Chapter 1 Welcome

Figure 1.1: In this book, you will become the business analyst who travels between the real-world of business and the powerful world of mathematics and computers. You will translate real-world decisions into representations where the math/computer world helps discover and visualize insight to improve real-world decisions

This book provides a hands-on journey to becoming proficient in the thinking processes, mathematics, and computing associated with modern business analytics workflows. Highlights include:

Figure 1.2: Buy a beautifully printed in-color version of “A Business Analyst’s Guide to Business Analytics” on Amazon: http://www.amazon.com/dp/B08DBYPRD2.

• First textbook addressing a complete business analytics workflow including data manipulation, data visualization, modelling business problems with graphical models, translating graphical models into code, and presenting insights back to stakeholders.
• Content is accessible to most analytics beginners. If you have taken a stats course, you will benefit from this book.
• Assumes no prior knowledge of any software and introduces readers to R, RStudio, and the Tidyverse.
• Provides a complete workflow within the R-ecosystem; there is no need to learn several programming languages.
• Provides a solid foundation for anyone wanting to learn Bayesian inference, but intimidated by other texts.
• 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.
• Written by a professor who wins teaching awards and 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.

At its heart, this is a Bayesian business analytics textbook made feasible by recent advances in Bayesian computing.\(^{**}\) ⊕** Most notably the use of better sampling techniques using adaptations of something known as Hamiltonian Markov Chain Monte Carlo (HMCMC). Using Bayesian inference is the provably best method of combining data with domain knowledge to extract interpretable and insightful results that lead us towards better outcomes. In my opinion, this is what you need to learn as a business analyst.

To use some of the datasets and functions that accompany this book, you will eventually need to install the causact R package (if you do not know how to do this yet, no worries … you will learn how to run the below line of code using RStudio in the introductory chapters):

install.packages("causact")

You will most likely use this package with greta (an R package) and TensorFlow (a free and open-source software library). Properly installing causact, greta, and Tensorflow can be done following the instructions here: https://www.causact.com/install-tensorflow-greta-and-causact.html.

If you want to jump to causact package specifics, start here: https://www.causact.com/causact-quick-inference-with-generative-dags.html#causact-quick-inference-with-generative-dags.

Please note that this work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (https://creativecommons.org/licenses/by-nc-nd/4.0/).

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and consider buying the printed version on Amazon. See it here: http://www.amazon.com/dp/B08DBYPRD2

A Business Analyst’s Introduction to Business Analytics by Adam Fleischhacker is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.