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The Data Science Design Manual
Buch von Steven S. Skiena
Sprache: Englisch

73,80 €*

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Beschreibung
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 [...]

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¿ ([...]
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 [...]

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¿ ([...]
Über den Autor

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.

Zusammenfassung

Provides an introduction to data science, focusing on the fundamental skills and principles needed to build systems for collecting, analyzing, and interpreting data

Lays the groundwork of what really matters in analyzing data; 'doing the simple things right'

Aids the reader in developing mathematical intuition, illustrating the key concepts with a minimum of formal mathematics

Highlights the core values of statistical reasoning using the approaches which come most naturally to computer scientists

Includes supplementary material: [...]

Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2017
Genre: Informatik, Mathematik, Medizin, Naturwissenschaften, Technik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Reihe: Texts in Computer Science
Inhalt: xvii
445 S.
43 s/w Illustr.
137 farbige Illustr.
445 p. 180 illus.
137 illus. in color.
ISBN-13: 9783319554433
ISBN-10: 3319554433
Sprache: Englisch
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Skiena, Steven S.
Hersteller: Springer International Publishing
Springer International Publishing AG
Texts in Computer Science
Verantwortliche Person für die EU: Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de
Maße: 260 x 183 x 29 mm
Von/Mit: Steven S. Skiena
Erscheinungsdatum: 29.08.2017
Gewicht: 1,168 kg
Artikel-ID: 109684291
Über den Autor

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.

Zusammenfassung

Provides an introduction to data science, focusing on the fundamental skills and principles needed to build systems for collecting, analyzing, and interpreting data

Lays the groundwork of what really matters in analyzing data; 'doing the simple things right'

Aids the reader in developing mathematical intuition, illustrating the key concepts with a minimum of formal mathematics

Highlights the core values of statistical reasoning using the approaches which come most naturally to computer scientists

Includes supplementary material: [...]

Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2017
Genre: Informatik, Mathematik, Medizin, Naturwissenschaften, Technik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Reihe: Texts in Computer Science
Inhalt: xvii
445 S.
43 s/w Illustr.
137 farbige Illustr.
445 p. 180 illus.
137 illus. in color.
ISBN-13: 9783319554433
ISBN-10: 3319554433
Sprache: Englisch
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Skiena, Steven S.
Hersteller: Springer International Publishing
Springer International Publishing AG
Texts in Computer Science
Verantwortliche Person für die EU: Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de
Maße: 260 x 183 x 29 mm
Von/Mit: Steven S. Skiena
Erscheinungsdatum: 29.08.2017
Gewicht: 1,168 kg
Artikel-ID: 109684291
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