How Should One Learn Data Science or Artificial Intelligence?

by Koo Ping Shung

“How should one learn data analytics, data science or  artificial intelligence?”

That was the common question I get a few weeks ago when I was having a chat with ‘green’ folks.

There are quite a handful of courses that sells to participants that they teach using XXX tools. To me that is the wrong focus! What is more important is learning about concepts and best-practices rather.

Here are a few reasons.

1) Tools comes and goes, and there are tools that WILL IMPROVE, making usage easier. Anchoring your learning through a tool is like building a foundation on moving sand.

A typical example I give is Hadoop and Spark. They are good tools, with advocates that were flushed with investors money but now they are unheard of,  after being uprooted by cloud.

If a course focuses on teaching a  tool, it is a red-flag in my opinion. If you are looking at a course, check out how much the content is about the tools.

2) Information about tools are readily available online, with videos, blogs, GitHub full of  such information. You must ask yourself, what is the value add of the  courses you are considering, given that info about the tools are so  readily available online. Once you have the value figure out, then ask  yourself is it worth paying the fees for it then.

3) Concepts  and best practices are usually evergreen (or slow to change) and transferable across industries and organizations. Learning them helps  build a strong foundation and the knowledge, imo, are tranferable to  picking up the tools effectively.

TLDR: Learn concepts -> apply them through current tools -> pick up the tool but get ready to un-learn if necessary. :)

This is an updated post on a previously written blog. If you want, can read it here, " Learn Concepts First, Tools Later"

What are your thoughts? Will love to hear them!

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