Data Science

This book offers solutions to all 284 exercises in Advanced R, Second Edition. All the solutions have been carefully documented and made to be as clear and accessible as possible.

The guide to targeting and leveraging business opportunities using big data & analytics .

Beginning R, Second Edition is a hands-on book showing how to use the R language, write and save R scripts, read in data files, and write custom statistical functions as well as use built-in functions.

This book presents papers from the International Research Workshop, Advanced High Performance Computing Systems, held in Cetraro, Italy, in July 2014. The papers selected for publication here discuss fundamental aspects of the definition of Big Data, as well as considerations from practice where complex datasets are collected, processed and stored.

Integrate big data into business to drive competitive advantage and sustainable success.

A practical guide to data mining using SQL and Excel; shows you how to leverage the two most popular tools for data query and analysis.

A uniquely practical guide to increasing sales success, using the power of data analytics.

Author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code.

A pragmatic approach to Big Data by taking the reader on a journey between Big Data (what it is) and the Smart Data (what it is for).

This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python, ranging from simple hypothesis testing to advanced multivariate modelling.

Brings advanced analytics into Human Resources, giving you a framework for optimizing human capital investments through predictive analysis.

Goes beyond general Spark overviews to provide targeted guidance toward using lightning-fast big-data clustering in production.

Become competent at implementing regression analysis in Python. Solve some of the complex data science problems related to predicting outcomes.

This book discusses advanced topics such as R core programing, object oriented R programing, parallel computing with R, and spatial data types.

Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery  A comprehensive introduction to statistical methods for data mining and knowledge discovery