The Data Science Design Manual (Texts in Computer Science) by Steven S. Skiena (PDF)

Description

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 worldIncludes “Homework Problems,” providing a wide range of exercises and projects for self-studyProvides a complete set of lecture slides and online video lectures at www.data-manual.comProvides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapterRecommends exciting “Kaggle Challenges” from the online platform KaggleHighlights “False Starts,” revealing the subtle reasons why certain approaches failOffers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)

User’s Reviews

Editorial Reviews: Review “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) ​ About the Author 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.

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Once you’ve brushed up on College AP Stats, this is a reeeally engaging book which goes in good detail but not too much detail. I enjoyed reading the specific examples, plots and war stories. Definitely a must read!

⭐BEWARE! DO NOT BUY THE KINDLE VERSION! It is broken, you cannot read bulleted lists or formulas. Very poor!The book is great otherwise and covers the landscape of data science with good story telling that you do not normally find with other books. It feels like youre having a conversation with Skiena as he guides you.

⭐I like how things are broken down which causes me to further research and learn.

⭐I am very pleased with the presentation of material in this book. Currently I am reading the review section on Linear Algebra and (having taken a course in the subject in the past) I have found many nice intuitions which were never presented in a more formal and comprehensive treatment.I am sure that I will continue to find such illuminating expositions on the remainder of the topics in this book.I am looking forward to the class next semester at SBU taught by the author which I am enrolled in.

⭐I love this book.This book is for beginners in the field. It well organized with many examples, intuition explanations and without math proofs and code examples

⭐No complain

⭐Is the paper back or hard cover colored?

⭐The analogies presented by Skiena help the reader grasp the bigger picture and be able to understand the purpose of many difficult topics.

⭐Gives a well rounded introduction and overview of data science but slightly dated in parts. The treatment of deep learning was a bit light.

⭐Whether you are merely curious about data science or are a studying data science at college or university, “The Data Science Design Manual” lays out a road map to carrying out data analysis, data visualization, and machine learning at a level easily understandable. This book provides the reader-practitioner with a solid foundation upon which to build their skills. If I was designing a data science or data analytics curriculum or a single introductory course, I would choose this book as a guide.

⭐Inhaltlich einigermaßen vollständiger Überblick über das Thema, Genaueres muss man andernorts nachlesen. Insgesamt meiner Meinung nach weniger ambitioniert und weniger gut gelungen als das “Algorithm Design Manual” des gleichen Autors. Einige Nachlässigkeiten in Sprache und Satz haben sich eingeschlichen, für eine Erstauflage aber nicht schlimm. Die ergänzende Video-Reihe ist gut gelungen.I have read this book online, and I ordered my own copy. This is an excellent introductory book that covers diverse topics on data science and explains the general problems that the domain looks into. Very well-written!

⭐The book is well written and provides a good introduction to data science at an undergrad level. However, this book is short on mathematical definitions.

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