Learn how you can structure your Data Science Education with Coursera in 2021.

Nowadays, there are endless ways to learn anything. With the rise of online learning, there are thousands of courses offered by universities and experts that can be completed at your own pace from the comfort of your home. Coursera, founded by two Stanford professors in 2012, offers courses from some of the world’s best universities such as Yale, Duke University, Johns Hopkins University, Imperial College London, and University of Michigan. The best part about Coursera is that you can access nearly all of its catalog for $399/year through its annual subscription Coursera Plus, which is a little more than $1 per day when you think about it.

With Coursera Plus, we were able to create the equivalent of a data science degree that can be completed entirely online at your own pace. Depending on how much time you have, this data science track could be completed in 1–4 years, similar to an actual degree. Of course, you wouldn’t receive a diploma for completing these courses, but you’d learn the skills necessary to pursue a career in data science. Upon completion of each course, Coursera provides a shareable certificate that recognizes your accomplishment and highlights your skills for employers.

About Data Science

What is data science?

Data science combines programming, math, and statistics to generate insights using data. The field also dabbles in topics such as data engineering, machine learning, and deep learning. From health informatics to sports analytics, data science is applicable to almost any field, company, or career.

What does a data scientist do?

At its core, a data scientist uses data to make business decisions through automated processes. On the human side of things, a data scientist makes other’s jobs easy by providing them with streamlined data analytics and insights. Essentially, you help simplify complex problems using coding, mathematics, and statistics.

Why pursue a career in data science?

Given the importance of data in today’s world, a data scientist can work in almost any industry. The opportunities are endless! According to Linkedin, demand for data scientists has grown 37% annually. Specific roles in Data Science, such as Artificial Intelligence Specialists, are expected to grow by a whopping 74%. Plus, Harvard Business Review calls it the “Sexiest Job of the 21st Century.” Not to mention, the pay rate isn’t too shabby either. According to GlassDoor, the average data scientist salary is more than $110,000.

A career in data science provides not only decent job security with a solid pay rate, but also a fun and exciting career for anyone who loves to solve complex problems and provide insights for key business decisions. Data science is a growing field, and we recommend that everybody learns the basics of data science to understand why it’s so important.

Self-Paced Data Science Degree Curriculum

In a typical data science curriculum, you’ll be required to take introductory courses in programming, math, statistics, and computer science. Sometimes, a data ethics course might be required as well. Once you’ve completed the basics, there are several advanced topics or career paths that can be pursued.

Foundations & Programming

Learning how to code is quintessential to data science. Although it can take years to feel comfortable with coding or consider yourself an expert, you can still achieve a lot as a beginner. There are three major coding languages we recommend that data science beginners learn the basics: Python, R, and SQL. With these three coding languages, you can pursue almost any data science career. Coding can be tough at first attempt, but don’t give up too fast! Complete the courses below to learn data science foundations and programming.

Programming Courses, University

1. Data Science: Foundations using R, Johns Hopkins University
2. Python for Everybody and/or Python 3 Programming, University of Michigan
3. SQL for Data Science, UC Davis

Math & Statistics

To move from programmer to data scientist (or even computer scientist), you will need to learn math and statistics. Some of the courses below are part of real Master’s degrees offered on Coursera. Completing the below courses will provide mastery of both math and statistics.

Math Courses

1. Introduction to Calculus, University of Sydney
2. Data Science Math Skills, Duke University
3. Math for Data Science, National Research University Higher School of Economics
4. Mathematics for Machine Learning, Imperial College London

Statistics Courses

1. Statistics with Python, University of Michigan
2. Data Science: Statistics and Machine Learning, Johns Hopkins University
3. Advanced Statistics for Data Science, Johns Hopkins University

Advanced Topics

Now that you’ve completed the foundations of data science, you can move onto advanced courses that apply the basics to data science. These courses unlock deeper understanding of topics such as machine learning, deep learning, and applied data science. Consider these as your upper division courses: take whichever of these topics spark your interest or help you move towards achieving your career goals.

Applications & Advanced Topic Courses

1. Applied Data Science with Python, University of Michigan
2. Machine Learning, University of Washington
3. Advanced Machine Learning, National Research University Higher School of Economics
4. Reinforcement Learning, University of Alberta

Other Courses & Applications (Not Included in Coursera Plus)

More than 3,000 courses are included in Coursera Plus, but there are some courses relevant to some fields in data science that aren’t included in Coursera’s annual subscription. The below courses are optional, but might be worth considering if relevant to your career goals.

Other Courses & Applications

1. Machine Learning, Stanford
2. Algorithms, Stanford
3. Deep Learning, DeepLearning.AI
4. DeepLearning.AI TensorFlow Developer, DeepLearning.AI
5. Natural Language Processing, DeepLearning.AI

Other Questions

Is Coursera Plus worth it?

At $399/year, Coursera Plus is about $1 per day and grants access to more than 3,000 courses from some of the best universities around the world.

Not only can you pursue the data science curriculum we share in this article, but you can also take courses from a variety of other subjects. Some of our favorites include The Science of Well-Being by Yale and Business Foundations by University of Pennsylvania’s Wharton School of Business. Coursera Plus also includes courses on soft skills such as resume writing, public speaking, and negotiation. With Coursera Plus, the learning possibilities are endless.

Should I learn Python or R programming?

Both languages are important in the field of data science. We recommend that beginners learn both. Once you’ve tried out both Python and R, go with whichever language you prefer and whichever is standard for the industry you plan to pursue a career in.

What else should I do to become a data scientist?

In addition to taking the above course curriculum, make sure to work on personal projects that apply what you learned. Practice makes perfect! Projects demonstrate your skills to employers and also help you master the data science skills you gained.

Finally, here’s the video discussion behind this article on Coursera Self-Paced Data Science Degree:

P.S. The above links are affiliate, and this article was created with the help of Coursera. Thank you for your support!