPT Design highlights the often underestimated yet pivotal phase of data cleaning. With this template, you'll understand how data lays the foundation for seamless analysis, leading to more accurate results and impactful communication. Download now!

Data Analysis Process PPT Set

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Template 2: Data Analysis Business Evaluation Process for Visualization and Presentation

This holistic PPT Bundle guides you through the complex stages of visualization and presentation while offering a profound understanding of each crucial phase. Use this presentation template to understand the essence of successful data analysis, as it breaks down the process into digestible segments. From the initial steps of business issue comprehension and data understanding to data preparation, exploratory analysis, monitoring, validation, and finally, captivating visualization and presentation – every facet is covered. This PPT Preset goes beyond mere process explanation, offering a robust framework for the holistic development of data conceptualization, collection, analysis, and cleaning procedures. Get it today!

Data Analysis Business Evaluation Process for Visualization and Presentation

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Template 3: Data Requirement Analysis PPT Bundle

Navigating challenges of problem-solving, prioritization, and data insight, this PPT Presentation presents a strategic roadmap that transforms raw information into actionable intelligence. It starts with a deep dive into the heart of your business challenges. Focusing on defining the core problems, this presentation template guides you through the process of setting priorities, ensuring every move is a step closer to your objectives. Data collection, a crucial cornerstone, is explained through insightful visual aids and organized segments. Witness the transformation of disparate data points into a coherent narrative, empowering you to decipher trends, anomalies, and opportunities.

This PPT Template equips you with the tools to not only gather data but also comprehend its implications, turning information into true knowledge. Navigating the challenges of data requirement analysis is no longer a daunting task. From security gaps that demand attention to complex data systems that require expertise, our template ensures you're prepared to overcome these hurdles with confidence. The high costs that often come with data analysis are confronted head-on, unraveling budget-friendly strategies that don't compromise on quality. Get this template today!

Data Requirement Analysis PPT Bundle

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Template 4: Big Data Analysis PPT Set

This comprehensive PPT Deck presents a pre-made Big Data Analysis funnel that guides you through the rather complex process of turning data into gold. Gain a competitive edge by understanding effective data analysis techniques of association rule learning, classification tree analysis, genetic algorithm, regression analysis, and sentiment analysis. It's more than a run-of-the-mill PPT Presentation; it's a transformative tool. Invest in a big data analysis PPT like resource that's not just about graphs and numbers; get it now. Download now!

Big Data Analysis PPT Set

Template 5: Data Management Analysis PPT Framework

For achieving business excellence, the quest for efficient and time-saving solutions is a universal endeavor. Recognizing your aspirations, we present the Data Management Analysis PowerPoint Presentation — an invaluable asset for seamless change management and effective data analysis. It incorporates PPT Slides designed to provide an effortless avenue for embracing change management and conducting incisive data analysis. It offers a cohesive platform for centralizing your objectives, ready to be shared with your team. The judicious use of text boxes empowers you to articulate your perspectives with precision on each pertinent subject. Download today!

Data Management Analysis PPT Framework

Template 6: Predictive Data Analysis PPT Layout

Get this PPT Preset to consolidate your stakeholder's grasp on predictive analytics, a discipline that uses statistical methodologies, cutting-edge machine learning algorithms, and a suite of tools to dissect historical data. This PPT Layout guides you through a well-structured journey, unfolding the essentials of predictive analytics, its foundational framework, and a suite of models that constitute its core. The significance of predictive analytics takes center stage, underscored by its multifaceted applications. Additionally, this resource has an Estimation Model PPT Slide, which explains the key tenets of diverse predictive analytics tools and their closely-knit workflows. The demarcation between the four pivotal categories of advanced analytics in this PPT deck receives careful attention. It sheds light on predictive analytics models – from classification to clustering models and beyond. Download now!

Predictive Data Analysis PPT Layout

Template 7: Dashboard For IT Operations Data Analysis

This PPT Template Dashboard is a dynamic representation of your operational landscape. This PPT Set helps track the total number of cases from inception to resolution. Visualize trends with a graph showcasing the weekly ebb and flow of opened and closed cases. Prioritize effectively, allocating resources where they matter most, as the presentation template depicts it across departments. Efficiency meets clarity as you explore the time distribution of tickets on a day-by-day basis. Gain a better understanding of workflow patterns and resource utilization. Analyze open case statuses, fostering an environment of proactive response and swift action. Download now!

Dashboard For IT Operations Data Analysis

Template 8: Quarterly Sales Data Analysis Report

Visualize your progress with ease using this PPT Template's intuitive presentation of monthly sales data. Get a clear view of team-wise statistics that showcase individual contributions, fostering a culture of recognition and growth. Uncover finer details through the nuanced comparison of total versus actual sales values, empowering you to identify trends and opportunities. Engage stakeholders in strategy evaluation as you assess team goals versus actual achievements. Pinpoint areas of excellence and those warranting attention, refining your approach. Download now!

Quarterly Sales Data Analysis Report

Template 9: Real-Time  Marketing Data Analysis

Here's a dynamic marketing analysis tool blending insights and aesthetics. It presents a pie chart comparing planned vs. actual budgets while diving deep into sections showcasing real-time marketing benefits: Elevated customer experiences, surging conversions, enhanced retention, and refined brand perception. Navigate budget allocation through intuitive bar graphs. Improve your strategy with data symphony, moving a step closer to success through informed choices. Download now!

Real-Time Marketing Data Analysis

Template 10: Data Analysis Process for Visualization and Presentation

Embark on a data-driven journey with this PPT Set. Learn the process of Data Analysis, Visualization, and Presentation to address complex business challenges. This PPT Design walks you through these stages, from issue identification and data preparation to exploratory analysis modeling. Witness raw data transform into insights through rigorous validation. Culminate in captivating visualizations and masterful presentations, setting new standards for impactful communication. Download now!

Data Analysis Process for Visualization and Presentation

Bridging Numbers and Narratives: Your Journey Through Data Analysis

In a world where data weaves the fabric of progress, our journey through this blog comes to an inspiring end. As you venture into data analysis armed with our templates, remember that each graph, each layout, and each piece of information is a brushstroke on the canvas of understanding. With every mouse click, you’re not just navigating slides; you're charting the course for informed decisions, breakthrough discoveries, and transformative strategies.

FAQs on Data Analysis

What is data analysis.

Data analysis involves inspecting, cleansing, transforming, and modeling data to derive meaningful insights, draw conclusions, and support decision-making. It encompasses various techniques, including statistical methods, machine learning, and visualization, to uncover patterns, trends, and relationships within datasets.

What are the four types of data analysis?

There are four main types of data analysis:

  • Descriptive Analysis: This type of analysis focuses on summarizing and describing the main features of a dataset. It involves statistical measures such as mean, median, mode, range, and standard deviation. Descriptive analysis aims to clearly understand the data's characteristics but doesn't involve drawing conclusions or making predictions.
  • Diagnostic Analysis: Diagnostic analysis involves digging deeper into data to understand why certain patterns or outcomes occurred. It aims to identify the root causes of specific events or trends. Techniques used in diagnostic analysis often include data visualization, exploratory data analysis, and statistical tests to uncover relationships and correlations.
  • Predictive Analysis: Predictive analysis involves using historical data to predict future events or outcomes. This type of analysis uses statistical models, machine learning algorithms, and data mining techniques to identify patterns and trends that can be used to forecast future trends. It's widely used in finance, marketing, and healthcare for making informed decisions.
  • Prescriptive Analysis: Prescriptive analysis goes beyond predicting future outcomes. It provides recommendations or solutions for specific situations based on historical and current data analysis. This type of analysis considers different possible actions and their potential outcomes to guide decision-making. Prescriptive analysis is often used in complex scenarios involving multiple variables and options.

Where is data analysis used?

Data analysis is used in a wide range of fields and industries, including but not limited to:

  • Business: Analyzing customer behavior, market trends, and financial performance.
  • Healthcare: Analyzing patient records, medical research data, and disease trends.
  • Science: Analyzing experimental results, simulations, and observations.
  • Finance: Analyzing investment trends, risk assessment, and portfolio management.
  • Marketing: Analyzing campaign effectiveness, consumer preferences, and market segmentation.
  • Social Sciences: Analyzing survey data, demographic trends, and human behavior.
  • Sports: Analyzing player performance, game statistics, and strategy optimization.

What is the main tool for data analysis?

There isn't a single "main" tool for data analysis, as the choice of tools depends on the specific tasks and the preferences of the analyst. However, some widely used tools for data analysis include:

  • Spreadsheet Software: Like Microsoft Excel or Google Sheets, used for basic data manipulation and visualization.
  • Statistical Software: Such as R and Python's libraries (e.g., pandas, numpy, scipy), used for in-depth statistical analysis and modeling.
  • Data Visualization Tools: Like Tableau, Power BI, or matplotlib/seaborn in Python, used to create visual representations of data.
  • Database Management Systems (DBMS): Such as SQL-based systems for querying and managing large datasets.
  • Machine Learning Libraries: Such as scikit-learn, TensorFlow, and PyTorch for building predictive models.

Why is data analysis important?

Data analysis is crucial for several reasons:

  • Informed Decision-Making: It provides insights that help individuals and organizations make informed decisions based on evidence rather than intuition.
  • Identifying Patterns and Trends: It helps to uncover hidden patterns, trends, and correlations in large datasets that might not be apparent on the surface.
  • Problem Solving: Data analysis aids in solving complex problems by providing a structured approach to understanding and addressing issues.
  • Improving Efficiency and Performance: It allows businesses to optimize processes, improve efficiency, and enhance performance based on data-driven insights.
  • Innovation and Research: Data analysis is essential in scientific research and innovation, helping to validate hypotheses and drive discoveries.
  • Competitive Advantage: Organizations that effectively use data analysis gain a competitive edge by better understanding their customers, markets, and internal operations.
  • Risk Management: Data analysis enables better risk assessment and management by identifying potential issues or anomalies early on.
  • Resource Allocation: It helps allocate resources effectively by understanding where investments are most likely to yield positive outcomes.

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  • Quarterly Business Review Presentation: All the Essential Slides You Need in Your Deck
  • [Updated 2023] How to Design The Perfect Product Launch Presentation [Best Templates Included]

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data analytics assignment example

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Data Analytics has been considered as a promising topic. This paper aims to review the trends of Data Analytics in terms of related publications. More specifically, in this study we analysed 18-years real-world data obtained from Web of Science database for the purpose. These data include the first relevant publication found in the database. In total, 18610 relevant publications have been identified during 2004 to 2021. According to the findings from analysing the identified publications, we suggest that Data Analytics is a glowing global topic involving affiliations and funding sponsors from different countries. On top of the industrial voice saying Data Analytics is an emerging topic, the findings from this paper can provide an additional reference for the education sector, government, and academia, to conduct, promote and support the Data Analytics related research. We believe this is the first time that a study has been conducted to comprehensively review the development trends ...

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Performance of traditional machine learning systems does not scale up while working in the world of Big Data with training sets that can easily contain petabytes of data. Thus, new technologies and approaches are needed that can efficiently perform complex and time-consuming data analytics with- out having to rely on expensive super machines. This paper discusses how a distributed machine learning system can be cre- ated to efficiently perform Big Data machine learning using classification algo- rithms. Specifically, it is shown how the Machine Learning Library (MLlib) of Apache Spark on Databricks can be utilized with several instances residing on Elastic Compute Cloud (EC2) of Amazon Web Services (AWS). In addition to performing predictive analytics on different numbers of executors, both in- memory processing and on-table scans were used to utilize the computing effi- ciency and flexibility of Spark. The conducted experiments, which were run multiple times on several instances and executors, demonstrate how to parallel- ize executions as well as to perform in-memory processing in order to drasti- cally improve a learning system’s performance. To highlight the advantages of the proposed system, two very large data sets and three different supervised classification algorithms were used in each experiment.

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The evolution of information systems and the growth in the use of the Internet and social networks has caused an explosion in the amount of available data relevant to the activities of the companies. Therefore, the treatment of these available data is vital to support operational, tactical and strategic decisions. This paper aims to present the concept of big data and the main technologies that support the analysis of large data volumes. The potential of big data is explored considering nine sectors of activity, such as financial, retail, healthcare, transports, agriculture, energy, manufacturing, public, and media and entertainment. In addition, the main current opportunities, vulnerabilities and privacy challenges of big data are discussed. It was possible to conclude that despite the potential for using the big data to grow in the previously identified areas, there are still some challenges that need to be considered and mitigated, namely the privacy of information, the existence of qualified human resources to work with Big Data and the promotion of a data-driven organizational culture. Information is now increasingly important and a successful differential, as the whirlwind of external events forces organizations to face new situations. The information becomes fundamental for the discovery and introduction of new technologies, as well as for exploring opportunities investment. It has the power to detect new opportunities, sign of the threats and reduces uncertainties during the decision-making process and, consequently, increases its quality. In this sense, the differential of companies and professionals is directly related to the value they give to information, knowledge and how they use it in meeting the demands of the market and in the search for innovative solutions. The process of decision-making is complex and rational, contemplating factors such as intuition, experiences and knowledge. Business managers are constantly going through situations where they are faced with a number of different paths, and must choose the one that leads the organization to achieve its results. Therefore, information plays a fundamental role in the decision-making process, in order to identify the various alternatives and their consequences. However, capturing relevant information for the company is a complex and difficult task. Useful data can come from anywhere and there is an increasing number of heterogeneous numbers of devices that capture data from different sources. The compilation and sharing of detailed information is only possible through the use of information and communication technologies (ICT), and this data can come from suppliers, consumers, partners and competitors. To this large volume of data coming from multiple heterogeneous sources we call Big Data, which is the next frontier for business innovation and productivity. For that reason, companies should be aware of the potentialities and vulnerabilities of Big Data and create strategies to handle large volumes of data in order to take advantage of its many potentialities. 2-Concept of Big Data Although the term "big data" is relatively new, the act of collecting and storing large amounts of information for eventual data analysis is quite old. Companies in diverse sectors of activity, mainly those of bigger dimension and with greater volume of data, have developed solutions of business intelligence (BI) to support business management processes. BI is characterized by the use of a set of methodologies, processes, structures and technologies that transform a large amount of raw data into useful information for making strategic decisions [1]. Table 1 performs a comparative *

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Top 8 Exciting Data Analytics Project Ideas & Topics [For Freshers]

Top 8 Exciting Data Analytics Project Ideas &#038; Topics [For Freshers]

Doing data Science courses has been one of the best and most reassuring career options of this generation for quite some time now. If you are an aspiring data scientist, you should be focusing more on improving your technical capabilities. By doing so, you will be increasing your skill level as a data scientist. The best way to practice your art is to take up personal projects to boost your knowledge, skills , and confidence.

Analyzing data also plays a significant role in your career growth. It is mostly about discovering new insights that can help with your decision making process. Even if you ask a veteran analyst, he will tell you that the intuition we see as consumers results from hard work. And around 80% of all data analytics assignments start with the evaluation of data. So, a data scientist needs to know more about data analysis and its types.

Rest assured, as time progresses; you will develop the necessary skills needed to collect data and produce reports based on your findings. You should also be able to:

  • Clean the web data
  • Execute exploratory analysis
  • Flush cluttered datasets
  • Visually communicate your results.

However, the most crucial part of becoming a skilled data scientist is working on various projects that focus on data scraping, exploratory analysis, and data visualization. So, let’s get started. Here are some of the project ideas that you will need to build up your job profile as a data scientist.

Data Scraping Project Ideas

1. movie data collection.

This beginner project will help you gain the necessary skills needed for a data scientist. Its primary aim is to collect and extract data for further analysis. For that purpose, you can use the IMDB website to gather information about popular movies, TV shows, actors, etc. The format for this website is relatively consistent and makes it easier to attain data for analysis. Besides, the project has great potential when it comes to data collection.

2. Job Websites

Nowadays, scraping data from job portals are used for training beginner data scientists. It is because these websites contain standard data types. You can also maximize your learning capabilities through different online tutorial sessions. The main objective is to collect data and information about job titles, companies, locations, skills, etc. This project has an excellent aptitude for further visualization enhancements, such as comparing and mapping out the difference between talents and companies.

3, Online Shopping Sites

Another way to improve your necessary data analytics skill set is to scrap product and cost data from online shopping sites. For example, you can collect data and information about the trending Bluetooth headsets on Flipkart. And the collected data is analyzed further for processing the information you need for the project. It is wiser to start experimenting and analyzing data that uses more straightforward algorithms first. And then, pave your way to getting comfortable with intricate data design.

4. Social Media Platforms

A beginner level data analyst is expected to scrape data from social media websites. For instance, you can collect data from unconventional sites like Reddit or Twitter. Searching for keywords, upvotes, user data, etc., is all possible in Reddit, giving you ample resources for further investigation.

The website has gained popularity over the past years for its straightforwardness and content creation. As a data analyst, you can compare and analyze popular keywords with upvoted content. You can also take it a step further with exploratory analysis to check for any correlation between them.

Exploratory Data Analysis Project Ideas

1. global suicide scale.

The next step in improving your data scientist skills is to carry out exploratory data analysis on the data structure, patterns, and characteristics. For example, analyze the datasets that cover the numbers of suicide cases happening in different countries.

Also, find information on almost everything you get your hands on, ranging from the year, gender, the age to population and GDP. After completing the data collection process, try to see if any patterns involve suicide rates. If you get better at analyzing data, you can evaluate the percentages based on the rise or fall in suicide rates.

2. UN World Happiness Report

Compared to the previous project, this assignment involves the World Happiness report. This particular report keeps track of six main factors that measure happiness around the world. The six factors are life expectancy, economy, social support, lack of corruption, freedom, and generosity. Multiple questions can pop into your mind based on the report, which is an excellent exercise to expand your data analyst skills.

The first step will be to collect and extract the data needed for your project. You can find the report to be well-organized and consistent, making it easier for analysis. The main focus here will be to observe the patterns and data structure used to design the world report. Probing for more information is the best way to perform a complete analysis.

Utilizing the right dataset will give you room to enhance your technical skills. If you find yourself drawing a blank when it comes to complex structures, try resetting the analysis to your advantage. Make it simple, clear and concise to extract the necessary information needed to achieve your project goals.

Related:  Top Data Science Project Ideas

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Data visualization project ideas, 1. covid-19 world report.

Apart from scraping, tiding, and analyzing the data, we have to find the means to communicate our results visually. In this case, we will be inspecting the Covid-19 health report. If you visit some famous sites like Kaggle, you get access to several thousands of Covid-19 datasets. The next step would be to collect data and scrap it. Tidy up the collected data for further investigation. Organized datasets make it easier for the analyst to visualize the results.

You can also perform various comparisons between different countries based on the number of active cases vs. the number of recovered patients. Producing charts and graphs are the critical elements needed for visualizing the results. And if you want to dive deeper, look for some online tutorials that can help you.   

2. Instagram

It does not matter whether you are interested in actors or brand culture. What matters is that Instagram has a unique set of data and information on various topics, making it a perfect instrument for visualization. The available options for analyzing this social media platform are boundless.

You can track the changes in the most followed accounts in real-time. Creating and developing bar charts based on the gathered information can help achieve your project goals. Advertising plays an essential role in this social media platform. Even comparing the company brands with popular brands will be an excellent exercise to amp up your tech skills.

Also Read: Top Data Analytics Project Ideas

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Top Data Science Skills to Learn

After mastering the necessary skills needed for data scraping, exploratory analysis, and data visualization, you can look forward to improving your data analyzing abilities further. You can start by taking up machine learning projects. Some of the projects include sentiment analysis, predictive analysis, and many more.

A vital element to take away from this post is that practice makes it perfect. So, try spending time on more straightforward projects at first to get comfortable with algorithms that are frequently used on datasets. Then, climb your way to taking up bugger projects that can help you grow in the industry.

If you are curious about learning data science to be in front of fast-paced technological advancements, check out upGrad & IIIT-B’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.


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In addition to the broad range of project ideas, data analysts face a number of challenges while working on these projects. 1. One of the main issues you'll face when it comes to monitoring real-time environments is that there aren't many suitable solutions. You should familiarise yourself with the various technologies you'll need when working on a big data project. 2. One of the most common data analysis issues is how long it takes to process data after virtualization is completed. More commonly, latency issues occur because of high-level performance demands, and most of these tools require it. 3. Higher-level scripting may be required when continuing to work on big data analytics projects, particularly if you're encountering tools or problem situations that you haven't used before 4. Inadequate security leads to leaks of confidential data, which has disastrous consequences for both your project and your work. Of can happen, so you must always be cognizant of this. 5. End-to-end testing can't be done with just one tool. Make sure you determine which software will be required to accomplish a particular project. 6. Occasionally, you'll find a dataset too large for you to manage. Alternatively, you may need to validate more data to finish the project.

Some good data analysis projects are – 1. Classify 1994 Census Income Data. 2. Analyze Crime Rates in Chicago. 3. Health status prediction. 4. Anomaly detection in cloud servers. 5. Malicious user detection in Big Data collection. 6. Tourist behaviour analysis. 7. Credit Scoring. 8. Electricity price forecasting.

To be successful in the Big Data industry, you must acquire these technologies. 1. The Apache Storm software is used for handling data streams in real-time. Java and Clojure are used, and integration with any computer language is possible. 2. MongoDB is indeed an open-source NoSQL database similar to modern databases. 3. Cassandra is used for managing massive quantities of data across several servers, with a distributed database management system. 4. In comparison to other Big Data technologies, Cloudera is among the fastest and most secure. 5. Refining data, converting it into different formats, and cleaning data are among the numerous applications for which OpenRefine is widely used.

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Online Free Samples

Big Data Analytics Assignment

Task: Worldwide Influence of Big Data Analytics on the Business Priorities and Decision-making Big Data analytics has entirely transformed the approaches as well as modes of the recent business scenarios and this particular concept is simply comprised of four important attributes such as value, velocity, volume as well as variety (Chen, Chiang and Storey 2012). This particular area of research can also result in the useful insights that in turn would aid the better strategic decisions in relation to the businesses. The concept of the big data analytics has risen beyond the storage of numerous information and it has also made the use of the analytical methods iterative along with the ongoing trends of marketplaces in the world of the mobile applications (LaValle er al., 2011). For an instance, businesses in today’s world are capable of analyzing the information on an immediate basis along with the speed of in-memory and Hadoop analytics combined with the capability of analyzing the new data sources (Demirkan and Delen 2013). Therefore, at today’s date, the organizations all over the globe are significantly utilizing the Big Data in driving the decisions of businesses as well as enhancing and improvising the ROI and the performances of the businesses (Chen, Chiang and Storey 2012). Big Data Analytics is a widely accepted or considered topic in the course and the profession of the engineering management as it has been turned into an impressive innovation in the engineering ground because it deals with offering numerous new ways of several technology integration.

Important Research Question How does Big Data Analytics influence the business decision-making and business priorities as well? Independent Variable - Big Data Analytics Dependent Variables - Decision-making and Business Priorities

Clarity on the Question Big Data Analytics deals with helping the companies in harnessing their data as well as utilizing it for identifying the new scopes, which in turn can result in the smarter moves of businesses, happier consumers, higher profits as well as the more efficient business operations (LaValle er al., 2011). Thus, such capability of working faster and staying agile can five the companies a competitive edge that they did not have before (Chen, Chiang and Storey 2012). Therefore, in order to make successful the application of the Big Data Analytics in the business operations of companies, it is very important to analyze the question mentioned above.

Introduction: Big data analysis is a term that is applied to a set of data that is beyond the preview of traditional database. It is used to store data in bulk thereby making way for management of data in a systematic manner. Organizations operating should go for management of data that would be helpful for future projection and implementation. It is important to note that data storage is a cumbersome procedure so should be stored in a definite chamber. Big data provides the platform wherein storage is made easy and exquisite. In fact with the inclusion of big data storage development in the field of decision making can be seen as it helps in understanding the present scenario from the consolidated stock and helps in delivering in a spontaneous manner. In this data analysis assignment we have strictly followed the format given in marking rubrics to cover in helping the student to cover all the deliverables in the assignment. Using the format given below will help you in drafting the data analysis assignment in a descent way.

Aims and objectives: The aim of this data analysis assignment is to define the possibilities that can be undertaken in the storage of data thereby making way for production in bulk for the welfare of the organization. In fact with the inclusion of this concept initiation can be seen in the development of storage capacity. Understanding the requirements of the consumers is of utmost importance thereby delivering taking into account the possibilities of productive development is always at hands (Gandomi & Haider, 2015).

Objectives: The objectives of this data analysis assignment is to

  • Provide the platform wherein bulk quantity of data can be stored thereby making way for introduction of more inputs
  • Provide the platform wherein product kept at stock can be provided to usage t any point of time.
  • Emphasize on team performance in building a coherent atmosphere in delivering output.
  • Employment of personnel in keeping records in delivering and maintaining data in the most comprehensive manner

Research Question: How does Big Data help in decision making for the organization? How convenient it is for the organizational personnel in maintaining data in bulk at a single point of time without any difficulty? How the concept of Big Data would be useful in future possibilities for an organization?

Literature Review: Big data analytics is one of the most developed and advanced means in maintaining forum for management of resources. It would help in building momentum for the organization. It would help in delivering in the most comprehensive manner. It would help in redefining concepts of progressive development. It acts as the forum that would help in understanding tastes and the preferences of the consumer of the consumers thereby act in conformity to it. It is important to note development is possible when there is the amalgamation of top authorities along with the technological experts, administrative experts, quality control experts, administrative experts so and so forth. In fact with the amalgamation of different departments inclusion of productive results can be seen at the outset. Big data is driving the corporate world by storm. It has helps in breaking barriers thereby making way for exclusive performance for the growth and development of the organization at large (García et al., 2016).

Management has improved on a significant manner. It has led to outclass probabilities and thereby turn them into possibilities in the organizational forum. In fact with the inclusion of probabilities development can be seen in the functioning at a rapid pace. Analysing information of the organization has been helpful and convenient thereby making way for progressive development in the performance of the organization. Big Data is the cluster of numerous information that is helpful and conclusive in providing the platform for all round development of the organization in the long run. In fact, with the inclusion of probabilities deliberate attempts have been made by personnel to improve the quality of insight in the functioning of the organization (Assunção et al., 2015).

In the most comprehensive ground, there is scope for all round development once functioning is being undertaken for the betterment of the organization. Onset of probabilities along with possibilities can help in diversifying performance of the organization in the long run. Big data acts as the warehouse that would help in maintaining data that would be used by the organizations in the functioning of the organization in the long run. It is the platform of numerous technology integration. Decision making of the business is being developed and enhancement can be seen in the functioning of the organization .In fact with the adaptation of the decision making future prospects can be developed in the most comprehensive manner. It has comprehensively changed the concept of data storage thereby making way for all round development in the delivaration of performance of the organization. Big Data is one of the conclusive and the most promising aspect in the professional world (Rajaraman, 2016). It has led to conceptualize on the propositions related to the functioning of an organization. In fact with the advent of technology need of the hour has been towards productive development rather than inclined towards promulgation of uncertainties.

In the recent context development is possible through the application of resources that would be helpful in the functioning of an organizational forum. In fact with the inclusion of propositions regarding the functioning of an organization it is important on the part of the officials in deliberately inclined towards development that would help in building a platform that would be helpful in maintaining momentum for future growth of the organization. A definite framework that would help in maintaining, should structure, inclusion of swiftness on the part of the development is necessary in this regime (Hu et al., 2014). Moreover, application of development would help in igniting a forum that would be effective in the development of the organization in the long run.

Data analysis: Data analysis is one of the most important and decisive aspect in the functioning of an management forum. In fact with the inclusion of data analysis one can understand the dimensions of big data thereby can make way for all round development in the undertaking of the same (Hashem et al., 2015). In this context there would be undertaking of qualitative analysis that would help in understanding the pros and cons of the management scenario. Development is possible by the application of resources in the functioning of an organizational forum. In the context of Big Data one might question on the implementation of data and its necessity. Qualitative data has been one of the most important aspects in redefining the propositions of research. It helps in demonstrating the consumer insights thereby making way for understanding the propositions in the long run. Generating consumer insights is the tedious task and most importantly implicit on the part of the developers of management in delivering in the most comprehensive manner (Chen et al., 2014).

Online research helps in understanding the expressions and the opinions of the people that are being asked regarding the particular prospect in this regard. In fact with the application of data analysis it is important to note that development seeks a hike once deliberations are the need of the hour. In fact with the application of development it is necessary to review the propositions in the functioning of management of an organization. It is a challenging proposition on the part of the operators and the expert officials in delivering in the functioning of the management. Deliberate attempt can be made on the part of the execution of the policies of the management thereby making way for all round development for the organization. In fact, taking a note of the records that has been used in the functioning of an organizational forum. Development measures should be undertaken by the management that would help in delivering in the most comprehensive manner. It would lead to deliberations in the functioning of the management as a whole. It is important to note that going for records thereby understanding the scenario is important as would help in improving the quality and prospect of the project (John Walker, 2014).

Big Data is blessing in disguise. It is imperative on the part of the officials in delivering in the most comprehensive manner. In fact there has to be recording of minutes that would help in maintaining records in the functioning of the management as a whole. In order to incite development it is imperative on the part of the management to act in forum thereby making way for productive means that would help in decision making of the organization as a whole. It is important in this context to deliver according to the propositions thereby making way for absolute development in the long run .In order to inculcate this training and development is necessary on the part of the officials in making way for alternatives that would help in maintaining forum for the management of the organization. Collection of data has to be in the integration mode thereby making way for transition time and again. Ineffectiveness has to be eliminated thereby making way for outreach that would be helpful in making propositions for development in the significant manner. In order to incorporate development review management has to be undertaken by the management time and again in order to incorporate development in the long run.

Research can be undertaken by the application of online medium thereby making way for all round development. Analytical tools should be used that would help in transition in the functioning of an organizational forum. In fact with the application of automation in the functioning of an organizational forum it is important to note that development would led to development in the long run. Needs and wants seems to be increasing accordingly. It has led to maintaining big data that would help in meeting the needs of the consumers thereby making way for enhancement in the customer base of the organization. It helps in escalation in yielding positive results for the functioning of the management thereby making inroads for all round development of the organization time and again. Communication plays an important role in the up gradation in the functioning of the organization. In order to incorporate effectiveness in the functioning of the management there has to be incorporation in the functioning of the management making inroads for productive development on a simultaneous basis (Kambatla et al., 2014).

Transition is the need of the hour therefore should be incorporated with the inroads of corporate simulation. Corporate culture should be maintained during the course of proceedings of the management, which is possible in the functioning of the management. In order to incorporate development it is necessary to inculcate transition in the functioning of the management thereby making inroads for effective development in the functioning of the organization in the significant manner. In order to incorporate development it is necessary to go for effective implementation of policies thereby making way for effective results (Talia, 2013).

Gantt chart

Conclusion: From the above evaluation in this data analysis assignment, it can be ascertained that functioning of big data is important to reconcile with the functioning of the management. In order to inculcate this training and development is necessary on the part of the officials in making way for alternatives that would help in maintaining forum for the management of the organization. Collection of data has to be in the integration mode thereby making way for transition time and again. Ineffectiveness has to be eliminated thereby making way for outreach that would be helpful in making propositions for development in the significant manner. In order to incorporate development review management has to be undertaken by the management time and again in order to incorporate development.

Reference Assunção, M. D., Calheiros, R. N., Bianchi, S., Netto, M. A., & Buyya, R. (2015). Big Data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing, 79, 3-15.

Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), 171-209.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.

García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016). Big data preprocessing: methods and prospects. Big Data Analytics, 1(1), 9.

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115.

Hu, H., Wen, Y., Chua, T. S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE access, 2, 652-687.

John Walker, S. (2014). Big data: A revolution that will transform how we live, work, and think.

Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561-2573.

Rajaraman, V. (2016). Big data analytics. Resonance, 21(8), 695-716.

Talia, D. (2013). Clouds for scalable big data analytics. Computer, 46(5), 98-101.


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  1. FREE 10+ Sample Data Analysis Templates in PDF

    data analytics assignment example

  2. 3CO02 Principles of Analytics Assignment

    data analytics assignment example

  3. Assignment 4: Data Analysis (Group Assignment) Objectives: After

    data analytics assignment example

  4. 50+ SAMPLE Data Analysis Templates in PDF

    data analytics assignment example

  5. FREE 7+ Data Analysis Samples in Excel

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  6. How-To: Data Analytics for Beginners

    data analytics assignment example



  2. Data Analytics with Python ASSIGNMENT 1 WEEK 1 NPTEL SWAYAM 2024

  3. Data Analytics Assignment Description

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  5. A university dashboard (M1 Big Data Analytics assignment)

  6. MDA621 Software Practice for Big Data Analytics Assignment 2


  1. 5 Data Analytics Projects for Beginners

    If you don't know how to code, don't worry. You'll also find several tools that automate the process (many offer a free trial), like Octoparse or ParseHub. If you're unsure where to start, here are some websites with interesting data options to inspire your project: Reddit Wikipedia Job portals

  2. Homework 3: Data Analysis

    CSE 163, Summer 2020: Homework 3: Data Analysis Overview 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.

  3. 40 sample dataset for data analysis projects

    COVID19 Stunning Dashboard in Excel Tomorrow, we will have a live session where we get to build a stunning storytelling dashboard for COVID19 Data with Excel. Yes, Microsoft Excel can also be used to build a stunning and interactive dashboards. For today, I will should you how to import COVID19 Data into Excel. Watch the video below for the session

  4. Assignment 2: Exploratory Data Analysis

    Step 1: Data Selection First, you will pick a topic area of interest to you and find a dataset that can provide insights into that topic. To streamline the assignment, we've pre-selected a number of datasets for you to choose from. However, if you would like to investigate a different topic and dataset, you are free to do so.

  5. Data Analytics Project Assignment

    Deliverable A minimum of one page description of your data analytics project which must include the following: A title for your project A brief description of the project Major stakeholders who would use the information that would be generated from your analysis and how they would use/benefit from that information

  6. 20 Data Analytics Projects for All Levels

    1. Exploring the NYC Airbnb Market In the Exploring the NYC Airbnb Market project, you will apply data importing and cleaning skills to analyze the Airbnb market in New york. You will ingest and combine the data from multiple file types, and clean strings and format dates to extract accurate information. Image by Author | Code from the project

  7. 36 Data Analytics Project Ideas and Datasets (2023 UPDATE)

    Python Data Analytics Projects. Python is a powerful tool for data analysis projects. Whether you are web scraping data - on sites like the New York Times and Craigslist - or you're conducting EDA on Uber trips, here are three Python data analytics project ideas to try: 7. Enigma Transforming CSV file Take-Home.

  8. What Is Data Analysis? (With Examples)

    What Is Data Analysis? (With Examples) Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions. "It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holme's proclaims ...

  9. Data Analytics: Definition, Uses, Examples, and More

    There are four key types of data analytics: descriptive, diagnostic, predictive, and prescriptive. Together, these four types of data analytics can help an organization make data-driven decisions. At a glance, each of them tells us the following: Descriptive analytics tell us what happened. Diagnostic analytics tell us why something happened.

  10. Big Data Analytics

    Big Data Analytics. CSE545 - Spring 2019. Assignment 1. Assignment 2. Assignment 3. Final Team Project.

  11. PDF Developing a Quantitative Data Analysis Plan

    A Data Analysis Plan (DAP) is about putting thoughts into a plan of action. Research questions are often framed broadly and need to be clarified and funnelled down into testable hypotheses and action steps. The DAP provides an opportunity for input from collaborators and provides a platform for training. Having a clear plan of action is also ...

  12. Examples of Business Analytics in Action

    There are four key types of business analytics: descriptive, predictive, diagnostic, and prescriptive. Descriptive analytics is the interpretation of historical data to identify trends and patterns, while predictive analytics centers on taking that information and using it to forecast future outcomes.

  13. PDF Data Analytics Projects with Microsoft Excel

    3. EXCEL ANALYTICS PROJECT 2 - DATA VISUALIZATION Data presentation and visualization are two of the top skills required by employers (Fisher, 2016). In this assignment, students will learn about appropriate chart types for data visualization and create several charts and graphs using Microsoft Excel. Prerequisites Skills

  14. PDF Data Analysis: Statistical Assignment

    data. 2. Develop a research question that you would like to consider in this area. Consider what you hope to prove/disprove by carrying out a statistical analysis and consider whether a statistical analysis is achievable and/or appropriate to the data. 3. Think about where you will get data to carry out research in this topic e.g.:

  15. How to Write a Business Analysis Report [Examples and Templates]

    A business analysis report examines the structure of a company, including its management, staff, departments, divisions, and supply chain. It also evaluates how well-managed the company is and how efficient its supply chain is. In order to develop a strong strategy, you need to be able to analyze your business structure.

  16. (PDF) Practical Data Analysis: An Example

    Practical Data Analysis: An Example. Before talking about the full-fledged data analysis process and di ving into the details. ... While the number of data analysis projects is rapidly expanding ...

  17. Top 10 Data Analysis Templates with Samples and Examples

    However, some widely used tools for data analysis include: Spreadsheet Software: Like Microsoft Excel or Google Sheets, used for basic data manipulation and visualization. Statistical Software: Such as R and Python's libraries (e.g., pandas, numpy, scipy), used for in-depth statistical analysis and modeling.


    Data Analytics has been considered as a promising topic. This paper aims to review the trends of Data Analytics in terms of related publications. More specifically, in this study we analysed 18-years real-world data obtained from Web of Science database for the purpose. These data include the first relevant publication found in the database.

  19. Top 8 Exciting Data Analytics Project Ideas & Topics [For Freshers]

    Data Scraping Project Ideas. 2. Exploratory Data Analysis Project Ideas. 3. Data Visualization Project Ideas. 4. Conclusion. Doing data Science courses has been one of the best and most reassuring career options of this generation for quite some time now. If you are an aspiring data scientist, you should be focusing more on improving your ...

  20. BUSI 650

    Mean and other statistical tools for data analysis. 32 pages 2021/2022 100% (1) 2021/2022 100% (1) Save. Report learning - Loan Approval New. ... Business Analytics Assignment 1. 14 pages 2023/2024 100% (3) 2023/2024 100% (3) Save. BUSI-650 Individual Paper II. ... Sample mean: 28.16 : Sample N: 31 : Sample Stdev: 4.77 : Average age : 27.93 ...

  21. PDF Assignment 2: Sampling & Data Analysis (Group Assignment)

    Explain the sampling and data analysis procedures used in research reports Determine whether the researcher wants to generalize his/her specific findings and/or conclusions to a group of people larger than those included in the sample and evaluate the degree to which a given sample is adequate for generalizing findings and/or conclusions


    Big Data Group report - big data assignment Analysis . Big Data and Analytics 100% (8) 26. Itech-1103 assignment report. Big Data and Analytics 100% (7) 16. ITECH1103 Assignment 1 SQL Report Kishani Agulugahagamage 30397085. Big Data and Analytics 100% (6) 22.

  23. 26 Data Science Interview Questions You Should Know

    Q: In an A/B test, how can you check if assignment to the various buckets was truly random? Data Analysis Questions . 16. Data Analytics Project Approach . Q: What process would you follow while working on a data analytics project? 17. Outliers Treatment . Q: How do you treat outliers in a dataset? 18. Understanding Data Visualization

  24. Big Data Analytics Assignment Sample

    Answer Introduction: Big data analysis is a term that is applied to a set of data that is beyond the preview of traditional database. It is used to store data in bulk thereby making way for management of data in a systematic manner.