000			01949nam a22002417a 4500
999			_c3867 _d3867
005			20221206142442.0
008			221206b ||||| |||| 00| 0 eng d
020			_a9781484240588
082			_a001.42 _bMAI
100			_aMailund, Thomas _99088
245			_aBeginning data science in R: _bdata analysis, visualization, and modelling for the data scientist
260			_bA Press  _aNew York _c2021
300			_axxvii, 352 p.
365			_aINR _b999.00
520			_aDiscover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code Specifications
650			_aQuantitative research _95213
650			_aR (Computer program language) _91512
650			_aComputer software--Development _910671
650			_aBig data _9212
650			_aProgramming languages (Electronic computers) _9838
650			_aData mining _9365
942			_2ddc _cBK