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1
The algorithm design manual / Steven S Skiena
London : Springer, 2008
730 p.
Ký hiệu phân loại (DDC): 005.1
This expanded and updated second edition of a classic bestseller continues to take the mystery out of designing and analyzing algorithms and their efficacy and efficiency. Expanding on the highly successful formula of the first edition, the book now serves as the primary textbook of choice for any algorithm design course while maintaining its status as the premier practical reference guide to algorithms. NEW: (1) Incorporates twice the tutorial material and exercises. (2) Provides full online support for lecturers, and a completely updated and improved website component with lecture slides, audio and video. (3) Contains a highly unique catalog of the 75 most important algorithmic problems. (4) Includes new war stories and interview problems, relating experiences from real-world applications. Unique, handy reference package with a practical, hands-on appeal to a wide audience This classic bestseller has been expanded and updated with twice the original tutorial material and exercises Contains a highly unique catalog of the 75 most important algorithmic problems Additional useful information such as lecture slides and updates available via author's website.
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2
The algorithm design manual / Steven S Skiena
London : Springer, 2020
740 p.
Ký hiệu phân loại (DDC): 005.1
This book is intended as a manual on algorithm design, providing access to combinatorial algorithm technology for both students and computer profession-als. It is divided into two parts: Techniques and Resources. The former is ageneral introduction to the design and analysis of computer algorithms. The Re-sources section is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations, and an extensive bibliography.
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3
The Data Science Design Manual / Steven S. Skiena
Switzerland : Springer, 2017
456 p. ; cm.
Ký hiệu phân loại (DDC): 519.50285
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 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)
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