Books Every Data Scientist Must Read In 2023

Data Science for Business by Foster Provost and Tom Fawcett

This book is a great introduction to data science for business professionals. It covers the basics of data science, such as data collection, data cleaning, and data analysis, and how to use data science to solve business problems.

Machine Learning

A Probabilistic Perspective by Kevin P. Murphy: This book is a more advanced book on machine learning. It covers the mathematical foundations of machine learning, such as probability theory and statistics, and how to use machine learning to build predictive models.

The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

This book is a classic text on statistical learning. It covers a wide range of topics in statistical learning, such as linear regression, logistic regression, and support vector machines.

Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

This book is a comprehensive introduction to deep learning. It covers the basics of deep learning, such as neural networks and backpropagation, and how to use deep learning to build state-of-the-art models for a variety of tasks.

Python for Data Analysis by Wes McKinney

This book is a great introduction to Python for data analysis. It covers the basics of Python, such as variables, functions, and data structures, and how to use Python to analyze data.

R for Data Science by Hadley Wickham and Garrett Grolemund

This book is a great introduction to R for data analysis. It covers the basics of R, such as variables, functions, and data structures, and how to use R to analyze data.

Data Science from Scratch by Joel Grus

This book is a self-contained introduction to data science. It covers the basics of data science, such as data collection, data cleaning, and data analysis, without assuming any prior knowledge of data science.

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