The Data Science Design Manual by Steven S Skiena

The Data Science Design Manual by 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 distinctheft 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 www.data-manual.com
  
• 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” (www.quant-shop.com)
  

“The book is more than a typical manualIn fact, the author himself designates it as a textbook for an introductory course on data science. The chapters are richly equipped with exercises. The topics are always explained starting with a proper motivation and continuing with practical examples. This is perhaps the most outstanding feature of the book. It can serve as a regular textbook for an academic course. In fact, I should like to recommend it exactly for this purpose. On the other hand, it provides a wealth of material for people from industry, such as software engineers, and can serve as a manual for them to accomplish data science tasks. It should be noted that the book is not just a text, but a much more complex product, including a full set of lecture slides available online as well as a solutions wiki.” (P. Navrat, Computing Reviews, February, 23, 2018)

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At Stony Brook University, Steven Skiena is a Distinguished Teaching Professor of Computer Science. His research focuses on the development of graph, string, and geometric algorithms, as well as their applications (especially in biology). His writings include The Algorithm Design Manual and Calculated Bets: Computers, Gambling, and Mathematical Models to Win. General Sentiment (www.generalsentiment.com), a media measurement company based on his Lydia text/sentiment analysis technology, is co-founded and led by him as Chief Scientist. Skiena earned his Ph.D. in Computer Science from the University of Illinois in 1988 and has authored more than 130 technical articles. He has received the ONR Young Investigator Award and the IEEE Computer Science and Engineer Teaching Award, and he is a former Fulbright scholar.

SKU	Unavailable
ISBN 13	9783319554433
ISBN 10	3319554433
Title	The Data Science Design Manual
Author	Steven S Skiena
Series	Texts In Computer Science
Condition	Unavailable
Binding Type	Hardback
Publisher	Springer International Publishing AG
Year published	2017-08-29
Number of pages	445
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.