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.
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.
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:
greta
, an R interface to TensorFlow
for Bayesian inference, and the causact
package for visual model development.Videos that help the material come to life are available on my YouTube channel. Each chapter’s video should be watched after reading and coding along with the textbook. Search for Adam Fleischhacker on YouTube or follow this link directly: https://www.youtube.com/playlist?list=PLassxuIVwGLPy-mtohX-NXrjD8fc9FBOc. You can copy and paste code from the online version of the textbook (https://causact.com/).
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 students need to learn to be clear-thinking and capable business analysts.
To use some of the datasets and functions that accompany this book, readers will eventually need to install the causact
R package and its dependencies (this installation process is covered in Chapter 15). Datasets and graph visualization do not require the dependencies and just require one to run this line:
install.packages("causact")
However, to use advanced functionality this package should be installed 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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A Business Analyst’s Introduction to Business Analytics by Adam Fleischhacker is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.