000			02589nam a22002537a 4500
003			ISI Library, Kolkata
005			20260211020018.0
008			250428b |||||||| |||| 00| 0 eng d
020			_a9789352138326
040			_aISI Library _bEnglish
082	0	4	_223rd _a005.7 _bG892
100	1	0	_aGrus, Joel
245	1	0	_aData science from scratch: _bfirst principles with Python/ _cJoel Grus
250			_a2nd
260			_aBeijing: _bO'Reilly, _c2019
300			_axvii, 384 p. : _bgraphs, illustration; _c23 cm.
504			_aIncludes index
505	0		_a1. Introduction -- 2. A crash course in python -- 3. Visualizing data -- 4. Linear algebra -- 5. Statistics -- 6. Probability -- 7. Hypothesis and inference -- 8. Gradient descent -- 9. Getting data -- 10. Working with data -- 11. Machine learning -- 12. k-Nearest neighbors -- 13. Naive Bayes -- 14. Simple linear regression -- 15. Multiple regression -- 16. Logistic regression -- 17. Decision trees -- 18. Neural networks -- 19. Deep learning -- 20. Clustering -- 21. Natural language processing -- 22. Network analysis -- 23. Recommender systems -- 24. Databases and SQL -- 25. MapReduce -- 26. Data ethics -- 27. Go forth and do data science -- Index
520			_aTo really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
650		4	_aComputer Science
650		4	_aData Science
650		4	_aPython
942			_2ddc _cBK _05
999			_c437004 _d437004