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Data Science for Business with R, written by Jeffrey S. Saltz and
Jeffrey M. Stanton, focuses on the concepts foundational for
students starting a business analytics or data science degree
program. To keep the book practical and applied, the authors
feature a running case using a global airline business's customer
survey dataset to illustrate how to turn data in business
decisions, in addition to numerous examples throughout. To aid in
usability beyond the classroom, the text features full integration
of freely-available R and RStudio software, one of the most popular
data science tools available. Designed for students with little to
no experience in related areas like computer science, the book
chapters follow a logical order from introduction and installation
of R and RStudio, working with data architecture, undertaking data
collection, performing data analysis, and transitioning to data
archiving and presentation. Each chapter follows a familiar
structure, starting with learning objectives and background,
following the basic steps of functions alongside simple examples,
applying these functions to the case study, and ending with chapter
challenge questions, sources, and a list of R functions so students
know what to expect in each step of their data science course. Data
Science for Business with R provides readers with a straightforward
and applied guide to this new and evolving field.
An Introduction to Data Science is an easy-to-read, gentle
introduction for advanced undergraduate, certificate, and graduate
students coming from a wide range of backgrounds into the world of
data science. After introducing the basic concepts of data science,
the book builds on these foundations to explain data science
techniques using the R programming language and RStudio (R) from
the ground up. Short chapters allow instructors to group concepts
together for a semester course and provide students with manageable
amounts of information for each concept. By taking students
systematically through the R programming environment, the book
takes the fear out of data science and familiarizes students with
the environment so they can be successful when performing advanced
functions. The authors cover statistics from a conceptual
standpoint, focusing on how to use and interpret statistics, rather
than the math behind the statistics. This text then demonstrates
how to use data effectively and efficiently to construct models,
predict outcomes, visualize data, and make decisions. Accompanying
digital resources provide code and datasets for instructors and
learners to perform a wide range of data science tasks.
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