The Data Science Design Manual

Name: The Data Science Design Manual
Brand: Springer
SKU: 9783319856636
Price: 4366.0 INR
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by Steven S. Skiena

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ISBN13: 9783319856636
Binding: Paperback
Subject: N/A
Publisher: Springer
Publisher Imprint: Springer
Publication Date: August 3, 2018
Pages: 445
Original Price: EUR 59.99
Language: English
Edition: Softcover Repri
Item Weight: 968 grams
BISAC Subject(s): Data Science / Data Analytics, Artificial Intelligence / Computer Vision & Pattern Recognition, and Industries / Computers & Information Technology

About The Book

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)

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.

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