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

This guide provides a clear, plain English survey of the math needed for data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus. Practical examples with Python code help you understand how concepts work and apply them to applications like machine learning.

Format: Paperback / softback
Length: 350 pages
Publication date: 10 June 2022
Publisher: O'Reilly Media

Data science is a rapidly growing field that requires a strong foundation in mathematics. While some math proficiency is necessary, not just any math will do. This guide provides a clear and concise overview of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.

Probability math is essential for understanding the likelihood of events occurring and making informed decisions based on data. It covers concepts such as random variables, probability distributions, conditional probability, and Bayesian inference. Practical examples with Python code will help you see how probability math applies to real-world scenarios, such as predicting the outcome of a coin toss or analyzing data from a survey.

Statistics is a powerful tool for analyzing data and making predictions. It covers concepts such as descriptive statistics, probability distributions, hypothesis testing, and regression analysis. Practical examples with Python code will help you understand how statistics can be used to analyze data from different fields, such as healthcare, finance, and marketing.

Hypothesis testing is a critical component of data science. It involves testing a hypothesis about a population parameter or a relationship between variables using statistical methods. This guide covers the basic principles of hypothesis testing, including the null hypothesis, alternative hypothesis, significance level, and p-value. Practical examples with Python code will help you apply hypothesis testing to real-world data and make informed decisions.

Linear algebra is a fundamental concept in data science that involves the manipulation of vectors and matrices. It is used in various applications, such as machine learning, computer vision, and signal processing. This guide covers the basics of linear algebra, including vector spaces, matrices, linear equations, and matrix operations. Practical examples with Python code will help you understand how linear algebra can be used to solve complex problems in data science.

Machine learning is a subset of artificial intelligence that involves the use of algorithms to learn from data and make predictions. It covers concepts such as supervised learning, unsupervised learning, and deep learning. This guide provides an introduction to machine learning, including the different types of algorithms, the training process, and the evaluation of machine learning models. Practical examples with Python code will help you implement machine learning algorithms and build your own models.

Calculus is a fundamental concept in mathematics that involves the study of continuous change and the integration of functions. It is used in various applications, such as physics, engineering, and finance. This guide provides an introduction to calculus, including the basic concepts of differentiation and integration, limits, and derivatives. Practical examples with Python code will help you apply calculus to solve complex problems in data science.

In conclusion, data science requires a strong foundation in mathematics, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus. This guide provides a clear and concise overview of these concepts, with practical examples and Python code to help you understand how they apply to real-world scenarios. By mastering these math skills, you'll be able to analyze data, make predictions, and build innovative solutions that can impact various fields. So, whether you're a beginner or an experienced data scientist, this guide is a valuable resource for advancing your knowledge in data science.

Weight: 604g
Dimension: 177 x 232 x 22 (mm)
ISBN-13: 9781098102937