Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares

By Stephen Boyd and Lieven Vandenberghe, Cambridge University Press:

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Aspiring data scientists often overlook learning discrete math, but they shouldn't.

There are a few key areas to study that that will really build your skills for data science:

1. Sets theory
2. Logic and Proofs
3. Combinatorics

👉 Why do you need to understand set theory, logic, and combinatorics for data science?

These areas of math are the basis for discrete probability and theoretical computer science, e.g. algorithms and data structures.

Don't expect to write good code if you don't understand algorithms and data structures, and don't expect to understand algorithms and data structures if you don't understand discrete math... (so study discrete math)

And really, if you've never studied logic, formally studying it will really help you be able to break problems down and solve them effectively as a data scientist.

So grab a book on discrete math, like this one, and starting working your way through the basics if you haven't already (chapters 0, 1, and 3 are most important).

👉 Download the free PDF -> https://lnkd.in/gNSJiYK

👉 Grab a copy from Amazon -> https://lnkd.in/gW_tVNf

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By the creator of Keras.

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