How to learn data science and (eventually) become an expert

How to learn data science and (eventually) become an expert


When I first found out I wanted to become a data scientist I was completely overwhelmed by the vast breadth and depth of the field of study. The overwhelming complexity blinded me on how to learn. I began with a rough outline of the different resourced I'd found available and planned to take a few MOOC's before moving onto projects.

Well, I did that, but made a few costly mistakes along the way which have slowed down my progress. I'm going to through the problems I encountered and how to overcome them.

MOOC's vs Projects

MOOC's vs Projects.png

The logical diagram above (I admit I went overboard) rationally explains why you should start by learning from a MOOC and then immediately progress to projects. It explains 3 points:

  • MOOC's teach core knowledge, but in a way where you're likely to forget
  • You can't start a project if you don't already have a rough understanding of the problem you're trying to solve
  • So you can learn from a MOOC to get the core knowledge and then immediately do projects to consolidate knowledge

The key takeaway is to ALWAYS start your learning off with theory (MOOC's or books) and then immediately follow up with projects!

The ideal first MOOC

How can you decide which MOOC is right for you? That simply comes down to which MOOC's give you enough foundational knowledge that afterwards, you can understand problems enough to know roughly where to look/how to piece different bits of knowledge together.

From what I've seen and learnt so far, I'd recommend Fast.AI or DeepLearning.AI for this. The main difference between the two courses is their approach to teaching. Fast.AI is top-bottom (starts with applied high-level stuff before going into the nitty-gritty details) whereas DeepLearning.AI is bottom-top (starts with the basic maths and then builds up into modern cutting edge content). Do note though that there are a plethora of other courses I've already categorised/broken down before.


Now that you've heard me ramble, I'd like to thank you for taking the time to read through my blog (or skipping to the end).