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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Main Authors: | |
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Corporate Authors: | |
Published: |
Springer International Publishing : Imprint: Springer,
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Publisher Address: | Cham : |
Publication Dates: | 2017. |
Literature type: | eBook |
Language: | English |
Series: |
Texts in Computer Science,
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Subjects: | |
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. |