Learning data science is hard.

But it's made even harder by the fact that new learners are somehow supposed to know what to learn. It can be tough to chart a way with university degrees, boot camps, MOOCs, online courses, offline courses, blog posts, books, self-study, and projects.

Everyone and their mother (including me) has a course. So how do you decide?

Do you already have a university degree?

Influencers love to tell you that you don't need any degrees these days.

As someone that has done hiring, that is true under exactly one circumstance: If you have job experience that surpasses the degree. But it's not just about getting that job. Any statistic that you look at will tell you the following pay increase after getting the degree compared to a high school diploma at an average of $39,000:

  • Bachelors: + ~$25.000 USD
  • Master's: + ~$39.000 USD (this statistically doubles your salary)
  • PhD: + ~$59.000 USD

You don't need a specific degree in data science. Got one in medicine or marketing, physics or pedagogy? Those are equally fine to get started with data science and make sure you aren't subject to the whim of employers as much.

If you don't have a degree yet, consider one, either in data science or in the topic you'd like to apply data science to.

Choose something applied!

Data science is an applied science.

While you can definitely read books, you should always seek out something that has you apply concepts. Whether they're the famous Coursera MOOCs, my course, or Joel Grus' Data science from scratch. You'll learn an incredible amount.

Once you start learning and applying data science to problems, you'll have a better idea of what to learn next.

Data science and creating a curriculum get easier once you get started.

Image of Atomic Essay Day 21 - How to finally start learning data science and stop worrying if it's the right course

This atomic essay was part of the October 2021 #Ship30for30 cohort. A 30-day daily writing challenge by Dickie Bush and Nicolas Cole. Want to join the challenge?