The data science design manual / Steven S. Skiena.
Material type: TextPublisher: New York, NY : Springer Berlin Heidelberg, 2017Description: pages cmContent type:- text
- computer
- online resource
- 9783319554433
Item type | Current library | Call number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|
E-Resources | Main Library E-Resources | 519.50285 Sk628 (Browse shelf(Opens below)) | Available | E000586 |
Browsing Main Library shelves, Shelving location: E-Resources Close shelf browser (Hides shelf browser)
No cover image available No cover image available | No cover image available No cover image available | |||||||
519.50246 M214 Introductory Business Statistics with Interactive Spreadsheets | 519.502465 T563 Introductory business statistics | 519.50285 H465 Statistical analysis and data display : an intermediate course with examples in R / | 519.50285 Sk628 The data science design manual / | 519.50285 Z96 A beginner's guide to R | 519.502855133 Ab138 Data manipulation with R : efficiently perform data manipulation using the split-apply-combine strategy in R | 519.502855133 D142 Introductory statistics with R |
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).
There are no comments on this title.