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Brian Steele - Algorithms for Data Science - 9783319457956 - V9783319457956
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Algorithms for Data Science

€ 92.45
€ 91.73
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Description for Algorithms for Data Science Hardback. Num Pages: 430 pages, 18 black & white illustrations, 30 colour illustrations, biography. BIC Classification: UFM; UMB; UNA; UYA. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 25. Weight in Grams: 842.
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 ... Read more

Product Details

Publisher
Springer International Publishing AG
Format
Hardback
Publication date
2016
Condition
New
Number of Pages
430
Place of Publication
Cham, Switzerland
ISBN
9783319457956
SKU
V9783319457956
Shipping Time
Usually ships in 10 to 12 working days
Ref
99-15

About Brian Steele
Brian Steele is a full professor of Mathematics at the University of Montana and a Senior Data Scientist for SoftMath Consultants, LLC. Dr. Steele has published on the EM algorithm, exact bagging, the bootstrap, and numerous statistical applications. He teaches data analytics and statistics and consults on a wide variety of subjects related to data science and statistics. John Chandler ... Read more

Reviews for Algorithms for Data Science
“This 430-page book contains an excellent collection of information on the subject of practical algorithms used in data science. The discussion of each algorithm starts with some basic concepts, followed by a tutorial with real datasets and detailed code examples in Python or R. Each chapter has a set of exercise problems so readers can practice the concepts learned in ... Read more

Goodreads reviews for Algorithms for Data Science


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