Algorithms for data science / Brian Steele, John Chandler and Swarna Reddy .
Material type:
TextPublication details: Cham, Switzerland : Springer, ©2016.Description: xxiii, 430 pages : illustrations (some color) ; 25 cmISBN: - 9783319457956
- 006.312 23 St814
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 006.312 St814 (Browse shelf(Opens below)) | Available | 138342 |
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| 006.312 Si611 Mathematical tools for data mining : | 006.312 Si611 Mathematical tools for data mining : | 006.312 Si611 Mathematical tools for data mining : | 006.312 St814 Algorithms for data science / | 006.312 T164 Exploring advances in interdisciplinary data mining and analytics : | 006.312 T215 Data mining applications using artificial adaptive systems / | 006.312 T682 Data mining with R : |
includes bibliographical references and index.
1. Introduction --
2. Data Mapping and Data Dictionaries --
3. Scalable Algorithms and Associative Statistics --
4. Hadoop and MapReduce --
5. Data Visualization --
6. Linear Regression Methods --
7. Healthcare Analytics --
8. Cluster Analysis --
9. k-Nearest Neighbor Prediction Functions --
10. The Multinomial Naive Bayes Prediction Function --
11. Forecasting --
12. Real-time Analytics.
This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses....This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.
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