With many recent advances in data science we have many more tools and techniques available for
data analysts to extract information from data sets. This book will assist data analysts to
move up from simple tools such as Excel for descriptive analytics to answer more sophisticated
questions using machine learning. Most of the exercises use R and Python but rather than focus
on coding algorithms the book employs interactive interfaces to these tools to perform the
analysis. Using the CRISP-DM data mining standard the early chapters cover conducting the
preparatory steps in data mining: translating business information needs into framed analytical
questions and data preparation. The Jamovi and the JASP interfaces are used with R and the
Orange3 data mining interface with Python. Where appropriate Voyant and other open-source
programs are used for text analytics. The techniques covered in this book range from basic
descriptive statistics such as summarization and tabulation to more sophisticated predictive
techniques such as linear and logistic regression clustering classification and text
analytics. Includes companion files with case study files solution spreadsheets data sets and
charts etc. from the book. Features: Covers basic descriptive statistics such as
summarization and tabulation to more sophisticated predictive techniques such as linear and
logistic regression clustering classification and text analytics Uses R Python Jamovi and
JASP interfaces and the Orange3 data mining interface Includes companion files with the case
study files from the book solution spreadsheets data sets etc.
