000 01978nam a22002057a 4500
008 220826b2020 |||||||| |||| 00| 0 eng d
020 _a9788194435006
082 _a001.422 BRU-P
100 _aBruce, Peter
245 _aPractical statistics for data scientists :
_b50+ essential concepts using R and Python /
_cPeter Bruce, Andrew Bruce and Peter Gedeck
260 _aIndia
_bShroff Publishers
_c2020
300 _a342 p.
365 _aINR
_b1475.00.
500 _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 format. 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
650 _aStatistics--Data processing
650 _aPython (Computer program language)
650 _aR (Computer program language)
650 _aQuantitative research
999 _c80375
_d80375