TY  - BOOK
AU  - Steele,Brian
AU  - Chandler,John
AU  - Reddy,Swarna
TI  - Algorithms for data science
SN  - 9783319457956
U1  - 006.312 23
PY  - 2016///
CY  - Cham, Switzerland : 
PB  - Springer, 
KW  - Data mining
KW  - Quantitative research
N1  - 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
N2  - 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
ER  -