000			01925nam a2200241Ia 4500
003			NULRC
005			20250520103029.0
008			250520s9999 xx 000 0 und d
020			_a9781492041139
040			_cNULRC
050			_aQA 76.73.P98 .G78 2019
100			_aGrus, Joel _eauthor
245		0	_aData science from scratch : _bfirst principles with python / _cJoel Grus
250			_aSecond Edition.
260			_aSebastopol, California : _bO'Reilly Media, Incorporated, _cc2019
300			_axvii, 384 pages : _billustrations ; _c24 cm.
365			_bUSD53
504			_aIncludes index.
505			_aIntroduction -- A crash course in Python -- Visualizing data -- Linear algebra -- Statistics -- Probability -- Hypothesis and inference -- Gradient descent -- Getting data -- Working with data -- Machine learning -- k-Nearest neighbors -- Naive bayes -- Simple linear regression -- Multiple regression -- Logistic regression -- Decision trees -- Neural networks -- Deep learning -- Clustering -- Natural language processing -- Network analysis -- Recommender systems -- Databases and SQL -- MapReduce -- Data ethics -- Go forth and do data science.
520			_aData science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. With this updated second edition, you'll learn how many of the most fundamental data science 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 hacking skills you need to get started as a data scientist. Today's messy glut of data holds answers to questions no one's even thought to ask.
650			_aDATA MINING
942			_2lcc _cBK
999			_c21793 _d21793