This workbook is intended for business analysts who wish to improve their skills in creating data visuals, presentations, and report illustrations used to support business decisions. It is a qualitative lab to develop the power of visualization and discrimination. It does not require the reader to modify charts, but to analyze and describe what would improve charts. In a set of controlled exercises, the reader is taken through the eighteen elements of six dimensions of analyzing and improving charts, visuals and reports used to communicate business concepts. Includes companion files with videos, sample files, and slides used in examples from the book. Features: Includes eighteen labs, three for each of the six major dimensions of data visuals: Story, Signs, Purpose, Perception, Method,and Charts Uses a comprehensive RAIKS (Rapid Assessment of Individual Knowledge and Skills) survey to judge readers' progress before and after using the text Provides a capstone exercise to review the aggregate analysis and final results for the two analyzed charts Companion files that include video tutorials and all of the sample files and templates used in the book's examples

This laboratory manual is intended for business analysts who wish to increase their skills in the use of statistical analysis to support business decisions. Most of the case studies use Excel,today's most common analysis tool. They range from the most basic descriptive analytical techniques to more advanced techniques such as linear regression and forecasting. Advanced projects cover inferential statistics for continuous variables (t-Test) and categorical variables (chi-square), as well as A/B testing. The manual ends with techniques to deal with the analysis of text data and tools to manage the analysis of large data sets (Big Data) using Excel. Includes companion files with solution spreadsheets, sample files, data sets, etc. from the book. Features: Teaches the statistical analysis skills needed to support business decisions Provides projects ranging from the most basic descriptive analytical techniques to more advanced techniques such as linear regression, forecasting, inferential statistics, and analyzing big data sets Includes companion files with solution spreadsheets, sample files, data sets, etc. used in the book's case studies

With the rise in data science development, we now have many remarkable techniques and tools to extend data analysis from numeric and categorical data to textual data. Sifting through the open-ended responses from a survey, for example, was an arduous process when performed by hand. Using a case study approach, this book was written for business analysts who wish to increase their skills in extracting answers for text data in order to support business decision making. Most of the exercises use Excel, today's most common analysis tool, and R, a popular analytic computer environment. The techniques covered range from the most basic text analytics, such as key word analysis, to more sophisticated techniques, such as topic extraction and text similarity scoring. Companion files with numerous datasets are included for use with case studies and exercises. FEATURES: Organized by tool or technique, with the basic techniques presented first and the more sophisticated techniques presented later Uses Excel and R for datasets in case studies and exercises Features the CRISP-DM data mining standard with early chapters for conducting the preparatory steps in data mining Companion files with numerous datasets and figures from the text