04292nam a22005655i 4500 36614 BD-DhAAL 20191208162458.0 191118t2017 gw | o |||| 0|eng d 41738 41738 9783319554433 (print) 9783319554440 10.1007/978-3-319-55444-0 doi (WaSeSS)OCM1ssj0001846412 10029592 (OCoLC)992989152 WaSeSS eng NIC BD-DhAAL QA76.9.D343 COM021030 bisacsh 006.312 23 Skiena, Steven S. 37243 The Data Science Design Manual / Steven S. Skiena. Cham, Switzerland : Springer, c2017 xvii, 445 pages : illustrations ; 24 cm Texts in Computer Science, 1868-0941 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. License restrictions may limit access. 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). CSE Trim Education 36614, 36615 Computer science. 37244 Big data. 37245 Data mining. 37246 Pattern recognition. 37247 Mathematics. 37248 Visualization. 37249 Statistics. 37250 Computer Science. 37251 Data Mining and Knowledge Discovery. 37252 Pattern Recognition. 37253 Big Data/Analytics. 37254 Visualization. 37249 Statistics and Computing/Statistics Programs. 37255 Mathematical statistics Data processing. fast 37256 SpringerLink (Online service) 37257 Texts in Computer Science, 37258 Ayesha Abed Library General Stacks ddc BK 0 0 ddc 0 006_312000000000000_SKI 0 67082 BRACUL BRACUL GEN 2019-11-06 Trim Education 3645.00 6 82 006.312 SKI 3010036614 2025-10-11 2024-12-15 1 3645.00 2019-11-06 BK 0 0 ddc 0 006_312000000000000_SKI 0 67083 BRACUL BRACUL GEN 2019-11-06 Trim Education 3645.00 6 96 006.312 SKI 3010036615 2026-04-12 2025-10-27 2 3645.00 2019-11-06 BK