The Data Science Design Manual
tarafından
 
Skiena, Steven S. author.

Başlık
The Data Science Design Manual

Yazar
Skiena, Steven S. author.

ISBN
9783319554440

Yazar
Skiena, Steven S. author.

Edisyon
1st ed. 2017.

Fiziksel Niteleme
XVII, 445 p. 180 illus., 137 illus. in color. online resource.

Seri
Texts in Computer Science,

İçindekiler
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.

Özet
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. 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).

Konu Başlığı
Data mining.
 
Optical pattern recognition.
 
Big data.
 
Visualization.
 
Mathematical statistics.
 
Data Mining and Knowledge Discovery. http://scigraph.springernature.com/things/product-market-codes/I18030
 
Pattern Recognition. http://scigraph.springernature.com/things/product-market-codes/I2203X
 
Big Data/Analytics. http://scigraph.springernature.com/things/product-market-codes/522070
 
Visualization. http://scigraph.springernature.com/things/product-market-codes/M14034
 
Statistics and Computing/Statistics Programs. http://scigraph.springernature.com/things/product-market-codes/S12008

Ek Kurum Yazar
SpringerLink (Online service)

Elektronik Erişim
https://doi.org/10.1007/978-3-319-55444-0


Materyal TürüBarkodYer NumarasıDurumu/İade Tarihi
Electronic Book223455-1001QA76.9 .D343Springer E-Book Collection