How do you become a data analyst without any experience?‎

If you are just starting out in data analytics, there are several proactive steps you can take to get into the career. Some concrete steps you can take to improve your chances of landing an entry-level data analyst job include: Obtain a credential through an educational program, such as a degree or professional certificate. Work on developing your technical skills, either through in-person or online instruction. Create a portfolio consisting of either self-directed or group projects. Gain experience through an internship or volunteer opportunity.

Do you have to be good at math to do data analysis?‎

Yes and no. While data analysts should have a foundational knowledge of statistics and mathematics, much of their work can be done without complex mathematics. Generally, though, data analysts should have a grasp of statistics, linear algebra, and calculus.

What workplace skills do you need to have in data analysis?‎

Workplace skills (also called “soft” skills or people skills) are all the intrinsic skills you use to do your job well. While data analysts are prized for their technical skills, you should also strive to hone your workplace skills in order to do your job well. Some of these skills include: Problem-solving: Data analysts must be adept problem solvers, capable of identifying strategies for finding the answers to the questions that they ask. Collaboration: Data analysts must often work with others to solve problems and ensure that their objectives are achieved. As a result, collaboration is a key skill that data analysts use every day. Storytelling and communication: While data analysts spend their time looking at data to glean useful insights, they must also communicate those insights to others. One of the most effective ways to communicate to non-experts is by using storytelling to convey just why your data insights are important and what they mean to others.

7 In-Demand Data Analyst Skills to Get You Hired in 2026

Written by Coursera Staff • Updated on Feb 6, 2026

Transitioning to a career in data analytics can mean stable employment in a high-paying industry once you have the right skills.

[Featured image] Two people sit in a living room, one working on data analysis on their laptop

Key takeaways

Building and strengthening your analytical skills is important as you set out on your career and later progress in it as a successful data analyst.

The US Bureau of Labor Statistics projects that job openings for data professionals will grow by 34 percent between 2024 and 2034, indicating demand for skilled data analysts [1].

In-demand data analyst skills include SQL, statistical programming, machine learning, probability and statistics, data management, statistical visualization, and econometrics.

You can improve your data analysis skills by regularly working on them, learning from mistakes, practicing with real data projects, joining an online community, and building your skills incrementally.

Explore seven data analyst skills and tips on how you can build them. If you're ready to build the skills needed for a data analyst role, consider enrolling in the Meta Data Analyst Professional Certificate. You’ll have the opportunity to learn key data analyst skills, including data visualization, descriptive statistics, and data analysis, in as little as five months. Upon completion, you’ll have earned a career certificate for your resume.

Why learn data analyst skills?

Each year, there is more demand for data analysts and scientists than there are people with the right skills to fill those roles [2]. In fact, according to the US Bureau of Labor Statistics (BLS), the number of job openings for data professionals is expected to grow by 34 percent between 2024 and 2034, significantly higher than the three percent average job growth projected for all jobs in the country [1].

Building and strengthening your analytical skills is important as you set out on your career and later progress in it as a successful data analyst.

7 data analyst technical skills

Let’s take a closer look at what they are and how you can start learning them.

1. SQL

Structured query language, or SQL, is the standard language used to communicate with databases. Knowing SQL lets you update, organize, and query data stored in relational databases and modify data structures (schema).

Since almost all data analysts need to use SQL to access data from a company’s database, it’s arguably the most important skill to learn to get a job. In fact, it’s common for data analyst interviews to include a technical screening with SQL. Luckily, SQL is one of the easier languages to learn.

Get fluent in SQL: With the Databases and SQL for Data Science with Python course from IBM, you'll have the opportunity to learn how to construct basic- to intermediate-level SQL queries using DML commands.

2. Statistical programming

Statistical programming languages like R or Python enable you to perform advanced analyses in ways that Excel cannot. Being able to write programs in these languages means that you can clean, analyze, and visualize large data sets more efficiently.

Both languages are open source, and learning at least one is a good idea. There’s some debate over which language is better for data analysis. Both languages can accomplish similar data science tasks. While R was designed specifically for analytics, Python is the more popular of the two and tends to be an easier language to learn (especially if it’s your first).

Learn your first programming language: If you’ve never written code before, Python for Everybody from the University of Michigan is a good place to start. After writing your first simple program, you can start to build more complex programs used to collect, clean, analyze, and visualize data.

3. Machine learning

Machine learning, a branch of artificial intelligence (AI), has become one of the most important developments in data science. This skill focuses on building algorithms designed to find patterns in big data sets, improving their accuracy over time.

The more data a machine learning algorithm processes, the “smarter” it becomes, allowing for more accurate predictions.

Data analysts aren’t generally expected to have a mastery of machine learning techniques. However, developing your machine learning skills could give you a competitive advantage and set you on a course for a future career as a data scientist.

Get started in machine learning: Andrew Ng’s Machine Learning Specialization from Stanford and DeepLearning.AI is one of the most highly-rated courses on Coursera. You’ll have the opportunity to learn about the best machine learning techniques and how to apply them to problems in this introductory class.

4. Probability and statistics

Statistics refers to the field of math and science concerned with collecting, analyzing, interpreting, and presenting data. That might sound familiar since it closely matches the description of what a data analyst does.

With a strong foundation in probability and statistics, you’ll be better able to:

Identify patterns and trends in the data
Avoid biases, fallacies, and logical errors in your analysis
Produce accurate and trustworthy results

Strengthen your statistical approach: Get the opportunity to explore foundational statistical concepts while learning how to calculate and interpret descriptive and inferential statistics to solve business problems in the DeepLearning.AI Data Analytics Professional Certificate.

5. Data management

Data management refers to the practices of collecting, organizing, and storing data efficiently, securely, and cost-effectively. While some organizations have roles dedicated to data management, such as data architects and engineers, database administrators, and information security analysts, data analysts often manage data in some capacity.

Different companies use different data management systems. As you’re developing your skill set, it can help to gain a broad understanding of how databases work, both in physical and cloud environments.

Learn data management: Get the opportunity to build practical, hands-on skills using essential tools for data management in the IBM Data Management Professional Certificate.

6. Statistical visualization

Gleaning insights from data is only one part of the data analysis process. Another fundamental part is telling a story with those insights to help inform better business decisions. That’s where data visualization comes in. As a data analyst, you'll use charts, graphs, maps, and other visual representations of data to help present your findings in an easy-to-understand way.

Improving your data visualization skills often means learning visualization software, like Tableau. This industry-standard piece of software empowers you to transform your analysis into dashboards, data models, visualizations, and business intelligence reports.

Learn to design dashboards and reports for powerful data visualization: You’ll have the chance to gain hands-on experience with statistical analysis, Microsoft Power BI, and data analysis expressions (DAX) through the Microsoft Data Visualization Professional Certificate.

7. Econometrics

With econometrics, analysts apply statistical and mathematical data models to the field of economics to help forecast future trends based on historical data. Understanding econometrics is key for data analysts looking for jobs in the financial sector, particularly at investment banks and hedge funds.

Practice econometrics: Get the chance to learn how to analyze and solve business and economic questions with data analysis tools in Econometrics: Methods and Applications from Erasmus University Rotterdam.

How to learn data analyst skills

Data analysts leverage these and other technical skills to help others make more informed decisions. Putting in the time and effort to learn these essential skills can set you up for a successful career as a data analyst. Here are a few quick tips for getting started:

Set aside time to regularly work on your skills
Learn from your mistakes
Practice with real data projects
Join an online data community
Build your skills bit by bit

If you’re ready to start building your skill set, explore more tips on how to rise to the challenge. Work with Excel and R to advance your data abilities in the IBM Data Analytics with Excel and R Professional Certificate.

How to include data analyst skills on your resume

As you add new skills to your data analyst toolbox, be sure to update them on your resume as well. Include a “skills” section with a bulleted list of around five of your top data skills. If you list a skill on your resume, be prepared to discuss it in your interview.

It’s also a good idea to incorporate your skills in context. When you include data analysis projects or previous roles, try to include a sentence on how you used a particular skill to complete a task (e.g., “Wrote a Python script to scrape data using the official Twitter API” or “used Tableau to visualize product sales over time”).

Hear from practicing data professionals about what they think employers look for when hiring data analysts.

Have career questions? We have answers.

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Find the tools you need to keep moving forward

If you’re interested in expert guidance and no-fluff tips to help you build your skills, subscribe to our YouTube channel. You can also explore free data analyst resources to learn about data analysis concepts, explore career tips, and discover new opportunities:

Discover career advice: 3 Career Tips from a Google Data Analyst

Find your career path: Career Paths in Data Analysis: Decision Tree

Bookmark for later: Data Analysis Terms & Definitions

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Frequently asked questions (FAQ)

If you are just starting out in data analytics, there are several proactive steps you can take to get into the career. Some concrete steps you can take to improve your chances of landing an entry-level data analyst job include:

Obtain a credential through an educational program, such as a degree or professional certificate.
Work on developing your technical skills, either through in-person or online instruction.
Create a portfolio consisting of either self-directed or group projects.
Gain experience through an internship or volunteer opportunity. ‎

Workplace skills (also called “soft” skills or people skills) are all the intrinsic skills you use to do your job well. While data analysts are prized for their technical skills, you should also strive to hone your workplace skills in order to do your job well. Some of these skills include:

Problem-solving: Data analysts must be adept problem solvers, capable of identifying strategies for finding the answers to the questions that they ask.
Collaboration: Data analysts must often work with others to solve problems and ensure that their objectives are achieved. As a result, collaboration is a key skill that data analysts use every day.
Storytelling and communication: While data analysts spend their time looking at data to glean useful insights, they must also communicate those insights to others. One of the most effective ways to communicate to non-experts is by using storytelling to convey just why your data insights are important and what they mean to others. ‎

Article sources

US Bureau of Labor Statistics. "Occupational Outlook Handbook: Data Scientists, Job Outlook, https://www.bls.gov/ooh/math/data-scientists.htm#tab-6." Accessed February 3, 2026.

Updated on Feb 6, 2026

Written by:

Coursera Staff

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

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