The Data Science Design Manual

Author:   Professor Steven S. Skiena
Publisher:   Springer International Publishing AG
Edition:   1st ed. 2017
ISBN:  

9783319554433


Pages:   445
Publication Date:   29 August 2017
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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The Data Science Design Manual


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Full Product Details

Author:   Professor Steven S. Skiena
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2017
Weight:   1.168kg
ISBN:  

9783319554433


ISBN 10:   3319554433
Pages:   445
Publication Date:   29 August 2017
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

What is Data Science?.- Mathematical Preliminaries.- Data Munging.- Scores and Rankings.- Statistical Analysis.- Visualizing Data.- Mathematical Models.- Linear Algebra.- Linear and Logistic Regression.- Distance and Network Methods.- Machine Learning.- Big Data: Achieving Scale.

Reviews

The 14 chapters of this book have been carefully devised to provide a comprehensive introduction to data science as an academic discipline. The special feature of this text is that it does so by focusing on the skills and principles needed to design systems for collecting, analyzing, and interpreting data. (P. Navrat, Computing Reviews, February, 23, 2018)


“The book is more than a typical manual. In 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) ​


The book is more than a typical manual. In 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)


Author Information

Dr. Steven S. Skiena is Distinguished Teaching Professor of Computer Science at Stony Brook University, with research interests in data science, natural language processing, and algorithms. He was awarded the IEEE Computer Science and Engineering Undergraduate Teaching Award “for outstanding contributions to undergraduate education ...and for influential textbooks and software.”  Dr. Skiena is the author of six books, including the popular Springer titles The Algorithm Design Manual and Programming Challenges: The Programming Contest Training Manual.

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