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Sto caricando le informazioni... The Data Science Design Manualdi Steven S. Skiena
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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). Non sono state trovate descrizioni di biblioteche |
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Google Books — Sto caricando le informazioni... GeneriSistema Decimale Melvil (DDC)006.312Information Computer Science; Knowledge and Systems Special Topics Artificial Intelligence Machine LearningClassificazione LCVotoMedia:
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It's always good to get Skiena's take on things -- I've read three or four of his books now -- and this one is no exception. The statistical-learner stuff is linked more closely to standard CS topics (e.g. algorithmic complexity) than in most other texts, and the overview of linear algebra is really quite good.
The only real downside is that it doesn't do what is says on the tin. Unlike The Algorithm Design Manual, this isn't presented as a taxonomy of data science methods with a briefing of when and how each should be supplied. More's the pity, as that particular book is sorely needed - even in this one, Skiena points out that most researchers become comfortable with one approach and use it for everything, rather than testing alternate approaches on new problems.
Instead, it's a standard Introduction to Data Science textbook with chapters devoted to topics of increasing complexity/sophistication. Well-written, often entertaining, with an excellent selection of exercises (including many Kaggle challenges and some publicly-available datasets - precisely the sort of project that a beginner needs to get their feet wet). ( )