Written by Coursera • Updated on
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 skills to fill those roles [1].In fact, according the US Bureau of Labor Statistics the number of job openings for analysts is expected to grow by 23-percent between 2021 and 2031, significantly higher than the five percent average job growth projected for all jobs in the country [2].
But, what skills are the most in-demand in the world of data?
These seven trending data science skills represent those that are some of the most searched by Coursera’s community of million global learners. 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.
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: 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.
specialization
Learn SQL Basics for Data Science
4.5
(8,138 ratings)
174,191 already enrolled
BEGINNER level
Learn More
Average time: 4 month(s)
Learn at your own pace
Skills you'll build:
Data Analysis, Apache Spark, Delta Lake, SQL, Data Science, Sqlite, A/B Testing, Query String, Predictive Analytics, Presentation Skills, creating metrics, Exploratory Data Analysis
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 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.
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. 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 Specialization from Stanford 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.
specialization
Machine Learning
#BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng
4.9
(6,870 ratings)
112,457 already enrolled
BEGINNER level
Learn More
Average time: 3 month(s)
Learn at your own pace
Skills you'll build:
Decision Trees, Artificial Neural Network, Logistic Regression, Recommender Systems, Linear Regression, Regularization to Avoid Overfitting, Gradient Descent, Supervised Learning, Logistic Regression for Classification, Xgboost, Tensorflow, Tree Ensembles, Advice for Model Development, Collaborative Filtering, Unsupervised Learning, Reinforcement Learning, Anomaly Detection
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 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.
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.
Category: Free course
Research Data Management and Sharing
This course will provide learners with an introduction to research data management and sharing. After completing this course, learners will understand the ...
4.7
(635 ratings)
32,144 already enrolled
Learn More
Average time: 1 month(s)
Learn at your own pace
Skills you'll build:
Data Management Plan, Research Data Archiving, Metadata, 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 with the Data Visualization with Tableau Specialization from UC Davis.
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 the three basic methods of econometrics and apply these models to problems in daily life with the Enjoyable Econometrics course 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 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.
Loading...
In this video, we will listen to data professionals talk about what employers look for in a Data Analyst.
Introduction to Data Analytics
IBM Skills Network
Course 1 of 9 in the IBM Data Analytics with Excel and R Professional Certificate
Enroll for Free
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
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.
professional certificate
Google Data Analytics
This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required.
4.8
(95,795 ratings)
1,296,154 already enrolled
BEGINNER level
Learn More
Average time: 6 month(s)
Learn at your own pace
Skills you'll build:
Spreadsheet, Data Cleansing, Data Analysis, Data Visualization (DataViz), SQL, Questioning, Decision-Making, Problem Solving, Metadata, Data Collection, Data Ethics, Sample Size Determination, Data Integrity, Data Calculations, Data Aggregation, Tableau Software, Presentation, R Programming, R Markdown, Rstudio, Job portfolio, case study
Give your team access to a catalog of 8,000+ engaging courses and hands-on Guided Projects to help them develop impactful skills. Learn more about Coursera for Business.
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.
Read: How to Become a Data Analyst (with or Without a Degree)
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.
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: Aata 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.
Read: Hard Skills vs. Soft Skills: What’s the Difference?
Written by Coursera • Updated on
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.
