Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics
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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 to:
- Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning
- Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon
- Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance
- Manipulate vectors and matrices and perform matrix decomposition
- Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks
- Navigate 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
25 reviews for Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics
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Court Miller –
My team references this book often when explaining why they formed a conclusion from data. It’s core to our business and this book outlines so comprehensively the methods of doing it right, identifying bias, and ensuring our conclusions are accurate.
This is not a how-to book. It’s not even a book on math, really. This is the “why” behind working with data. It is the definitive handbook on data that every data scientist, analyst, business manager should understand before working with data.
If you work with data – and just as importantly – if you rely on a team that works with data, this needs to be on your bookshelf.
Michael –
My math skills are rusty since I left school for so many years. This book makes so many complex math topic simple to understand. The author definitely took a very careful approach to 1. Give real life example 2. Avoid complex math deduction. 3 Avoid unnecessary complexity and focus on core concepts.
I felt all my college CS knowledge are back. This helps me on studying other machine learning books more smoothly.
Highly recommend.
Arthur Ronald –
Esse livro cobre com perfeição os fundamentos matemáticos e estatísticos necessários para você entender as nuances de Ciência de dados. É muito prático, com exemplos em python. Além disso, ele implementa alguns algoritmos tradicionais de Machine Learning ao final do livro.
Macario Lullo –
This is a terrific book for anyone who wants to understand the math necessary for data science. It’s also great for experienced professionals who want to brush up on their mathematics. I fall into the latter camp. Usually, I crack open an math book and plug away at the code but this book embeds the code with examples and concise explanations. As a nerd book junkie, I love this one.
CookieWizard –
I came to this with very little stats and linear algebra knowledge and no calculus. The author goes into just enough detail to be able to understand the math without getting overwhelmed, and the Python implementations really help break up the content and stick the math into your mind. The chapters build on each other with a final chapter on Neural Networks integrating everything you have learned previously. This chapter was a bit hard for me to completely follow and I plan to revisit it after some additional math training. This is a book I think I will go back to again and again through the next couple of years.
Venky –
This is one of the essential materials for early stage of Data Scientist to understand the subject meaningfully. I recommend all stage of data scientist and anlayst should have this book.
Issa Ayoub –
All of the material presented in this book are important/excellent. My only comment is with regard to the grammatical mistakes which confuses the reader sometime. All in all, I highly recommend the book.
Alejandro Martinez –
It is amazing how fast it was delivered to Colombia and without additional charge.
Adrian –
I enjoyed the writing style and the simplicity of the complex concepts.
Marcelo Rezende Módolo –
A leitura é fácil e os exemplos podem ser testados com facilidade.
Stephen Martin –
If you’re looking to learn Machine Learning, start with the math behind the process. This is the book you need.
Cooking Texan –
Since I retired over 20 years ago, I lost much of my familiarity with mathematical and statistical concepts. This book provides a good review of those concepts and made me happy to visit them again.
Mathematical Customer –
I really enjoy the break down of the concepts and how they are applied in Data Science.
Chaminda Ranasinghe –
This book gives a simple and methodical background to the use of math in DS. The author makes sure that there are no gaps in the introduction of math knowledge of the reader when explaining the algorithms. This book makes you appreciate the theories behind DS and ML.
The final chapter discloses the reality in the demand vs hype of DS skills, which is honest and valuable.
Few errors in the narrations which should be fixed in the next revision.
Hemal –
This is one of the best books I have read, really explaining the fundamentals of math like never explained before. I am also making by teenagers read this book to clarify some concepts and develop more interest in mathematics. I highly recommend this book.
tb –
mnowa –
Presents mathematical concepts in an approachable way that is actually applicable to DS. When they told you the math would be useful some day, this book actually points out why in terms of DS problems.
Don –
Comprehensive material. Using it to get smart on the latest data science work.
nesha.st –
Interests
CJ –
Book is brand new and is inside a plastic sleeve. I got the book at a very reasonable price. Item arrived earlier than expected. Have not gone through the whole book yet, but the it seems to be a good refresher for Data Science.
daniel –
This book is proving to be tremendously helpful for me, the concepts are introduced in a nugget-sized non-intimidating way. I find myself understanding more and more, and gaining confidence with concepts I previously felt shaky about. The book is hard to put down. Great job!
Nayeli –
The book arrived wrapped in plastic which was great to protect the book. However, when I unwrapped it and opened it, some pages were dirty (with brown stains). I am unsure why since it is supposed to be a new book. However the contents of the book are good for refreshing topics I had learned in university so that’s why I am giving it a 4 star review.
Eduardo Hiroshi Nakamura –
Excelente
Inder Punna –
Good book to understand the basic math for Data Science
KMP100 –
This book is a good basic review, however if it is the first time you are seeing these concepts the number of exercises provided might not be enough to cement the ideas in your head. I also have found at least 3 typos in the python examples in the first 50 pages. While this can be seen as a learning tool because you then need to think about why the example does not run it can also be a bit frustrating. Particulary if it is coupled with a concept you are struggling to grasp fully.