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Data Science, Analytics and Machine Learning with R explains the
principles of data mining and machine learning techniques and
accentuates the importance of applied and multivariate modeling.
The book emphasizes the fundamentals of each technique, with
step-by-step codes and real-world examples with data from areas
such as medicine and health, biology, engineering, technology and
related sciences. Examples use the most recent R language syntax,
with recognized robust, widespread and current packages. Code
scripts are exhaustively commented, making it clear to readers what
happens in each command. For data collection, readers are
instructed how to build their own robots from the very beginning.
In addition, an entire chapter focuses on the concept of spatial
analysis, allowing readers to build their own maps through
geo-referenced data (such as in epidemiologic research) and some
basic statistical techniques. Other chapters cover ensemble and
uplift modeling and GLMM (Generalized Linear Mixed Models)
estimations, both linear and nonlinear.
Data Science for Business and Decision Making covers both
statistics and operations research while most competing textbooks
focus on one or the other. As a result, the book more clearly
defines the principles of business analytics for those who want to
apply quantitative methods in their work. Its emphasis reflects the
importance of regression, optimization and simulation for
practitioners of business analytics. Each chapter uses a didactic
format that is followed by exercises and answers. Freely-accessible
datasets enable students and professionals to work with Excel,
Stata Statistical Software (R), and IBM SPSS Statistics Software
(R).
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