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

Demand for data scientists continues to grow, and experts anticipate 30 per cent of these roles in the next 10 years will lay vacant due to employee shortages [1]. According to the Government of Canada Job Bank,  the outlook for data analyst jobs in most provinces is good to very good over the next few years [2].

But what skills are the most in-demand in the world of data?

These seven trending data science skills represent some of the most searched by Coursera’s community of global learners. To prepare for a new career in the high-growth field of data analysis, start by developing these skills.

1. SQL

Structured Query Language (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 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. 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: Develop SQL fluency, even if you have no previous coding experience, with the Learn SQL Basics for Data Science Specialization from UC Davis. Work through four progressive SQL projects as you learn how to analyze and explore data.

2. Statistical programming

Statistical programming languages, like R or Python, enable you to perform advanced analyses in ways that Excel cannot. Writing programs in these languages means 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 explicitly designed 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 build more complex programs 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 generally don’t need 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 is one of the most highly-rated courses on Coursera. In this introductory class, learn about the best machine learning techniques and how to apply them to problems.

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 practice of collecting, organizing, and storing data efficiently, securely, and cost-effectively. 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 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.

Get visual with Tableau: Once you’re comfortable working with data and data sets, practice creating powerful visualizations of your data in Data Visualization with Tableau course from Tableau itself.

7. Econometrics

With econometrics, analysts apply statistical and mathematical data models to 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 at 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 work on your skills regularly

• Learn from your mistakes

• Practice with real data projects

• Join an online data community

• Build your skills bit by bit

Learn more about what employers look for in a data analyst in this lecture from IBM's Introduction to Data Analytics course:

How to include data analyst skills on your resume

As you add new skills to your data analyst toolbox, 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. For example, “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.

Get started with Coursera.

Start building many of these data analyst job-ready skills with the Google Data Analytics Professional Certificate through Coursera. Learn how to clean and organize data with SQL and R, visualize with Tableau, and complete a case study for your portfolio—no prior experience or degree required. Upon completion, you can start applying for entry-level jobs directly with Google and more than 130 other US employers.