Chapter 1 Welcome

Figure 1.1: In this book, you will become the business analyst who easily travels between the real-world of business and the theoretical world of mathematics. You will translate real-world scenarios into both mathematical and computational representations that yield actionable insight. You will then take that insight back to the real-world to persuade stakeholders to alter and improve their real-world decisions.

This textbook goes farther than just teaching you to make computational models using software or mathematical models using statistics. It teaches you how to align computational and mathematical models with real-world scenarios; empowering you to communicate with and leverage the expertise of business stakeholders while using modern software stacks and statistical workflows. In this book, you do not learn business analytics to make models; you learn business analytics to add tangible value in the real-world.

Written by me, Dr. Adam Fleischhacker, award-winning professor, software designer, researcher, and industry-active analytics consultant. I wrote this guide to accelerate your journey mastering the data-driven business analyst workflow. On your journey to becoming a world-class business analyst, here are some highlights of what you will encounter using this textbook:

• Lessons covering a complete business analytics workflow using the R programming language including data manipulation, data visualization, modelling business problems with graphical models, translating graphical models into code, and presenting insights back to stakeholders.
• Content that is accessible to analytics beginners. If you have taken a stats course, you will benefit from this book. The book assumes no prior knowledge of software and introduces readers to the proper toolkit for business analytics including R, RStudio, and the tidyverse.
• A single interface to a complete analytics workflow within the R-ecosystem; there is no need to learn several programming languages.
• A non-intimidating and gentle approach to learning Bayesian inference and Bayesian data analysis.
• First textbook using greta, an R interface to TensorFlow for Bayesian inference, and the causact package for visual model development.
• Code to reproduce all results and almost all visualizations is included right in the text. You can copy and paste the code from the online version (https://causact.com/).
• A teaching-award winning analytics professor’s perspective who has had a successful corporate career in analytics and software product management.
  
• All datasets in the book are freely and easily accessed.
• Cloud computing options freely available for those who are limited to internet browser access only.