The Data Science Design Manual (Texts in Computer Science)

Steven S. Skiena



This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on 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 really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead 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 a wide range of exercises and projects for self-study
  • Provides a complete set of lecture slides and online video lectures at
  • Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each 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 television show “The Quant Shop” (















- 包含「戰爭故事」,提供資料科學在現實世界中的應用觀點
- 包含「作業問題」,提供各種練習和自學專案
- 提供完整的講義和線上視頻講座,網址為
- 提供「帶回家的教訓」,強調每章所學的整體概念
- 推薦來自線上平台 Kaggle 的刺激「Kaggle 挑戰」
- 突顯「失敗的開始」,揭示某些方法失敗的微妙原因
- 提供來自資料科學電視節目「The Quant Shop」的範例,網址為