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The Data Science Design Manual /

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...

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Bibliographic Details
Main Authors: Skiena, Steven S
Corporate Authors: SpringerLink Online service
Published: Springer International Publishing : Imprint: Springer,
Publisher Address: Cham :
Publication Dates: 2017.
Literature type: eBook
Language: English
Series: Texts in Computer Science,
Subjects:
Computer science
Big data
Data mining
Pattern Recognition
Mathematics
Visualization
Statistics
Data Mining and Knowledge Discovery
Big Data/Analytics
Statistics and Computing/Statistics Programs
Online Access: http://dx.doi.org/10.1007/978-3-319-55444-0
Summary: 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 da
Carrier Form: 1 online resource (XVII, 445 pages): illustrations.
ISBN: 9783319554440
Index Number: QA76
CLC: TP311.13
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.

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