Transitioning to a career in data analytics can mean stable employment in a high-paying industry once you have the right skills.
Each year, there is more demand for data analysts and scientists than there are people with the right skill set to fill those roles .
But what skills are the most in-demand in the world of data? These seven trending data science skills represent those with the most searches and enrollments by Coursera’s community of 87 million global learners (as of December 2021). To prepare for a new career in the high-growth field of data analysis, start by developing these skills.
Let’s take a closer look at what they are and how you can start learning them.
You can learn many of these data skills, including SQL, R, and data visualization, with the Google Data Analyst Professional Certificate on Coursera. Learn more about the program and how to get started for free.
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: 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.
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 it’s a good idea to learn at least one of them. 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.
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. But 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 course from Stanford was one of the most popular courses on Coursera in 2020. Learn about the best machine learning techniques and how to apply them to problems in this introductory class.
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 into 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.
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 will 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.
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 with the Data Visualization with Tableau Specialization from UC Davis.
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 the three basic methods of econometrics and apply these models to problems in daily life with the Enjoyable Econometrics course from Erasmus University Rotterdam.
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 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.
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
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.
1. CrowdFlower. "2017 Data Scientist Report, https://visit.figure-eight.com/rs/416-ZBE-142/images/CrowdFlower_DataScienceReport.pdf." Accessed December 23, 2021.
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.