Practical statistics for data scientists : 50+ essential concepts using R and…

Edition

Second edition

ISBN/ISSN

9781492072942

Collation

xvi, 342 p. : ill. : ind. ; 24 cm.

Series Title

-

Call Number

001.422 Bru p

Beginning data science in R : data analysis, visualization, and modelling for…

Edition

-

ISBN/ISSN

9781484226704

Collation

xxvii, 352 p. : ill. : ind. ; 26 cm.

Series Title

-

Call Number

001.42 Mai b

Using R at the bench : step-by-step data analytics for biologists

Edition

-

ISBN/ISSN

9781621821120

Collation

ix, 188 p. : ill. : ind. ; 24 cm.

Series Title

-

Call Number

570.285 Bre u

Statistics and data analysis for microarrays using R and Bioconductor

Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that teaches students th…

Bioinformatics with R cookbook : over 90 practical recipes for computational …

This book provides a number of recipes offering you solutions to all the computational tasks related to bioinformatics in terms of packages and testes codes.

The R book

Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carry…

Statistical bioinformatics with R

Designed for a one or two semester senior undergraduate or graduate bioinformatics course, Statistical Bioinformatics takes a broad view of the subject - not just gene expression and sequence analysis, but a careful balance of statistical theory in the context of bioinformatics applications. The inclusion of R code as well as the development of advanced methodology such as Bayesian and Markov …