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

by Thomas Nield

Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career.

Learn how

Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learningUnderstand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargonPerform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significanceManipulate vectors and matrices and perform matrix decompositionIntegrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networksNavigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market

Genres: Mathematics, Programming, Computer Science, Nonfiction, Textbooks

568 pages, Kindle Edition

Published May 26, 2022

Reviews

[Nick · Rating 4 out of 5]

The book goes into the basics of machine learning and how it works from a mathematical perspective. It doesn't go so deep where you'll feel overwhelmed and that you cannot finish the book. This is one of the better parts of the book, as I was not familiar with machine learning and some of the mathematical principles behind that. I'm also not a math whiz and still I was able to follow the book or at least gain an understanding of how to run something in say python. I was looking more for ways to do correlation analysis in my work.

This book does have a few examples. Of being able to do this using some Python libraries. And I found that very intriguing. My favorite chapter of the book is actually near the end, right before the appendix I'm not sure the exact chapter but it goes into what exactly data science is and how it's changing how everyone has a different definition of it. And how you can prepare yourself for the changing landscape and data science. I felt this chapter was super helpful. I almost wish it was a standalone article.

And it probably is, but I felt like the last chapter really had a lot of benefit. And it's one of the better chapters I've seen on data science. It was not mathematical, but it was well written and taught you that you need to do some things to stay relevant in the field. It was very helpful. This book is highly recommended and I do think you should check it out to get an understanding of machine learning and AI to a certain extent and then you can kind of get an idea of where to go from there. It's a great introductory book to the subject and does the job

[Jeevan · Rating 3 out of 5]

This one is a short read and a gentle introduction to all necessary concepts to understand logistic regression. Within the limited bounds of the book, the chapter on neural networks was fine.

[38 · Rating 4 out of 5]

Essential Math for Data Science does a great job on explanation confused mathematical topics (e.g. statistics, linear algebra) and connecting these topics with machine learning methods (e.g. linear and logistic regressions) and neural networks. Although, I'm a little bit disappointed that the author hasn't covered some essential machine learning methods like random forest or gradient boosting, I appreciate his efforts. Nevertheless, I can assure you that you will need an additional help in digesting more abstract theory, the best I could find is StatQuest with Josh Starmer and 3Blue1Brown videos on YouTube. I highly recommend to watch the animated videos from aforenamed channels to fully understand what's going on. One of the best parts of this book is the last chapter, where the author gives you insights about real-world data scientist work.

[Kathryn · Rating 5 out of 5]

This was exactly what I needed. I am a non-technical product manager with a very strong but ancient math education. I have been seeking to understand our data scientists’ work and the field of AI. This book empowered me to better understand the terminology used in my daily work life as well as in news articles beyond dictionary definition. I can now ask more probing questions and understand the nuances in sophistication when different options are being discussed.

I focused on the math and conceptual explanations and skipped digging into the code. I also enjoyed the last chapter which helped demystify the role of a data scientist.

I’m left ready to explore more/deeper topics in AI, ML and data science. I can no longer be fooled by sales pitches that haphazardly toss around magical terms like 🪄Machine Learning or 🪄Powered by AI. 100% a great read for professionals like me.

[Shane Simon · Rating 5 out of 5]

I’m a data scientist, but I’m not necessarily a mathematician. This means I have some knowledge gaps that this book helped fill. I wanted to read this book to give me a different perspective on how I’m using these tools. I appreciated the anecdotes and resources throughout the entire book, including the introduction to Statquest. The last chapter was excellent, and I’d suggest it to any data scientist. Cheers and thanks for the fun read

[Paweł Rusin · Rating 4 out of 5]

I really like the idea of trying to present and explain the math behind the popular data science concepts and algorithms. It's also great that author presents all the algorithms in pure python, without any libraries or tools. The math introduced in the book is a bit basic though.

[Chris Tamez · Rating 5 out of 5]

A wonderful read, Essential Math for Data Science illuminates the "black boxes" that ML algorithms can be. Instead of just model.fit(X, Y), you get to build the models from scratch with in-depth explanations on the math.

[rpast]

A good review of basic concepts. Last chapter on professional advice is a gem.

[Leonie · Rating 2 out of 5]

Im Buch sind pro Kapitel einfach mehrere Fehler, was ziemlich enttäuschend ist, dafür, dass es knapp 60€ kostet. Teilweise den Code zu erklären, wäre definitiv auch sinnvoll gewesen.

[Jose Seco Sanz · Rating 5 out of 5]

It is a good book, but very very introductory.

I'm going to improve its rating because I realized that within two days, it helped me to look at two scientific studies with different approach, noticed the bias in the original data.