5 Things to Know Before Rushing to Start in Data Science

Matrix calculations, derivatives, eigenvalues, Set Theory, functions, vectors, linear transformations, etc. are extremely important to understand the theory behind statistical methods and programming. Therefore, before starting your next MOOC or Machine Learning book it’s crucial to review all those concepts again. Most schools request students to be proficient at these methods in order to graduate, but the silver lining is that it won’t require too much of your time to refresh or obtain this knowledge.

There are plenty of resources to start, but what worked for me was The Manga Guide to Linear Algebra, which is very simple, graphic and provides a great foundation prior getting into more complex stuff.

My suggestion is to schedule some weeks to review these concepts and to use the Feynman Technique to be able to explain in simple terms each of these topics.

2. Although there are many useful internet resources, books are still one of the best tools to learn from.

One of the issues people face today when trying to get into a field such as Data Science is Information Overload, a term used when talking in relation to the effect of having too many resources at the disposal. There are hundreds of MOOCs, online courses, specialisations, videos, etc., but the best use of the most valuable resource that we have, “time”, is to pick a book and start from the basics up to new concepts, and then keep filling the gaps with other books.

Learning Data Science should be seen like a building blocks game.

I believe this analogy is the best for learning most of the things, but it is extremely useful in our Data Science journey:

The same should be done with all the techniques in each area of Data Science. Learn what most all the blocks are, learn how to use them and then when you want to create more complex stuff look for the missing parts that you don’t have.

3. Computing skills are essential, not just for Data Science but for tomorrow’s world.