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The data science design manual

By: Skiena, Steven S.
Material type: materialTypeLabelBookSeries: Texts in Computer Science. Publisher: Cham Springer 2017Description: xvii, 445 p. Includes bibliographical references and index.ISBN: 9783319554433.Subject(s): Mathematical statistics - Data processing | Pattern perception | Visualization | Quantitative research | Big dataDDC classification: 519.50285 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 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. https://www.springer.com/gp/book/9783319554433
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Item type Current location Item location Collection Call number Status Date due Barcode
Books Vikram Sarabhai Library
General Stacks
Slot 1419 (0 Floor, East Wing) Non-fiction 519.50285 S5D2 (Browse shelf) Checked out 23/12/2019 200256

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
Coda

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.

https://www.springer.com/gp/book/9783319554433

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