Wiley Series on Methods and Applications in Data Mining) 1st Edition
by Chantal D. Larose (Author), Daniel T. Larose (Author)
Learn data science by doing data science!
Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R.
Data science is hot. Bloomberg called
data scientist “the hottest job in America.” Python and R are the top
two open-source data science tools in the world. In Data Science Using Python and R,
you will learn step-by-step how to produce hands-on solutions to
real-world business problems, using state-of-the-art techniques.
Data Science Using Python and R
is written for the general reader with no previous analytics or
programming experience. An entire chapter is dedicated to learning the
basics of Python and R. Then, each chapter presents step-by-step
instructions and walkthroughs for solving data science problems using
Python and R.
Those with analytics experience will appreciate
having a one-stop shop for learning how to do data science using Python
and R. Topics covered include data preparation, exploratory data
analysis, preparing to model the data, decision trees, model evaluation,
misclassification costs, naïve Bayes classification, neural networks,
clustering, regression modeling, dimension reduction, and association
rules mining.
Further, exciting new topics such as random forests
and general linear models are also included. The book emphasizes
data-driven error costs to enhance profitability, which avoids the
common pitfalls that may cost a company millions of dollars.
Data Science Using Python and R
provides exercises at the end of every chapter, totaling over 500
exercises in the book. Readers will therefore have plenty of opportunity
to test their newfound data science skills and expertise. In the
Hands-on Analysis exercises, readers are challenged to solve interesting
business problems using real-world data sets.
