“Foundations of Data Science” by John Hopcroft and Ravindran Kannan is a textbook that provides an introduction to the fundamental concepts and techniques used in data science. The book is designed for undergraduate students and assumes no prior knowledge of computer science or statistics.
The book covers a wide range of topics including probability, statistics, linear algebra, optimization, and machine learning. It introduces the basic principles and techniques of each topic and then shows how they can be applied to solve real-world problems.
The book is divided into four parts.
Part 1 covers probability and statistics, including basic probability theory, random variables, and hypothesis testing.
Part 2 covers linear algebra, including matrices, eigenvectors, and singular value decomposition.
Part 3 covers optimization, including linear programming, convex optimization, and gradient descent.
Part 5 covers machine learning, including classification, clustering, and neural networks.
Throughout the book, the authors provide numerous examples and exercises to help readers understand and apply the concepts and techniques presented. The book also includes a number of case studies that demonstrate how data science is used in various industries and applications.
Overall, “Foundations of Data Science” is a comprehensive and accessible introduction to data science that is suitable for undergraduate students, and anyone interested in learning the basics of data analysis and machine learning.
