7 In-Demand Data Analyst Skills to Get You Hired in 2024

Each year, there is more demand for data analysts and scientists than there are people with the right skills to fill those roles [1]. In fact, according to the US Bureau of Labor Statistics (BLS), the number of job openings for analysts is expected to grow by 23 percent between 2022 and 2032, significantly higher than the five percent average job growth projected for all jobs in the country [2].

Building and strengthening your skill set is important as you set out on your career and later progress in it. In this article, we'll review seven trending data science skills. If you're ready to get started right away, consider enrolling in the Meta Data Analyst Professional Certificate.

7 data analyst 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, as well as modify data structures (schema). 

Since almost all data analysts will 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 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. Either language 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. 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. 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—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

Master modern statistical thinking: Get a refresher with the Probability and Statistics course from the University of London. If you’ve already picked up some programming, learn to apply your skills to statistical analysis through Statistics with Python from the University of Michigan or Statistics with R from Duke University.

5. Data management

Data management refers to the practices of collecting, organizing, and storing data in a way that is efficient, secure, and cost-effective. While some organizations will have roles dedicated to data management—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 about data engineering: Get an overview of the modern data ecosystem with Introduction to Data Engineering from IBM. Learn more about the role data analysts, scientists, and engineers play in data management.

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 can 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 can gain hands-on experience with statistical analysis, Microsoft Power BI, and data analysis expressions (DAX) through Microsoft's Data Visualization Professional Certificate program.

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: Learn how to analyze and solve business and economic questions with data analysis tools in Econometrics: Methods and Applications from Erasmus University Rotterdam.

Tips for learning data analysis skills

Data analysts leverage these and other technical skills to help inform decisions in their organizations. Putting in the time and effort to learn these 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.

Read more: Data Analyst Cover Letter: Sample and Guide

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Learn data analyst skills on Coursera

Start building many of these data analyst job-ready skills with the Meta Data Analyst Professional Certificate. You'll learn how to use statistical analysis, including hypothesis testing, regression analysis, and more, to make data-driven decisions.

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