000			02017 a2200229 4500
005			20251011132756.0
008			251011b |||||||| |||| 00| 0 eng d
020			_a9788194435006
082			_a001.4 _bBruP
100			_aBruce, Peter
245			_aPractical Statistics for Data Scientists : _b50+ essential concepts using R and Python / _cPeter Bruce , Andrew Bruce and Peter Gedeck
250			_a2nd ed.
260			_bSPD : _aNavi Mumbai , _c©2020.
300			_axvi, 342p.
520			_aStatistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable formate With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that "learn" from data Unsupervised learning methods for extracting meaning from unlabeled data
650			_aStatistics--Data processing
650			_aPython (Computer program language)
650			_aR (Computer program language)
700			_aGedeck, Peter
700			_aBruce, Andrew
942			_cBK
999			_c7456 _d7456