Essential Math for Data Science: Take Control of Your Data with Fundamental Calculus, Linear Algebra, Probability, and Statistics

by Hadrien Jean

Master the math needed to excel in data science and machine learning. If you're a data scientist who lacks a math or scientific background or a developer who wants to add data domains to your skillset, this is your book. Author Hadrien Jean provides you with a foundation in math for data science, machine learning, and deep learning.

Through the course of this book, you'll learn how to use mathematical notation to understand new developments in the field, communicate with your peers, and solve problems in mathematical form. You'll also understand what's under the hood of the algorithms you're using.

Learn how to:


Use Python and Jupyter notebooks to plot data, represent equations, and visualize space transformations
Read and write math notation to communicate ideas in data science and machine learning
Perform descriptive statistics and preliminary observation on a dataset
Manipulate vectors, matrices, and tensors to use machine learning and deep learning libraries such as TensorFlow or Keras
Explore reasons behind a broken model and be prepared to tune and fix it
Choose the right tool or algorithm for the right data problem

Genres: Mathematics, Technology, Programming, Software, Nonfiction, Technical

347 pages, Paperback

Published July 13, 2021

Reviews

[Findus · Rating 1 out of 5]

Lazy and half-a**ed execution.
Full of mistakes.

This is a book where you have to triple-check every formula, because it is so full of mistakes. Absolutely useless, if you cannot even trust the "Maths" in a book about maths. It is dangerous to rely on this book before meticulously "updating" it using the errata (none of which are acknowledged by either the author or the publisher). Stay clear! Useless waste of money.

And in addition, for a book that pretends to give you the "Essential Maths", I would expect more explanation. If I just wanted the formulas and how to use them in Python, I can just find that on wikipedia and stackoverflow (and probably with fewer errors at that).

There are errors in the formulas, the code blocks, the "solutions" to the exercises have errors and use different values than presented in the original exercise (and STILL manage to get the calculations wrong on top of that), the text is full of errors and often references the wrong python packages, the figures have extremely glaring mistakes (wrong signs for vectors, errors in matrix multiplication, errors in vector scaling)... the list goes on and on.

Plus, the math is not explained. The author just dumps the formulas on you, without any explanations. E.g., quote on pg. 82: "There is a lot to take apart in this formula here", but guess what, it is never "taken apart" or explained, the author just moves on to the next section.

Also: Goodreads should get the author correctly. This is written by Thomas Nield.

[Risto Hinno · Rating 5 out of 5]

Good book to refresh or gain knowledge about calculus, linear algebra, probability in machine learning. Has python notebooks and tries to explain meaning of things before diving into formulas. Great read.

[Krishna]

Heard this is good for maths needed for data science

[Marvin · Rating 4 out of 5]

I like the "why's" most in this book. Not just the 'this = that'.

[Joshua Hruzik · Rating 5 out of 5]

A book I would have loved to have when starting out!

Essential Math for Data Science by Thomas Nield is exactly what the title suggests. It covers the most important math concepts that are needed to work in data and analytics related jobs. The topics range from basic math, to probability, stats, linear algebra, and calculus.
By focusing on the most important aspects and by providing very manageable examples in Python, one can grasp the intuition behind these topics very fast. Even if you are already a seasoned vet, you might learn new things or at least see them from a different perspective (loved the explanation of statistical significance using the CDF).
However, keep in mind that this a very dense book. A lot of content is packed into very few packages. This might be even too dense if you have never been exposed to these topics. Maybe grab a good stats, linear algebra, and calculus intro before jumping into this book.

[Vaishnav Patil · Rating 5 out of 5]

Good start to data science career

[Pawin · Rating 5 out of 5]

A book that any data scientist should not miss. The book covers the topic of basic calculus, linear algebra, and statistics.

[Abanoub Maurice]

amazing

[Emanu · Rating 3 out of 5]

It's a good book if you already know the terms and math in it.
Covers more on how you can apply it than actually teaching the math

[Diego Brand · Rating 4 out of 5]

It’s great for starters but as some people have pointed it has some errors. Anyways I liked it since it teaches a lot of different concepts as well as some final thoughts on the job itself.