| 000 | 02235nam a22002057a 4500 | ||
|---|---|---|---|
| 999 |
_c4124 _d4124 |
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| 005 | 20221205124419.0 | ||
| 008 | 221205b ||||| |||| 00| 0 eng d | ||
| 020 | _a9781108472449 | ||
| 082 |
_a004 _bSHA |
||
| 100 |
_aShah, Chirag _99325 |
||
| 245 | _aA hands-on introduction to data science | ||
| 260 |
_bCambridge University Press _aUK _c2021 |
||
| 300 | _axxiii, 433 p. | ||
| 365 |
_aGBP _b39.99 |
||
| 504 | _aFrontmatter Part I: - Conceptual Introductions pp 1-2 1 - Introduction pp 3-36 2 - Data pp 37-65 3 - Techniques pp 66-96 Part II: - Tools for Data Science pp 97-98 4 - UNIX pp 99-124 5 - Python pp 125-160 6 - R pp 161-186 7 - MySQL pp 187-206 Part III: - Machine Learning for Data Science pp 207-208 8 - Machine Learning Introduction and Regression pp 209-234 9 - Supervised Learning pp 235-289 10 - Unsupervised Learning pp 290-318 Part IV: - Applications, Evaluations, and Methods pp 319-320 11 - Hands-On with Solving Data Problems pp 321-353 12 - Data Collection, Experimentation, and Evaluation pp 354-378 Appendices pp 379-417 Index | ||
| 520 | _aDescription This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science. | ||
| 650 |
_aComputer science _91018 |
||
| 650 |
_aInformation technology _9827 |
||
| 942 |
_2ddc _cBK |
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