Description
This engaging and clearly written textbook/reference provides a should-have introduction to the impulsively emerging interdisciplinary field of data science. It specializes in the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data.
The Data Science Design Manual is a source of practical insights that highlights what in point of fact matters in analyzing data, and provides an intuitive understanding of how these core concepts can be utilized. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing as an alternative on high-level discussion of important design principles.
This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well.
Additional learning tools:
- Contains “War Stories,” offering perspectives on how data science applies in the real world
- Includes “Homework Problems,” providing quite a lot of exercises and projects for self-study
- Provides a complete set of lecture slides and online video lectures at www.data-manual.com
- Provides “Take-Home Lessons,” emphasizing the big-picture concepts to be informed from every chapter
- Recommends exciting “Kaggle Challenges” from the online platform Kaggle
- Highlights “False Starts,” revealing the subtle reasons why certain approaches fail
- Offers examples taken from the data science tv show “The Quant Shop” (www.quant-shop.com)