The Skiena, Steven S. creator SpringerLink (Online service) text gw Cham, Switzerland Springer c2017 2017 monographic eng xvii, 445 pages : illustrations ; 24 cm 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 (3)4z(BIntroduction to Data Science(3)4y (Bcourse. 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 (3)4z(BWar Stories,(3)4y (Boffering perspectives on how data science applies in the real world Includes (3)4z(BHomework Problems,(3)4y (Bproviding 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 (3)4z(BTake-Home Lessons,(3)4y (Bemphasizing the big-picture concepts to learn from each chapter Recommends exciting (3)4z(BKaggle Challenges(3)4y (Bfrom the online platform Kaggle Highlights (3)4z(BFalse Starts,(3)4y (Brevealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show (3)4z(BThe Quant Shop(3)4y ((Bwww.quant-shop.com). 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. Steven S. Skiena. License restrictions may limit access. CSE Computer science Big data Data mining Pattern recognition Mathematics Visualization Statistics Computer Science Data Mining and Knowledge Discovery Pattern Recognition Big Data/Analytics Visualization Statistics and Computing/Statistics Programs Mathematical statistics Data processing QA76.9.D343 006.312 9783319554433 (print) 9783319554440 Ayesha Abed Library License restrictions may limit access. 191118 20191208162458.0 36614 eng