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Building Data Science Skills as an Undergraduate

While there are a growing number of universities that offer undergraduate data science degrees, for one reason or another those programs may not be perfect for everyone interested in data science. So, what do you do if you attend a school that does not offer a data science degree? This is a question frequently asked of me, so I thought I would elaborate on my typical response.

You Cannot Know It All

First off, you will never know all there is to know about data science. The field is vast and contains many sub-fields. Thus, as an undergraduate, a good plan is to learn the fundamentals. Then expand your knowledge/expertise as your education and career continue. Data Science is evolving rapidly and it requires continual learning. Hopefully, this is one of the reasons you are interested in the field.

My Recommended Approach

A good plan is to major in computer science or statistics and minor in the other. If your school doesn’t have either of those major, then take as many of those classes as you can. Next, choose a domain specific area such as business, chemistry, psychology, etc.; and gear your elective classes toward that domain area. This approach will give you a solid base understanding of the statistical and computational underpinnings of data science. You should also be well-prepared to find a job or continue your studies in graduate school.

Also, somewhat related, taking an art class or two might not be a bad idea. Visualization is very important to data science. Understanding color palettes and usage of space on a canvas are concepts that will serve you well. Plus, many people strong in computer science and statistical algorithms are lacking in artistic skills.

Some Enhancements to Your Education

If your location allows, consider attending local meetups. Finally, get involved with whatever projects you can (Kaggle, internships, open source, …).

Do you have any advice for undergraduates looking to study data science? If so, please leave a comment.

Are you and undergraduate with questions? Please ask in the comments below.

7 thoughts on “Building Data Science Skills as an Undergraduate”

  1. Every company will have a different take on job tasks. Some treat their data scientists as glorified data analysts or combine their duties with data engineers ; others need top-level analytics experts skilled in intense machine learning and data visualizations.

  2. Ryan,

    If I wanted to reboot my math education, should I start at algebra and work my way back up into statistics?

    I’d like to get a better grasp of stats/ML, but want to rebuild the foundation of my math understanding from ground zero.


    1. Jeff,
      Here is my recommendation. Start with stats and just be prepared to look some things up as a reference (algebra and other math topic you forgot). This way you start to learn the things you want (stats) right away. It will also help motivate why you are learning the algebra. If you start with algebra, you may lose interest or motivation before you actually get to statistics. That is just my recommendation, take it or leave it.

      Thanks for commenting and asking,

  3. Yeah! I have some tips for students (undergraduates and graduates) or anyone else who is planning to enter the field of data science.

    Learn basics, such as Python programming
    Be Polymath, meaning be ready to understand higher levels of math
    Be ready to continually learn

    On the other hand, there are many websites that provide data science training classes. is one of them, please visit this site to grab more knowledge about data science.

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