"Value-packed on every page. Everything you need to break into data science is covered. Nelson also offers various technical examples through code to ensure every concept is thoroughly understood. I Highly recommend this book to both data science beginners and seasoned practitioners."
- Derrick Mwiti, Machine Learning Developer,
"This book has it all! From the history of data science to practical examples for how to grow your data career. I wish I had this book when I was starting out. It's a must-read for anyone trying to break into data science."
- Asa Howard, data analyst and educator,
"A work of immense practicality and intellect, which serves as both an extensive guide for novices and a valuable reference for seasoned practitioners."
- Stuart Flint, Chief Data Scientist, Yomp and Cloudify Legal,
"Provides a clear, vibrant guide to all things data. This book engages the reader in a comprehensive exploration of data science. Nelson approaches the topic with humility, clearly sketching both the possibilities and limits of data science."
- Julie F. Mead, Professor Emerita, University of Wisconsin-Madison,
"This is not just a book-it's an invitation to understand and immerse yourself in the world of data science. It artfully discusses ethics, data culture, and the processes that help data science provide immense business value."
- Diogo Resende,
"Data science guru Adam Ross Nelson thoughtfully discusses key fundamentals of data science - including AI and machine learning - so that you can learn and apply the concepts no matter how the field evolves."
- Kristina 'KP' Powers,
"If you are thinking about making a career move into data science, then pick up this book before you get on that path."
- Mark Paige, Professor & Chair, Department of Public Policy, UMass-Darthmouth,
- Section - SECTION ONE: Getting oriented;
- Chapter - 01: The untold history of data science;
- Chapter - 02: Genres and flavours of analysis;
- Chapter - 03: Data culture and the data science process;
- Section - SECTION TWO: Getting going;
- Chapter - 04: Data science examples in production;
- Chapter - 05: A weekend crash course;
- Section - SECTION THREE: Data science for clients;
- Chapter - 06: The client, the project and the data;
- Chapter - 07: Topic analysis;
- Chapter - 08: Regression;
- Chapter - 09: Classification;
- Chapter - 10: Sentiment analysis;
- Section - SECTION FOUR: Tools of the trade;
- Chapter - 11: Data sources;
- Chapter - 12: Data visualization;
- Chapter - 13: Python + R;
- Chapter - 14: Retrospective / prospective