Welcome to the 2020 update of the Self-Taught Data Scientist Curriculum! Smart, scrappy, and resourceful data professionals are more in-demand than ever. That’s as true now, as it was 3 years ago when I first published this article.

What’s changed, however? Plenty! First, the industry is flooded with talent from fresh grads and more mature workers who’ve invested in re-tooling their skill sets. Thankfully though, there is still more demand than supply of data professionals, so if you’re among us then you can join in that *happy dance*.

what it means to be a self taught data scientist in 2020

What else has changed? Well since then, I’ve gone on to train over 1 Million workers on how to do data science and machine learning (with my 5 LinkedIn Learning courses available through this link HERE, and my book Data Science For Dummies HERE.)

And while we should be jumping for joy that there is a more data-educated workforce to staff business requirements…

I’ve seen data initiative and data professionals STRUGGLING profoundly in 2 big ways:

  1. 85% of data projects FAIL – that’s according to Gartner! It’s been this way… Although it seems like there are lots of people focusing on how to build data solutions, and very few people focusing on making sure those projects actually generate profits for the company. Yikes!
  2. With more data professionals on the market, it becomes harder and harder for individual data workers to gain traction as leaders in the industry.

To solve these 2 problems in one FAST & FUN fell swoop… I’ve recently released Winning With Data. It is chalked full of data career quick win challenges that are focused on upgrading a data professional’s skills & visibility with respect to 4 main superpowers: Data Strategy, Project Management, Thought Leadership, and (Organizational) Leadership. The whole point of this product is to get you data career wins within 30 days. You can see more about that HERE.

Some data scientists are trained in academia, and that’s fine. For people with degrees in non-quantitative fields, I recommend those formal academic programs. And then there’s the – driven data scientist, – the dedicated data scientist, – the self-taught data scientist! These are the people who aren’t afraid to go in deep with data, math, and code. These are the type that love to explore the numbers and know that they don’t need some academia professor forcing assignments down their throat in order to make progress in a field.

If that’s you then, welcome to the club!

If you’ve been following along with the Data-Mania blog, then you’ve already researched and identified the skills you need to land a job in data science. If you haven’t gotten that far, worry not – I broke the process down inside this FREE 52-PAGE GUIDE for breaking into data. You can download that and get the whole step-by-step process for free.

(Hhheeeyyy – Let’s help each other out by crowd-sourcing the research. Why not?! In the comment section, write the title of the specific role you research and the top 5 skills that are needed for this role.)

Ok, so… you’re (going to be) a self-taught data scientist. You know what skills you need to master. Now let’s take a look at some of the best places you can go online to learn these skills. The commons skills they need to acquire include (the obvious):

  • Python (for data scientists and engineers)
  • R (for data scientists)
  • Spark (for data scientists and engineers)
  • Tableau (for data scientists and other analytics professionals)
  • Hadoop (for data engineers)
  • SQL (for data scientists and engineers)
  • NoSQL (for data engineers)
  • Machine Learning (for data scientists)
  • Deep Learning (for data scientists)
  • Natural Language Processing (for data scientists)

Helpful note: If you are deciding which skill to master first, I recommend that you learn the skill that is as versatile as possible (notice how Python, Spark, and Tableau are useful in more than one data niche??).

Courses for The Self-Taught Data Scientist and Engineer

The listing below is just a small sample of the courses I recommend. These are generalist courses aimed to please the self-taught data scientist or engineer. [Please note: These are the materials I recommend to clients and to friends, because I believe in the quality of their content. If you purchase through some of these links though, I may earn a small commission on your purchase.]








Machine Learning

Deep Learning

Natural Language Processing

You may have noticed the absence of Coursera and other MOOC courses here. My experience is that courses on Udemy, LinkedIn Learning, and Udacity are much more friendly. By that I mean, you don’t feel like a freshman attending a weed-out course. Instead, the courses that I recommend are designed to make it as easy as possible for you to succeed. I mean, that is the goal – right? ????

Now that you’ve seen my list of recommended self-training materials, why not recommend a few of your personal favorites in the comments section below!?!

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