Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics 1st Edition ISBN: 9781098102937
Original price was: $64.99.$44.99Current price is: $44.99.
Product Details
- Condition: New
- Publisher: O’Reilly Media
- Language: English
- Paperback: 349 pages
- ISBN: 9781098102937
- Item Weight: 1.28 pounds
- Dimensions: 7 x 0.75 x 9 inches
Description
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
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| Weight | 1.28 lbs |
|---|---|
| Dimensions | 17.78 × 1.905 × 22.86 in |
Patrick Llewellyn –
Well organized and practical. A helpful guide for students who want to strengthen their analytical skills.
Gordon Ellsworth –
Very useful for building confidence in mathematical fundamentals. It’s a dependable starting point for beginners.
Bryce Monaghan –
Simple, clear, and informative.
Leonard Sloane –
A comprehensive introduction to core math concepts required for data science. The writing style makes complex topics easier to grasp.
Trevor Hargreaves –
Clear explanations and practical examples. The sections on probability and statistics are especially useful.
Dennis Whitfield –
A solid foundation guide for anyone entering analytics or machine learning. The structured chapters make learning comfortable.
Colin Armitage –
This book connects mathematical theory with real data science applications. The examples help in understanding how formulas are used in practice.
Victor Langford –
Well written and beginner friendly. It breaks down mathematical concepts in a way that’s easy to follow.