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Data Analyst Career Path: A Step-by-Step Guide to Advance Your Career

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Being a data analyst is an exciting and promising career choice in today’s data-driven world. As companies continue to rely heavily on data to make informed business decisions, the demand for skilled data analysts is on the rise. If you are considering a career in data analytics or are already on this career path and want to advance further, this comprehensive guide will provide you with a step-by-step data analyst career path roadmap to excel in your profession.

Mastering Core Skills for Your Data Analyst Career

To excel in the field of data analytics, it is important to master some core skills that are essential for success. These skills are the foundation upon which a successful data analyst career is built. 

In addition to the key skills mentioned, there are a few more aspects that can further enhance your capabilities in this dynamic field.

The field of data analytics is constantly evolving, with new tools and techniques being developed regularly. By staying informed and adapting to these changes, you can ensure that your skills remain relevant and in demand. Some core data analyst skills to master include- 

• Strong Analytical Skills : Data analysts are responsible for collecting, analyzing, and interpreting data. Having strong analytical skills is crucial for effectively interpreting complex datasets and extracting meaningful insights.
• Problem-Solving : Around 90% of analytics involves critical thinking , and knowing the right questions to ask. And the ability to effectively solve complex problems using data-driven approaches is crucial for success.
• Proficiency in Programming Languages : Familiarity with programming languages like Python, R, and SQL is essential for data analysts. These languages are commonly used for data manipulation and analysis.
• Data visualization : Being able to present data in a visually appealing and understandable way is an important skill for data analysts. Tools like Tableau and Power BI can be used to create interactive data visualizations.
• Domain knowledge : Having domain knowledge in the industry you work in is advantageous. It allows you to better understand the data you are working with and make more informed decisions.

By constantly honing these skills and staying up to date with industry developments, you will not only position yourself as a proficient data analyst but also as a forward-thinking professional ready to tackle the challenges of tomorrow. Remember, these are just a few core skills; there are many more in the bucket to explore and master.

Progressing in Your Data Analyst Career Path

As a data analyst, it is important to continuously strive for growth and progress in your career- transitioning from a junior position to a mid-level or senior data analyst position, the pace at which you advance in your career will depend on factors such as the company’s size and whether you’re advancing internally or looking for any external opportunities. 

Here are some key strategies to continuously enhance your skill set and advance in your data analyst career path:

• Continual learning : Stay updated with the latest trends, tools, and techniques in data analytics through online courses, workshops, and industry publications.
• Seek mentorship : Find a mentor who has extensive experience in the field and can provide guidance and support in your career journey.
• Specialize in a niche : Consider specializing in a specific area of data analytics, such as finance, healthcare, or marketing. This can make you an expert in your chosen field and open up new opportunities.
• Take on challenging projects : Volunteer for projects that push your limits and allow you to demonstrate your problem-solving skills. This can help you stand out and gain recognition within your organization.
• Obtain certifications : Certifications in data analysis and related areas, such as data visualization or machine learning, can enhance your credibility and validate your skills.

To align your career goals with available opportunities, start by defining your long-term career objectives. Research the industry to understand the types of roles that align with your goals and the skills they require. 

The data analyst career path isn’t one-size-fits-all. Different organizations and sectors offer different opportunities for growth and progress, so here flexibility and adaptability are the key. 

Moving forward, now let us discuss various data analyst career paths that you can pursue:

Career Path No. 1: Specialist Data Analyst Career Paths

Data analytics offers domain-specific career opportunities, allowing you to specialize in areas like finance, healthcare, cybersecurity, etc. Each specialty presents unique challenges and prospects. Given the high demand for data analysts, choosing a path that aligns your interests with your analytical skills is advisable for a rewarding career.

Here are some specialist data analyst career paths you can consider:

Operations Analysts

Operations analysts are key to boosting organizational efficiency, analyzing data to identify and resolve inefficiencies, and optimizing processes and resource use. Their strategic recommendations are vital for reducing costs and enhancing overall operational performance.

Business Intelligence Analyst

Business intelligence analysts are crucial for leveraging data to offer strategic insights, aiding organizational decision-making and goal achievement. They detect trends and assess customer behaviors, converting complex data into clear, actionable guidance.

Market Research Analyst

Market research analysts analyze data to identify market trends and consumer preferences, providing essential insights that help organizations align their marketing strategies and product development with market demands to stimulate growth.

Financial Data Analyst

Financial data analysts analyze financial data to aid in financial planning, risk management, and investment decisions. Their insights help organizations assess financial health, manage risks, and optimize investment strategies, driving informed financial choices and profitability.

Health Data Analyst

Health data analysts in the healthcare industry analyze patient data to improve care and inform policy. Their work helps identify trends, enhance patient treatment, and optimize healthcare resource allocation, contributing significantly to data-driven healthcare improvements.

Statistical Analyst

Statistical analysts utilize statistical methods to uncover insights from data, aiding organizations in data-driven decision-making and optimization of business processes. Their role is crucial in identifying trends, forecasting outcomes, and providing a foundation for strategic decisions.

Supply Chain Analyst

Supply chain analysts optimize supply chain efficiency, reduce costs, and improve operational processes. They utilize data analysis to pinpoint improvement areas, streamline operations, and enhance supplier relationships, for smoother and more effective supply chain management.

Systems Analysts

Systems analysts enhance organizational IT systems, assessing and optimizing system performance and security. They recommend improvements and implement upgrades, ensuring the technology infrastructure supports business goals efficiently.

Data Quality Assistant

Data quality assistants ensure the reliability and accuracy of data through meticulous validation and cleaning processes. Their work underpins the integrity of data-driven decisions and strategies, safeguarding the foundational data quality in organizations.

These are just a few examples of specialist data analyst career paths. Depending on your interests and skills, you can choose a career path that aligns with your goals and aspirations. 

Career Path No. 2: Transitioning From Data Analyst to Data Scientist

For data analysts looking to expand their skill set and take on more advanced roles, transitioning to a data scientist position can be a logical next step. Data scientists are responsible for developing and implementing complex algorithms, predictive models, and machine-learning solutions.

To make a successful transition from data analyst to data scientist, consider the following steps:

• Develop programming skills : Data scientists often have advanced programming skills in languages like Python, R, and Java. Strengthening your programming skills can open up opportunities for more advanced data science roles.
• Learn machine learning : Familiarize yourself with machine learning concepts and algorithms. Understanding how to train and deploy machine learning models is a valuable skill in the field of data science.
• Build a strong foundation in statistics : Data scientists rely heavily on statistical analysis to draw meaningful insights from data. Enhancing your statistical knowledge will enable you to tackle complex data science problems.
• Work on real-world projects : Completing data science projects can showcase your practical skills and provide you with valuable experience. Consider working on Kaggle challenges or contributing to open-source projects.
• Obtain relevant certifications : Certifications in data science, machine learning, or related areas can boost your credibility as a data science professional.

By investing time and effort into developing the necessary skills, you can successfully transition from a data analyst to a data scientist and unlock new career opportunities.

Career Path No. 3: The Management Career Path

As a data analyst, you also have the option to transition into management positions This path involves taking on leadership roles, managing teams, and overseeing data analytics projects.

Here’s how to transition from data analyst to data analyst manager:

• Sharpen Your Leadership Skills: Take on extra responsibilities, mentor junior analysts, and lead projects to showcase your leadership potential.
• Boost Your Business Acumen : Gain a solid understanding of budgeting, resource allocation, and strategic planning, crucial for data-driven decision-making in a management role.
• Consider Management Certifications : Certifications in management or project management demonstrate your commitment to leadership and enhance your resume.
• Network Like a Pro : Build strong connections with industry professionals; they can offer valuable insights and potential career opportunities.

Transitioning into a management role can be a rewarding career step for data analysts who enjoy leading teams and driving strategic initiatives. Some companies may require you to have a master’s degree in business administration (MBA) or data analytics to get into these higher-lever job roles. 

Career Path No. 4: Lateral Movement into Various Job Roles

While some data analysts prefer to specialize in a specific area, others may be interested in exploring different job roles and industries. Lateral movement allows data analysts to gain diverse experience and expand their skill set.

To pursue lateral movement in your data analyst career, consider the following strategies:

• Identify transferable skills : Assess your existing skills and identify how they can be valuable in different roles or industries. For example, strong analytical and problem-solving skills are highly transferable.
• Research different industries : Explore industries that interest you and align with your career goals. Research the specific data analytics roles available in those industries and identify any additional skills or knowledge required.
• Gain relevant experience : If necessary, acquire additional experience or certifications that are specific to the role or industry you wish to transition into. This will make you a more competitive candidate.

Here are some job roles that can be considered – 

• Project Manager
• Product Manager
• Operations Manager
• Customer Success Manager
• Strategy Analyst
• Business Development Specialist
• Sales Analyst
• IT Consultant

Career Path No. 5: Career as a Data Analytics Consultant

Once you get enough experience as a data analyst, around 6-8 years, another data analytics career path can be working as a data analytics consultant or freelancer. As a consultant, you will work with clients from various industries to solve their data-related challenges and provide strategic insights.

To pursue a career as a data analytics consultant, consider the following steps:

• Build a strong foundation in data analytics : Develop expertise in different data analytics techniques, tools, and methodologies. This will enable you to provide valuable insights to your clients.
• Build a portfolio of successful projects : Showcase your experience and expertise by highlighting successful projects you have worked on. This will enhance your credibility as a consultant.
• Create a professional network : Connect with professionals in the consulting industry and attend industry events where you can meet potential clients and collaborators.
• Market your services : Develop a strong online presence on freelance platforms like Upwork, Freelancer, Fiverr, and Toptal to connect with potential clients and offer your data analysis services, expanding your reach and business opportunities.

Working as a data analytics consultant offers the opportunity to work on diverse projects, collaborate with different clients, and continuously learn and grow in your profession.

Getting Your First Job as a Data Analyst

Starting your data analyst career can be challenging, especially if you have limited professional experience in the field. However, there are several steps you can take to increase your chances of landing your first job as a data analyst:

• Earn a Relevant Degree : Pursuing a degree in a field like data science, statistics, or mathematics can strengthen your qualifications as a data analyst.
• Gain Practical Experience : Internships, freelance projects, or participation in Kaggle competitions can provide you with hands-on experience and help you build your portfolio.
• Develop a Strong Resume : Develop an ATS-friendly resume and tailor your resume to highlight your relevant skills and experiences. Including projects you have worked on and any certifications you have obtained can demonstrate your proficiency as a data analyst.
• Network : Networking can lead to valuable connections and job opportunities. Attend industry events, join professional organizations, and engage with data analysts in online communities. LinkedIn is a great way to connect with industry professionals. 

By following these steps, you can increase your visibility and attract potential employers who are seeking talented data analysts.

Employers value candidates who can not only analyze data but also provide actionable insights and solutions based on their findings. Highlighting specific examples of how you have tackled complex problems in the past can set you apart from other applicants.

Data Analyst Salaries by Role: How much you can make?

So far, we’ve explored various data analytics career paths in the field of data analysis. Now, you might be wondering what each of these roles pays. Overall, every data analyst position pays well, whether it be entry-level or higher-level positions. As you progress in your career and gain more experience and skills, your earning potential as a data analyst is likely to increase. Additionally, obtaining relevant certifications and developing expertise in a niche area can further boost your market value as a data analyst.

According to Glassdoor, the average annual salary for data analysts in the United States ranges from $62,000 to $96,000 per annum, and in India, the salary ranges from ₹4L to ₹10L per annum.

Specialist data analysts’ career paths, such as financial data analysts and business intelligence analysts, tend to have higher earning potentials due to the complexity and specialization of their roles. Let’s check out the average base pay of some of these job roles based on Glassdoor –

Certifications and Courses for Career Growth in Data Analytics

Certifications and specialized courses can play a crucial role in your data analytics career path. They validate your skills and knowledge, making you a more competitive candidate for job opportunities and promotions.

Here are some prominent certifications in the field of data analytics:

• Microsoft Certified: Azure Data Scientist Associate
• Google Certified Data Analytics Professional
• SAS Certified Data Scientist
• Data Science Council of America (DASCA) Senior Data Scientist (SDS)

Obtaining these certifications demonstrates your commitment to your profession and can open up new doors for career growth and advancement.

How Scaler Can Help You in Your Career Growth?

If you are looking for a comprehensive course to master data science, then you can consider Scalers’ Data Science Course which can significantly aid in your career growth as a data analyst. 

This course provides deep dives into Python programming, statistics, and machine learning, equipping you with the skills required to excel in the data analytics field. Through hands-on projects and industry mentorship, Scaler’s course ensures practical experience, enhancing your problem-solving abilities and preparing you for advanced roles in data analytics. 

This structured learning path can help you transition into more specialized or higher-level data analytics positions.

A career as a data analyst offers numerous opportunities for growth, advancement, and success. By continuously developing your core skills, exploring different career paths, and staying updated with the latest trends and technologies, you can position yourself as a highly sought-after data analyst.

Remember to network, seek professional development opportunities, and always strive for excellence in your profession. With dedication and a passion for data analytics, you can build a fulfilling and rewarding career in this dynamic field.

Frequently Asked Questions

Do data analysts require coding.

Yes, coding is a fundamental skill for data analysts. Proficiency in programming languages like Python, R, and SQL is crucial for data manipulation, analysis, and visualization.

What skills do data analysts need?

Data analysts require strong analytical skills, programming skills, data visualization skills, and domain knowledge in the industry they work in. Effective communication, problem-solving abilities, and attention to detail are also important skills for data analysts to possess.

Is data analyst a good career in 2024 and beyond?

Yes, data analytics is a rapidly growing field and is expected to continue growing in the coming years. With increasing volumes of data being generated, the demand for skilled data analysts is likely to remain strong.

Will AI replace data analysts in the near future?

While AI and automation have the potential to automate certain tasks performed by data analysts, they are unlikely to replace data analysts entirely. Data analysts bring critical thinking, domain knowledge, and creativity to their work, which are essential for interpreting data and making strategic decisions.

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How to Write a Data Analyst Job Description: Important Skills and Role Responsibilities

Use these job description examples to guide your writing.

The terms may sound similar, but data analysts , data engineers and data scientists are actually very different roles.

Here’s the simplified version: Data analysts are responsible for collecting, cleaning, analyzing and reporting data; meanwhile, data engineers create and maintain architectural systems for collecting, storing, analyzing and managing large quantities of raw data; and finally, data scientists handle data collection, analysis and visualization — and sometimes build things like machine learning models.

To write a stellar job description for data analysts — and attract top candidates — you’ll need to understand their role more specifically. That’s where this guide comes in.

Table of Contents

What Does a Data Analyst Do?

Types of data analyst jobs.

• Data Analyst Skills 

Data Analyst Salary Information

Data analyst job description template.

Access our entire library of templates for your open roles.

Data analysts gather data across a business, analyze it and translate the results into non-technical language for team members of all backgrounds.

Data analysts are typically early in their careers and may be seeking their first job after completing a bachelor’s degree or gaining the equivalent professional experience. Common degrees include statistics, math, computer science, physics, finance, business administration, economics or a related field.

What Is a Data Analyst?

Data analysts may be responsible for building data models to organize important data for different teams across the business and for monitoring and handling data. With large quantities of data comes endless possibilities for mistakes, requiring data analysts to constantly be on the lookout for information that needs cleansing and updating.

In addition to gathering, analyzing and cleansing information, data analysts create business reports for teams and individuals across the business. They also help translate analytics into non-technical insights to help all teams make well-informed decisions based on empirical evidence.

As they progress in their careers, data analysts may continue their education and become data engineers and eventually data scientists.

There are many different careers and jobs that data analysts can hold. Some of the most common fields for data analysts to work in include healthcare, big data , market research, operations and intelligence. 

Let’s take a closer look at a few different types of data analyst jobs and what they do. 

1. Business Intelligence Analyst

The primary job of a business intelligence analyst is to extract valuable insights from company data. Someone in this role should be comfortable with SQL, analyzing data, as well as creating data models.

2. Marketing Analyst 

Marketing analysts help their team track the success of campaigns by using Google Analytics, custom reporting tools or other traffic analytics sites to determine the impact advertisements are making. Marketing analysts are key to marketing departments as they help understand what efforts are working and what advertisements to spend company money on. 

3. Transportation Logistics Specialist

Transportation logistics specialists can utilize a data analytics background in a variety of ways. This role relies heavily on the ability to identify efficient delivery routes for products and services. Someone in this role uses large datasets to eliminate transit bottlenecks. 

4. Operations Analyst 

An operations analyst’s primary job is to organize a company’s internal processes. This role focuses on general operations as well as internal reporting and product manufacturing and distribution. Operations analysts can work for nearly every type of business, including supermarket chains, delivery providers or even government agencies. 

5. Healthcare Analyst

Healthcare data analysts collect, organize and interpret data from sources like electronic health records, billing claims, cost reports and surveys. The purpose of this role is to assist healthcare providers in order to improve the quality of care, lower costs and improve patient experiences. Someone in this field might have duties like automating internal and external reports, creating data dashboards or being responsible for presenting information to hospital executives. 

Related Reading Data Analyst vs. Data Scientist: Similarities and Differences Explained

Data Analyst Skills

Data analysts employ a variety of soft and technical skills throughout their careers. Like many positions, having clear communication skills and the ability to present complex information is crucial to this role. Critical thinking skills are an essential part of many jobs, and data analysts are no exception. These soft skills are especially important to data analysts because they are often responsible for presenting data to stakeholders and other teams in ways that everyone can understand. 

Along with communication and critical thinking skills, data analysts will need to understand different visualization tools, coding languages and mathematical principles.

Top Data Analyst Skills

• Data visualization
• Data cleaning
• Critical thinking
• Communication

Coding Languages

Mastering coding languages like R and Python is important as they are standard in the industry. These languages also provide advanced analyses and predictive analytics on large data sets. Some coding languages data analysts need to know are: 

Data Visualization

A key element of a data analyst’s job is data visualization. Data visualization allows analysts to identify patterns and showcase their findings to stakeholders and other teams. This skill is crucial in shaping company decisions and roadmaps. Some data visualization tools that data analysts use include: 

• Google Analytics & Google Tag Manager
• Microsoft Power BI

Data analysts rely on databases to store, maintain and organize data. There are several types of database languages that analysts may need to learn early on in their career. SQL , one of the first database languages created in 1970, is still a standard for querying and handling data today. Some common database languages for data analysis include: 

• Apache Cassandra

Data Warehouses

Data analysts use data warehouses to perform queries and analysis on historical data. The information contained in a data warehouse can include data such as application log files and transaction applications. These tools are useful to analysts because they consolidate large datasets from many sources. Often called a “single source of truth,” a data warehouse allows a company to improve decision making based on historical insights over time. Some types of data warehouses are: 

• Amazon Redshift
• Apache Hive
• Microsoft Azure SQL Database
• Oracle Database
• Oracle Warehouse Builder
• SAP NetWeaver Business Warehouse

Data Analyst Education Requirements

Although it may be possible to get a job in data analytics without a degree, having a bachelor’s degree can help candidates stand out and is often a requirement for many positions. Majoring in data analytics in an undergraduate program is a great place to start but not all universities offer this. Some alternative majors to look into include data science, computer science, applied mathematics or statistics. 

Whatever major you choose, taking courses on statistics, calculus and linear algebra will help you develop crucial skills for your career. Computer science courses with a focus on databases and statistical software will also provide a solid background to draw from. For those that have an idea of what field they’d like to work in, it’s always a good idea to take a course or two in a specific industry like healthcare or finance. 

Obtaining a master’s degree in analytics or a related field will open up more opportunities as well as senior positions. In fact, approximately 50 percent of professionals in the data science and analytics industry hold master’s degrees. Master’s degrees can help data analysts advance their visualization skills, understand how to use data in an ethical way and learn the best practices for data security. 

More on Job Descriptions How to Write a Job Description: Data Driven Results

To help determine what candidates expect, we’ve gathered average data analyst salary information from seven major hiring markets in the United States.

• Austin, TX: $78,469
• Boston, MA: $83,313
• Chicago, IL: $78,462
• Colorado: $77,359
• Los Angeles, CA: $89,517
• New York, NY: $86,392
• Seattle, WA: $83,224

Below are some resources to help you write a job description that will attract candidates with the skills needed to be successful in their role. It includes a data analyst job description template for you to alter and customize so that it includes the necessary responsibilities and requirements while reflecting your unique company culture. 

Company Bio

Use this section to provide a high level overview of your company, culture, perks and benefits, career development opportunities and anything else that will get candidates excited about your company.

Responsibilities

• Collaborate with various stakeholders and teams including product, engineering and finance.
• Provide teams and stakeholders with actionable insights and analysis reports based on data to support decision making efforts.
• Collect data from numerous data sources, clean data and analyze data to identify trends.
• Build and analyze automated dataset dashboards to predict issues before they arise, identify bugs in data and resolve them.
• Support individual team members by creating customizable tabular or visual reports with ad hoc reporting via SQL.
• Communicate and present technical information with non-technical team members and stakeholders.

Requirements

• Bachelor’s degree in computer science, mathematics, finance, economics, statistics or a related field.
• [X] years experience working in technical data analysis, data science, data warehousing in [insert industry] or a related industry.
• Experience with designing reports and dashboards on [insert tools].
• Experience with [insert relevant databases].
• Strong knowledge of [insert coding languages].
• Excellent communication skills including written, verbal and presentation.

Recent Job Descriptions Articles

Data Analyst 1 (1 Year Fixed Term)

🔍 school of medicine, stanford, california, united states.

The Department of Radiology at Stanford University is dedicated to excellence in clinical care, research, education, and administration. The people and programs comprising Stanford Radiology are world-renowned. Stanford Radiology’s core strength lies in its people: faculty, who are highly regarded for their deep subspecialty expertise, dedication to patient care, and responsiveness to referring providers; multidisciplinary researchers who continue to push the boundaries of innovation in physics and engineering to develop cutting-edge methods for enhanced anatomic and functional imaging; staff who are dedicated and engaged in moving the mission of the department, school, and university forward.

The Section of Neuroradiology is seeking a full-time data analyst 1 in the Zaharchuk lab which focuses on developing novel Magnetic Resonance Imaging (MRI) techniques to better understand human brain functions, delineate brain structures, and diagnose brain diseases. The data analyst will be involved in defined research projects and will be responsible for independently conducting and analyzing experiments related to neuroimaging technology to neurosciences problems, and independently carrying out analysis to completion.  The data analyst is expected to interpret and analyze the results and suggest modifications to procedures as appropriate. 

Duties include*:

• Identify and select usable data from subtle and complex data patterns. Assess and produce relevant, standard, or custom information (reports, charts, graphs and tables) from structured data sources by querying data repositories and generating the associated information. 
• Design methods to validate data to ensure high quality product. Explore creative approach to using data based on technical expertise of available data. Distribute reports to applicable agencies, researchers, management and other internal end-users and provide interpretation of data when needed.  
• Develop and produce dashboards, key performance indicators, and other recognized metrics used to monitor and report organizational performance. Collect and analyze metric data.
• Collect, manage and clean datasets using an extraction and reporting programming language to ensure data integrity. 
• Research and reconcile data discrepancies occurring among various information systems and reports.
• Collaborate with data managers to define and implement data standards and common data elements for data collection.
• Identify new sources of data and methods to improve data collection, analysis and reporting.
• May test prototype software and participate in approval and release process for new software.

* - Other duties may also be assigned.

DESIRED QUALIFICATIONS:

• Image interpretation and manipulation using OSIRIX on Macintosh.
• Ability to use image processing softwares, including Matlab, ImageJ, FSL, SPM, ITKsnap.
• Ability to develop Linux-based scripts, program in Python, use of GitHub.
• Experience with medical imaging datasets.
• Experience with basic statistics for health sciences.
• Experience with AI software frameworks, including TensorFlow, pyTorch, and Keras.

EDUCATION & EXPERIENCE (REQUIRED):

Bachelor's degree or a combination of education and relevant experience. Experience in a quantitative discipline such as economics, finance, statistics or engineering.

KNOWLEDGE, SKILLS AND ABILITIES (REQUIRED):

• Basic knowledge and demonstrated experience using and applying analytical software, database management system software, database reporting software, database user interface and query software, and data mining software.
• Ability to collect data using a variety of methods, such as data mining and hardcopy or electronic documentation study, to improve or expand databases.
• Strong listening, verbal and written communication skills.
• Ability to manage multiple activities in a deadline-oriented environment; highly organized, flexible and rigorous attention to detail.
• Ability to use logic to calculate data; efficiently construct a database or scrutinize the form of a question.
• Ability to work with data of varying levels of quality and validity.
• Demonstrated ability to produce data in a clear and understandable manner meeting user requirements. 
• Ability to work effectively with multiple internal and external customers.

PHYSICAL REQUIREMENTS*:

• Constantly perform desk-based computer tasks.
• Frequently sit, sort, file paperwork or parts, grasp lightly, and use fine manipulation, lift, carry, push and pull objects that weigh 10 pounds or less.
• Occasionally write by hand, twist, bend, stoop and squat.
• Rarely stand, walk, reach or work above shoulders and use a telephone.

* - Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of his or her job.

WORKING CONDITIONS:

May work extended hours during peak business cycles.

WORK STANDARDS:

• Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
• Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned.
• Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University's Administrative Guide, http://adminguide.stanford.edu .

The expected pay range for this position is $75,000.00 to $97,000.00 per annum. 

Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs.

At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The Cardinal at Work website ( https://cardinalatwork.stanford.edu/benefits-rewards ) provides detailed information on Stanford’s extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process.

Consistent with its obligations under the law, the University will provide reasonable accommodations to applicants and employees with disabilities. Applicants requiring a reasonable accommodation for any part of the application or hiring process should contact Stanford University Human Resources by submitting a contact form . 

Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.

The job duties listed are typical examples of work performed by positions in this job classification and are not designed to contain or be interpreted as a comprehensive inventory of all duties, tasks, and responsibilities. Specific duties and responsibilities may vary depending on department or program needs without changing the general nature and scope of the job or level of responsibility. Employees may also perform other duties as assigned.

• Schedule: Full-time
• Job Code: 4744
• Employee Status: Fixed-Term
• Requisition ID: 103276
• Work Arrangement : On Site

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Work From Home as a Data Analyst and Earn $76,000 Annually

Charlotte Chaze went from a $28k salary as an academic researcher to a $158k salary as a data analyst in less than four years.

As one of TikTok’s leading tech influencers, her entire TikTok is full of free information detailing how to get started as a data analyst. She's also created a free data analytics course for anyone who needs minimal guidance to get started on their own.

Before I learned data analytics online, I had no idea where to start. I wasn’t sure I’d be capable, and I didn’t know if it would lead anywhere. All I knew was that I needed to leave my job doing academic research for something that would pay more with a better work/life balance and less stress. It took me longer to change careers than it should have because I didn’t know where to find the right resources. Now that I know exactly what it takes, I’m sharing it with anyone else who wants to do what I did!

Tell Us How You Became a Data Analyst.

I was working full-time as a research assistant and wasn’t happy with my salary or my lack of time off, and I was just burnt out. When I started looking on job boards to find other opportunities, data analytics popped up everywhere, and I noticed the pay was awesome ($75k average at entry level!). That was the motivation I needed to teach myself data analytics online. I used free resources to learn data analytics from scratch and then started interviewing. After just a few interviews, I landed my first data analyst job! 

How Did You Get Involved in Teaching Others How to Break Into Data Analytics? 

I’ve always been passionate about empowering others, but this career aspect of it started when I helped my best friend land her first tech job. She was working at a government contracting company and was super underpaid, but she didn’t feel like she could ask for more. I helped her feel like she deserves a great salary and that she’s capable of getting one. This helped me realize that the biggest blocker that people have in terms of getting a better career is that they don’t believe in themselves. That’s why I started posting motivational videos on TikTok , and that’s what eventually led me to create a free data analytics course.

What Do People Learn In Your Free Course?

In my free data analytics course , I give out all the information you need to learn data analytics from scratch and get your first job in the field without spending a cent. So, the free course starts with links to all the best free resources across the internet and my recommendations on the best order to work through them so you can learn data analytics on your own.

Then, I explain how to create a portfolio with real projects in it, and I include my resume template and LinkedIn tips to help you land a job faster. The idea is to help people make a complete transition from their current job into a real tech career completely for free. 

What are Some of the Benefits of Being a Data Analyst?

My absolute favorite thing about data analytics is that it’s a job that can be done from anywhere with an internet connection, so many jobs are fully remote or hybrid.

According to Glassdoor , the entry-level salary is shockingly high at $76k and goes up from there, so you can hit six figures after only a year or two of experience.

According to the World Economic Forum , data analytics is also the #1 job with the most increasing demand in the entire world, so there are always lots of job openings, and that number will continue to go up.

I can’t think of a better career path, especially considering you don’t need a degree or previous experience to get your first job - you just need to learn the skills. 

What Personalities What Good Data Analysts? 

Data analytics is the kind of job anyone can do, so any personality type can be successful at it. If you want to use your personality type in choosing a job, I recommend choosing the company you want to do data analytics for based on your personality type. For example, let’s look at the four Myers-Briggs groups: Analysts, Explorers, Sentinels, and Diplomats. A true “Analyst” personality type may want to work at an analytics, consulting, or engineering company; an “Explorer” could look into startups; a “Sentinel” would excel at any large Fortune 500 company; and a “Diplomat” could consider non-profits or companies that focus on a cause. 

That said, I don’t believe work is that deep. It’s just an exchange of your time for money, so let your personality type shine outside of work and just use work to get paid as much as possible. It’s business, it’s not personal.

How Much Can Data Analysts Earn?

According to Glassdoor.com , the average entry-level salary for data analysts is $76k, and as you gain experience, you can break six figures pretty easily. For example, after just one promotion to a Senior Data Analyst, the average salary hits $112k - and that’s just the base salary. Most data analysts also receive yearly bonuses, 401k matches, and stock options. 

How Can Beginners Land Their First Job? 

The key for beginners to land their first data analytics job is to have a portfolio. That’s why my free course includes instructions on how to do real data analytics projects and put them into a portfolio. Recruiters and hiring managers don’t mind if you have no previous paid experience if you can show that you’re able to do the job. That’s why a portfolio is so important at entry level; it proves that you know what you’re doing and are ready to be paid for your skills. 

Is There Anything Else You Would Like Readers to Know About Becoming a Data Analyst?

The biggest secret to switching careers is to fake it until you make it. You can’t fake the skills - you’ll need to really learn those, and you can do that for free online. But you  do  need to fake your confidence in those skills! You won’t get hired if you aren’t confident in yourself; recruiters and hiring managers can feel that come across during interviews. You should be confidently stating that you can do this job and that you want this job. If you have a portfolio, you’re ready for the job. If you’re willing to ask questions and use Google to figure out anything you don’t already know, you’re ready for the job. With confidence, fake it until you make it! 

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Research Data Analyst

Specific Duties & Responsibilities

• To perform mapping and data manipulation/integration in ArcGIS Pro.
• To perform statistical and spatial statistical analyses in R.
• Work with other principal investigators to understand their spatial analysis needs.
• Engage in manuscript publications (preparation and writing).
• Support various projects for the Johns Hopkins Spatial Science for Public Health Center.
• Bachelor's Degree in related discipline.
• Three years related experience.
• Additional education may substitute for required experience to the extent permitted by the JHU equivalency formula.
• Demonstrated experience in spatial statistical analysis in R including geostatistics, point pattern analyses and area-level analysis.
• Experience in cluster detection analysis via the SaTScan software.

Classified Title: Research Data Analyst Role/Level/Range: ACRP/04/MC Starting Salary Range: $47,500 - $83,300 Annually Employee group: Full Time Schedule: Monday to Friday: 9 am – 5 pm Exempt Status: Exempt Location: Hybrid/School of Public Health Department name: ​​​​​​​Epidemiology Personnel area: School of Public Health

Total Rewards The referenced salary range is based on Johns Hopkins University’s good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level. Johns Hopkins offers a total rewards package that supports our employees' health, life, career and retirement. More information can be found here: https://hr.jhu.edu/benefits-worklife/ .

Please refer to the job description above to see which forms of equivalency are permitted for this position. If permitted, equivalencies will follow these guidelines: JHU Equivalency Formula: 30 undergraduate degree credits (semester hours) or 18 graduate degree credits may substitute for one year of experience. Additional related experience may substitute for required education on the same basis. For jobs where equivalency is permitted, up to two years of non-related college course work may be applied towards the total minimum education/experience required for the respective job.

**Applicants who do not meet the posted requirements but are completing their final academic semester/quarter will be considered eligible for employment and may be asked to provide additional information confirming their academic completion date.

The successful candidate(s) for this position will be subject to a pre-employment background check. Johns Hopkins is committed to hiring individuals with a justice-involved background, consistent with applicable policies and current practice. A prior criminal history does not automatically preclude candidates from employment at Johns Hopkins University. In accordance with applicable law, the university will review, on an individual basis, the date of a candidate's conviction, the nature of the conviction and how the conviction relates to an essential job-related qualification or function.

The Johns Hopkins University values diversity, equity and inclusion and advances these through our key strategic framework, the JHU Roadmap on Diversity and Inclusion.

Equal Opportunity Employer All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

EEO is the Law: https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf

Accommodation Information If you are interested in applying for employment with The Johns Hopkins University and require special assistance or accommodation during any part of the pre-employment process, please contact the Talent Acquisition Office at [email protected] . For TTY users, call via Maryland Relay or dial 711. For more information about workplace accommodations or accessibility at Johns Hopkins University, please visit https://accessibility.jhu.edu/ .

Johns Hopkins has mandated COVID-19 and influenza vaccines, as applicable. The COVID-19 vaccine does not apply to positions located in the State of Florida. Exceptions to the COVID and flu vaccine requirements may be provided to individuals for religious beliefs or medical reasons. Requests for an exception must be submitted to the JHU vaccination registry. For additional information, applicants for SOM positions should visit https://www.hopkinsmedicine.org/coronavirus/covid-19-vaccine/ and all other JHU applicants should visit https://covidinfo.jhu.edu/health-safety/covid-vaccination-information/ .

The following additional provisions may apply, depending upon campus. Your recruiter will advise accordingly.

The pre-employment physical for positions in clinical areas, laboratories, working with research subjects, or involving community contact requires documentation of immune status against Rubella (German measles), Rubeola (Measles), Mumps, Varicella (chickenpox), Hepatitis B and documentation of having received the Tdap (Tetanus, diphtheria, pertussis) vaccination. This may include documentation of having two (2) MMR vaccines; two (2) Varicella vaccines; or antibody status to these diseases from laboratory testing. Blood tests for immunities to these diseases are ordinarily included in the pre-employment physical exam except for those employees who provide results of blood tests or immunization documentation from their own health care providers. Any vaccinations required for these diseases will be given at no cost in our Occupational Health office.

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Business & Data Analyst

C.H. Robinson is seeking a Business & Data Analyst that will provide direct support and analysis for our Inland Services. You will help determine key performance indicators for the business and help create dashboard reporting for management to monitor performance and trends. You'll be expected to establish and maintain strong relationships with the leadership team to ensure timely resolution of issues and concerns as well as to increase content knowledge in areas of evolving focus. This role requires an organized individual with strong project management disciplines that can manage their workload and prioritize tasks in a fast-paced work environment.

This position offers the flexibility of remote work, allowing you to contribute your expertise and thrive from anywhere in Canada.

Responsibilities:

• Data profiling, exploratory work, prototyping, visual development, initial data structuring
• Adapt applications, reporting & analysis based on feedback
• Data asset buildout, validation, and rework 
• Update and maintain standard library of reporting & analysis for current and future business application
• Using agile processes, takes in reviewed work through work management tools such as Azure DevOps (ADO) boards
• Evaluates level of effectiveness of business decisions and current processes, specifically in the leads and small business functions of the business, through critical analysis of internal data and reporting
• Proposes and implements enhancements based on data findings and continues to monitor success levels to drive growth and improvements in profitability
• Develop innovative solutions and insights from data that will allow C.H. Robinson to improve supply chain services
• Advocate and build data-fueled products that help our global customers and partners improve business outcomes
• Partners with internal departments to better understand drivers of trends and impact of business changes
• Document, and maintain project related plans, process flows, and metrics
• Communicate project progress to team members and leaders
• Works with a limited degree of supervision, with oversight focused only on complex new assignments
• Engage with the commercial owner on identifying and prioritizing areas of analysis and opportunity globally
• May need to prepare and present to various stakeholders with updates on trends that could impact the company's growth
• Demonstrate intermediate knowledge on existing reports, and helps to develop additional reports using business knowledge
• Promote and lead a positive working relationship for all team members

Required Qualifications:

• High School Diploma or GED
• Minimum of 2 years’ experience in a financial or business analyst capacity 
• Advanced proficiency in Excel and PowerPoint
• Experience with data visualization tools, including Power BI, Tableau, and SQL querying
• Expert knowledge of data visualization, DAX and M/Power Query
• Familiarity with Python, R, or similar programming languages

Preferred Qualifications:

• Bachelor’s degree from an accredited college or university
• Logistics and supply-chain experience
• Experience with data/financial analysis for a sales or operations organization
• Positive, collaborative working style
• Strong written and verbal communication skills
• Demonstrated ability to effectively present, influence, and consult with multiple stakeholders at a variety of levels
• Able to make recommendations, articulate analysis and field questions and/or pushback from key internal and external stakeholders
• Proven success in developing and communicating business recommendations and insights in an easy-to-understand way, leveraging data to tell a story
• Embraces entrepreneurial spirit in work through recognizing and capitalizing on opportunities and challenging the status quo
• Values a diverse and inclusive work environment

Questioning if you meet the mark? Studies have shown that women, people of color, and individuals with disabilities may be less likely to apply unless they match the job description exactly. Here at C.H. Robinson, we’re building a diverse and inclusive workplace where all employees feel they belong. If this position excites you, we welcome you to apply whether you check all the preferred qualifications or just a few. You may just be our next great fit!

Your Health, Wealth and Self

Your total wellbeing is the foundation of our business, and our benefits support your financial, family and personal goals. We provide the top-tier benefits that matter to you most, including:

Medical (including Vision and Telemedicine)

Basic and Supplemental Life Insurance

Vacation and PTO time

Paid holidays

Short-Term and Long-Term Disability

Retirement Plan with 5% company matching

Employee Stock Purchase Plan (ESPP)

Charitable Giving Match Program

Plus a broad range of career development, networking, and team-building opportunities

Dig in to our full list of benefits on OUR CULTURE page.

Why Do You Belong at C.H. Robinson?

C.H. Robinson solves logistics problems for companies across the globe and across industries, from the simple to the most complex. With $22 billion in freight under management and 19 million shipments annually, we are one of the world’s largest logistics platforms and rank in the FORTUNE 200. We’ve been an innovator in logistics for over 100 years. Our global suite of services accelerates trade to seamlessly deliver the products and goods that drive the world’s economy. With the combination of our multimodal transportation management system and expertise, we use our information advantage to deliver smarter solutions for more than 90,000 customers and 450,000 contract carriers on our platform. Our technology is built by and for supply chain experts to bring faster, more meaningful improvements to our customers’ businesses.

As a responsible global citizen, we contribute millions of dollars to support causes that matter to us and our people. FORTUNE has named C.H. Robinson one of the World’s Most Admired Companies 2024, showcasing our position as a leader in our industry. Our commitment to excellence is further affirmed by being named a Great Place to Work 2023-24 by the Great Place to Work Institute, one of Forbes’ Best Employers for Diversity and one of America’s Greatest Workplaces in 2023 by Newsweek. Join us as we collaborate, innovate, and work as one global team to make life better and more sustainable for our customers, communities, and world. For more information, visit us at www.chrobinson.com.

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What Does a Data Analyst Do? 2024 Career Guide

There’s no escaping it: data analytics is one of the hottest jobs of the 21st century! But what exactly is data analytics, and what does a data analyst do, actually?

There’s no end of discussion and commentary about data analytics online. However, it’s not always easy to find a simple description of what a data analyst does on a day-to-day basis. This is made even harder by the fact that data analytics is often mixed in with related fields like data science, machine learning, artificial intelligence, and business analytics. While data analytics plays a key role in all these fields, it’s a distinct discipline in its own right.

Want to dip your toes in this field? This free 5-day data analytics short course is a great start!

In this article, we offer a clear, career-focused introduction to data analytics. We’ll cover all the need-to-know knowledge without the fuss, answering:

• What is data analytics?
• What does a data analyst do?
• Data analyst vs. data scientist: what’s the difference?
• What types of data analysts are there?
• What tasks and processes does a data analyst follow?
• What skills does a data analyst need?
• What tools do data analysts use?
• How much do data analysts earn?
• Wrap-up and further reading

So, what does a data analyst do? Let’s find out.

1. What is data analytics?

Before diving into what a data analyst does, it’s necessary to answer: what is data analytics? And why is it important? Watch this video for an introduction to the field, or keep reading!

In its simplest form, data analytics is the process of drawing meaning from disordered information. By systematically exploring data for patterns and relationships, data analysts seek to find and communicate useful insights using those data. But what counts as data? Well, pretty much anything you can imagine. Often, data are numerical (quantitative data). But sounds, images, words, or anything else that can be interpreted in some way can also be classed as data (qualitative data).

An analyst’s job begins with what’s known as ‘raw data.’ Raw data are disordered and—without context—essentially meaningless. We can only obtain useful information from them once we have brought order to chaos. As such, collecting, cleaning, and organizing data are all parts of the data analytics process.

What’s more, effective data analytics incorporates many techniques to help the process along. These include statistics, programming, visualization, and more. Luckily, to streamline the process, many of these techniques have been automated. Some are even developing as fields in their own right. However, a good data analyst will have at least some knowledge of them all.

Why does data analytics matter?

There are two simple reasons why data analytics matters. Firstly, it’s useful for decision-making. Secondly, it’s evidence-based. Combine these two attributes, and data analytics becomes a potent tool. Basing decisions on empirical information (rather than relying on opinion or ‘gut feel’) is a much more scientific way of approaching problems. While this does not mean data analytics is always 100% accurate, it’s by far the best tool we have for predicting future trends and drawing conclusions about past events.

Data analytics also has a wide range of applications across society. Online, you’ll often find data analytics touted as a tool for business intelligence, e.g. predicting future sales or informing product development and marketing spend.

2. What does a data analyst do?

Now we know what data analytics is , let’s take a look at what the role of the data analyst actually entails.

As a data analyst, it’s your responsibility to turn raw data into meaningful insights. Following the data analysis process (which we’ll cover in the next section), you’ll solve specific problems or answer certain questions based on data and the insights it provides.

You’ll then take these insights and share them with key stakeholders and decision makers, who can take action or plan for the future accordingly. At the same time, data analysts may be responsible for overseeing the overall processes for collecting and storing data, as well as setting guidelines for data quality .

A great way to gauge what a data analyst actually does on a day-to-day basis is to look at the tasks and responsibilities that are typically listed in data analyst job descriptions . Based on actual job descriptions posted on indeed.com , here’s what you can expect to do as a data analyst:

• Develop and implement databases and data collection systems
• Work closely with management to identify critical metrics and KPIs, and to prioritize business needs
• Collect data from primary and / or secondary data sources
• Filter and clean data
• Identify, analyze, and interpret trends and patterns in complex data sets
• Visualize and present findings to key stakeholders
• Build and customize reports
• Develop and maintain dashboards
• Create and maintain documentation regarding data models, measures, and infrastructure as they are developed

So far, we’ve taken a rather high-level look at the work of a data analyst. Next, we’ll look at the difference in the job titles of  data analyst and  data scientist .

3. Data analyst vs. data scientist: What’s the difference?

So, you may have already done a bit of research into the role of the data analyst and come across some content which talks about data science. Despite the fact that these two terms are often used interchangeably, they are in fact two separate career paths, serving different purposes—and requiring a different skillset.

As we’ve already covered, data analysts use a company’s data and interpret it for those in charge of making business decisions. Their work is focused on answering questions and developing solutions by looking into data patterns and turning those into dashboards and visualizations for broader use.

In turn, a data scientist will work deeper within the data, identifying patterns using data mining and machine learning. They will set up experiments, then produce models and tests in order to prove or disprove their findings. Then, based on their findings, they’ll offer solutions as to how a company should act going forward.

In short: data analysts analyze the past, while data scientists are more concerned with the future. To look into this topic in more detail, check out this article: What’s The Difference Between A Data Scientist And A Data Analyst?

4. What types of data analysts are there?

As you might have been able to glean so far, the practice of data analysis has an important function with applications across many industries.

However, data analytics goes far beyond simply boosting a company’s bottom line. It’s also used in health settings to improve patient care . It’s currently being applied in agriculture to transform the way we feed the world. It’s even used by governments to tackle issues like human trafficking . So if you want to help improve the world—as well as business—a career in data analytics might be for you!

With regards to types of data analysts and job titles, here are some of the common titles you may see on job advertisements:

• Business analyst
• Business intelligence analyst
• Business systems analyst
• Medical and healthcare analyst
• Market research analyst
• Operations analyst
• Intelligence analyst

Now let’s zoom in on some of the more specific tasks associated with the data analysis process.

5. What tasks and processes does a data analyst follow?

As a data analyst, your job is to carry out each step of the data analytics process to identify and solve a problem.

As your career progresses, you may choose to specialize in a particular area, such as data visualization or data engineering. As a beginner, though, it’s important to learn the process as a whole.

So, what are the key tasks and processes that a data analyst should expect to follow? Here’s a brief video overview, from my colleague Will:

Although it’s not as straightforward as following one task directly after another (you may find yourself repeating steps, going back on yourself, and so on) the main tasks include:

Defining a question

Collecting data, data cleaning, conducting an analysis, communicating your results.

First up, you need to define your objective. In some ways, this is the hardest part of the process. This is because what seems like an obvious problem may not always get to the core of an issue.

For example, let’s say you work for a company that wants to boost its revenue. The senior management is set on doing this by launching a suite of new products. As a result, you spend lots of time and resources analyzing what products to create, which market to launch them in, and so on.

However, with a bit more probing upfront, you might discover that there’s nothing wrong with the company’s existing products: it’s simply that the sales process is poor, resulting in low customer satisfaction and less repeat business. With this insight, you might find that investing in sales training will boost revenue at a much lower cost.

While this is just a hypothetical case, it illustrates the importance of probing an issue from multiple angles before investing too much time in it. It also means not being afraid to speak truth to power (in this case, telling managers that their new product idea is wrong). Defining the question you want to answer involves obtaining a deep understanding of the needs and demands of the business, keeping track of metrics, KPIs, and so on. You’ll usually carry out some initial analyses at this stage, too.

Once you’ve identified the question, your next task is to figure out which data are best-suited to help you solve it. This can be quantitative data (such as marketing figures) or qualitative data (such as customer reviews). More specifically, data types can be divided into three categories: first-party data (collected directly by you or your organization), second-party data (the first-party data of another organization), and third-party data (which is aggregated from numerous sources by a third-party).

If you don’t already have access to these data, you’ll have to devise a strategy for collecting them. This might include carrying out surveys, social media monitoring, website analytics, online tracking, and so on. However you collect it, once you have the data at your fingertips, you’re ready to clean it.

Freshly collected data will usually be in a raw format. This means that it hasn’t yet been organized, checked for errors, and so on. To get it into a state that’s suitable for analysis, the data need cleaning. This involves a variety of tools and techniques (such as custom algorithms, generic software, and exploratory analyses) to get it into a more suitable state.

Data cleaning tasks include removing errors, duplicates, and outliers, eradicating unwanted data (i.e. those that don’t serve your analysis), structuring the data in a more useful way, filling in gaps, and so on. When this is done, you’ll validate the data. This involves checking that it meets your requirements. Often, you’ll find it doesn’t, which means you’ll have to go back a step.

For this reason, data cleaning is considered an iterative process. The combined process of collecting and cleaning data is sometimes referred to as data wrangling . You can learn more about data cleaning in this guide .

Once your dataset is clean and tidy, you are good to analyze! There are a great many types of data analysis , and part of the challenge is identifying which approach is best-suited to the task at hand. To keep things simple, we’ll offer a quick overview of the four main categories of data analytics.

The first is descriptive analytics. This involves summarizing (or describing) the features of a dataset to better understand it. It isn’t usually used to draw firm conclusions, but it’s a useful first step for deciding how to investigate the data further.

Next, diagnostic analytics focuses on understanding why something has happened (e.g. by exploring correlations between values in a dataset). This helps identify problems and is often used in the first stage of data analytics, i.e. defining the question.

Finally, we have predictive analysis (which helps to identify trends based on past data) and prescriptive analytics (which helps decide on a future course of action). The latter is sometimes carried out using machine learning techniques.

Once you’ve carried out an analysis and drawn some insights, the final step is to communicate these to those who commissioned them in the first place. This usually involves visualizing your data in some way—creating graphs and charts, for example.

It may also involve creating interactive dashboards, documents, reports, or presentations. It’s easy to overlook the artistry of this step, but it’s very important to get it right. Not only must you interpret your findings correctly, but you need to share them in a way that is clear for time-short, non-technical personnel. This is important as it ensures any decision-making is based on high-quality, well-understood insights.

6. What skills does a data analyst need?

In some ways, the skills a data analyst needs vary depending on their role. For instance, knowledge of the business you’re working in is very important. However, as a rule, this is something you can learn on the job.

Before nabbing that first opportunity, though, there’s a core set of skills that all beginner data analysts need. We can divide these into hard skills (or technical abilities) and soft skills (or useful personality traits that help you get the job done).

Technical skills for data analysts

Hard skills sometimes have a steep learning curve. However, with a little discipline, anyone can pick them up. Key hard skills for data analysts include:

• Math and statistics: You’ll be mathematically minded. You may have an undergraduate or Master’s degree in an area like applied math, statistics, or computing. However, while qualifications can be useful, they’re not always necessary if you’re a newcomer to the field. As long as you have solid math skills, e.g. algebra and calculus, that could be sufficient.
• Programming skills: To create or tweak algorithms that automate data analytics tasks (like parsing or re-structuring large datasets) an element of programming know-how is unavoidable. Scripting languages like Python or MATLAB and statistical computing languages like R and SAS are all popular in data analytics.
• Database knowledge: As well as programming languages, you’ll need some understanding of database warehousing software, e.g. Hive, and analytics engines like Spark. You’ll also need to know database query languages like SQL.
• Excel skills: Commonly used for transforming raw data into a readable format, or for automating complex calculations, MS Excel is core to any data analyst’s toolset. Be sure to familiarize yourself with its key analytical functions .
• Visualization skills: A core aspect of data analytics is the ability to visualize data with charts and graphs. This helps us identify patterns, correlations, and trends. At the very least, you should be able to create plots using Python, or tables and charts using MS Excel.
• Basic machine learning knowledge: As a beginner, nobody will expect you to be an expert in machine learning—it’s an entire discipline in its own right. Nevertheless, the tenets of machine learning underpin many data analytics tasks. You should be familiar with the theory, e.g. supervised learning versus unsupervised learning.

Non-technical skills for data analysts

While soft skills can be honed with practice, they are generally considered more inherent. You’ll need to have a natural flair for the following:

• Communication: Communication is key in any job, but especially in data analytics. Obtaining accurate insights is the priority, but effectively communicating these to wider audiences is vital. You should have excellent interpersonal skills, be able to communicate complex concepts in straightforward terms, and be confident giving presentations and answering questions for non-technical personnel.
• Critical thinking: Arguably the most important skill in data analytics, critical thinking is the ability to question what’s in front of you to better understand it. You’ll have a naturally inquisitive mindset, won’t take anything at face value, and will approach tasks using logical reasoning and deduction.
• Creative problem-solving: Problem-solving involves applying your reflective way of seeing the world to specific data-related situations or problems. You’ll take a step-by-step approach when defining a problem, devise an approach for solving it, and carry out the necessary subsequent tasks. These tasks will be different every time, so you’ll need a creative mindset.
• Ethics: You’ll understand the importance of data privacy, be aware of your personal biases, and be comfortable presenting outcomes—even when these are undesirable or are unlikely to win you any praise. Adhering to a strong ethical code is hugely important. Without it, data can be easily misused, which can have a real-world impact on individuals and groups affected by your work.

If you’re dipping a toe into data analytics for the first time, ask yourself: do these skills describe you? If not, don’t worry. While it’s important to appraise your strengths and weaknesses honestly, the most important thing is to be enthusiastic about the field and willing to develop the necessary skills. Nobody hiring a beginner will expect you to be an expert right away.

7. What tools do data analysts use?

So far, we’ve covered the skills a data analyst needs and the high-level process and tasks they need to carry out. As a beginner, this may feel a bit overwhelming. Fortunately, there’s a huge range of applications and software to help streamline the process. While these require a bit of technical know-how, once you’ve covered the basics, you should find the whole process a lot easier.

Common tools for data analysts include:

• Databases and management systems

Let’s take a closer look at some of those now.

MS Excel for data analytics

A must-have for any data analyst is MS Excel. Excel allows you to sort data, break it into smaller subsets, and use a wide variety of functions to understand it better.

These functions include pivot tables , search functions like XLOOKUP and VLOOKUP , the AVERAGE function (which gives you the average of a given range of numbers), and the SUMIF function (which lets you calculate the sum of different cells). These tools, along with a great many more, make Excel an invaluable piece of software for beginners and experts alike.

Python for data analytics

The general-purpose programming language, Python, has fast become the go-to programming tool for data analysts. This is partly because of its simple syntax, which makes it quick and easy to learn. However, its popularity is also down to the fact that the Python Package Index (PyPI) offers a massive range of software libraries.

Python can be used for almost any aspect of the data analytics process. For instance, Pandas is excellent for manipulating time-series and other quantitative data. Matplotlib is perfect for data visualization. And NumPy is popular for conducting a range of complex mathematical functions. These are just three of the many thousands of Python packages that are available.

R for data analytics

R, another programming language, is also common in data analytics.

While R is generally considered more complex to learn than Python, it remains popular due to its historical use in statistical programming (which has benefits in a field like data analytics). While R doesn’t carry out things like image processing with the ease of Python, it has more data analytics functions built in. It’s also often used in scientific fields. Like Python, R also has a library of software, CRAN , with many additional packages available.

Databases and data management systems

As the variety of data we collect becomes more complex, the way we store and manage these data is also evolving. In data analytics, it’s vital to have an understanding of how databases and data warehouses work. For instance, MySQL is a relatively simple type of relational database management system that is commonly used.

Apache Hadoop , meanwhile, is a more complex framework, used to store, manage and process big data using distributed databases. Whether you’re using simple databases or complex infrastructures, they are ultimately unavoidable!

Structured Query Language (SQL)

SQL (sometimes pronounced ‘sequel’) is a programming language designed to communicate with relational databases. In a world where data is the main currency, this has obvious applications. While relational databases are built using a variety of languages, such as C or C++, SQL allows you to pull, add or edit data without needing knowledge of the database’s native language.

Since most organizations now have information stored digitally or online, SQL is becoming an important language to learn, even for non-analysts. It’s a must-have for those in the field.

Industry-specific data analytics tools

In addition to the tools already described, the industry is starting to produce ever-more sophisticated sector-specific applications to support data analytics. These tools range from general business intelligence software like Microsoft Power BI , to data visualization and dashboarding applications like Tableau .

They also include niche products that you’ll only be likely to learn if you work in a specific industry. For instance, Definitive Healthcare is an analytics platform designed specifically to manage tasks relating to health data.

8. How much do data analysts earn?

So, what kind of salary can you expect to get as a data analyst, then? Unfortunately, that’s not a question that’s easy to answer, as salaries will be dependent on job location, experience, and likely industry, too.

To give an example of what a data analyst can expect to earn, Payscale  gives a good indication of estimated salaries across the board.

Data analyst salary based on experience in the U.S.

Entry level  (<1 years experience): $60,642 Early career  (1-4 years experience): $65,400 Mid-career  (5-9 years experience): $73,424 Experienced  (10-20 years experience): $74,329 Late career  (20+ years experience): $77,085

To learn more, we’ve got an in-depth guide that covers the average salaries across these criteria in our guide .

9. Wrap-up and further reading

In this post, we’ve covered everything you need to know if you’re just starting in data analytics. We’ve explored what a data analyst does, what skills they need, and the basic tools that a beginner analyst should aim to learn.

Once you have all these skills at your fingertips, you’ll soon be ready to enter the field. Whether you’re interested in data analytics for e-commerce, finance, healthcare, government, the sciences, or any other area of your choosing, one of the great benefits of the field is its versatility. With a little experience under your belt, you can branch into broader data science , or specialize in areas like data engineering, data modeling, or machine learning.

For a deeper taste of what data analytics involves, try our free, 5-day data analytics short course . Want to learn more about a career in data? Take a look at the following:

• Am I a good fit for a career as a data analyst?
• What’s the typical data analyst career path?
• A guide to the best data analytics certification programs