MIT 6.00.1x/2x : A Course to Close Your Computer Science Gap as a Data Scientist

Why All Data Scientists Not From Computer Science Background Should Take the Course

Photo by Irvan Smith on Unsplash

…my Journey into Data Science So Far

My Data Science self-education journey has always been a bit upside down. Instead of the usual Math > Computer Science > Language/Framework > Machine Learning > Deep Learning path. I took the other way around. I stumbled into Deep Learning from the fast.ai ‘Practical Deep Learning for Coders’ course first, which greatly boosted my interest and intention to learn more. Later I took the classic ‘Machine Learning’ course by Andrew Ng. (my review on these two courses here.) These are both great courses in their own merits and as I learn more and start doing my own projects, I found that my coding skills is stopping me from making solid progress. I had to stop and learn some programming concept on the fly to make sense of the important snippets in the course or GitHub. So I took the famous CS50 course by Harvard celebrity professor David J. Malan. It helped me a lot, but I still felt incompetent when it comes to Python programming, especially OOP, data structures, and algorithms, until I find MIT 6.00.1x and 6.00.2x on Edx.

Overview

You can find the courses here and here. The course full names are:

“Introduction to Computer Science and Programming Using Python”

“”Introduction to Computational Thinking and Data Science”

XSeries of the two courses

They belong to the “Computational Thinking using Python” program on EdX. It used to be one course(YouTube playlist here) for MIT on-campus students. They split it into two parts, so it’s more digestible for online education. We can still treat them as one course since the lessons are closely related to each other.

Having taken both courses, here are my two cents if you don’t have the time to read through the rest of this article:

Start from ‘level 0’, well-designed learning curve, but still challenging