Data Science Books 2-in-1 deal (Essential Math Data Science + Practical Statistics)

Essential Math for Data Science by Thomas Nield

Build a strong foundation in data science with Essential Math for Data Science by Thomas Nield. This practical guide introduces the core mathematical concepts needed to understand and work with data effectively, including linear algebra, probability, and statistics.

Nield breaks down complex ideas into clear, intuitive explanations, helping readers grasp how mathematical principles apply to real-world data analysis and machine learning. With examples and problem-solving approaches, the book supports learners in developing both conceptual understanding and practical skills.

Accessible and focused, this edition is ideal for beginners and aspiring data scientists looking to take control of data through essential mathematical tools.

Practical Statistics for Data Scientists 50+ Essential Concepts Using R & Pyt

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you'll learn: 

• Why exploratory data analysis is a key preliminary step in data science 
• How random sampling can reduce bias and yield a higher-quality dataset, even with big data 
• How the principles of experimental design yield definitive answers to questions 
• How to use regression to estimate outcomes and detect anomalies 
• Key classification techniques for predicting which categories a record belongs to 
• Statistical machine learning methods that "learn" from data 
• Unsupervised learning methods for extracting meaning from unlabeled data