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 predictivetechniques, 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.
