7 Books Every Data Scientist
Must Read In 2023
"The Art of Statistics: Learning from Data"
by David Spiegelhalter
This book explores the principles and techniques of statistics, providing valuable insights into how data can be effectively analyzed and interpreted.
"Python for Data Analysis"
by Wes McKinney
As Python is a popular programming language for data science, this book offers a comprehensive guide to using Python for data manipulation, analysis, and visualization.
by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Deep learning is a crucial aspect of modern data science. This book provides a comprehensive introduction to deep learning concepts and techniques.
"Storytelling with Data: A Data Visualization Guide for Business Professionals"
by Cole Nussbaumer Knaflic
Effective data visualization is essential for conveying insights. This book offers practical guidance on how to create compelling visualizations that tell a story.
"Data Science for Business"
by Foster Provost and Tom Fawcett
This book focuses on the intersection of data science and business, exploring how data-driven decision-making can drive business success.
"The Hundred-Page Machine Learning Book"
by Andriy Burkov
This concise book provides a comprehensive overview of machine learning concepts, algorithms, and practical applications.
"Applied Predictive Modeling"
by Max Kuhn and Kjell Johnson
This book delves into the practical aspects of predictive modeling, offering insights into techniques and best practices for building accurate predictive models.