Chapter 1 Welcome

Figure 1.1: Buy a beautifully printed in-color version of “A Business Analyst’s Guide to Business Analytics(2e)” on Amazon: http://www.amazon.com/dp/B0CFZMKRGX.

There are three languages at the core of any data-driven business analysis. I label the three languages narrative, math, and code:

Narrative: This is the language of your real-world understanding, the story of you, your business challenges, and possible ways to improve the future.
Math: Hopefully, this is the language describing a faithful mathematical representation of your real-world narrative whose solution or output might turn your data into data-driven insight.
Code: This is your programming language, the code required to generate output that exactly or approximately solves your math problem or generates your desired output.

Unifying narrative, math, and code is the objective of any good business analyst and it is my goal with this book to train your mind to think this way.

While numerous other data analysis books may trap you in intricate technical details and pitfalls of mathematics and computation, this beginner’s textbook prioritizes boosting your confidence to handle these complexities in due time. Within this text, we maintain a continuous flow between real-world situations and mathematical concepts, honing the ability to traverse the analytics bridge illustrated in Figure 1.2. Although it places less emphasis on technical intricacies, this book significantly concentrates on skillfully connecting mathematical principles and coding with real-life situations. This equips you to collaborate seamlessly with business experts and champion enhanced decisions and actions in practical contexts.

Figure 1.2: When digesting this book, strive to become the business analyst who easily travels between the real-world of business and the theoretical world of mathematics. Learn to translate real-world scenarios into both mathematical and computational representations that yield actionable insight. Plan to astound your stakeholders with insights that alter and improve their real-world decisions.

When digesting this book, strive to become the business analyst who easily travels between the real-world of business and the theoretical world of mathematics. Learn to translate real-world scenarios into both mathematical and computational representations that yield actionable insight. Plan to astound your stakeholders with insights that alter and improve their real-world decisions.

By learning to program in R, manipulate and visualize data in the tidyverse, and model business scenarios in the visual language of Bayesian inference with causact, you will proficiently articulate and unlock insightful narratives utilizing data, models, and visualization. In essence, you will become a skilled translator proficiently conversant in narrative, math, and code.

Thank you for investing your time in this book and I look forward to being on this journey with you.

1.1 About The Author and This Textbook

Written by me, Dr. Adam Fleischhacker, award-winning professor, software designer, researcher, and industry consultant. I wrote this guide to help you become a data-driven business analyst who excels at compelling action and creating value. On your journey to becoming a world-class business analyst, here are some highlights of what you will encounter using this textbook:

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.
Lessons covering a complete business analytics workflow to help you learn to unify narrative, math, and code.
Introduction to the R programming language starting with data manipulation and data visualization from the tidyverse package (Wickham (⊕2017Wickham, Hadley. 2017. Tidyverse: Easily Install and Load the ’Tidyverse’. https://CRAN.R-project.org/package=tidyverse.)).
A non-intimidating and gentle approach to learning computational Bayesian inference and Bayesian data analysis.
A single interface to a complete Bayesian-based analytics workflow within the R-ecosystem; there is no need to learn several programming languages.
First textbook using causact (Fleischhacker and Nguyen (⊕2022Fleischhacker, Adam J., and Thi Hong Nhung Nguyen. 2022. “Generative DAGs as an Interface into Probabilistic Programming with the r Package Causact.” The Journal of Open Source Software 7 (76): 4415. https://doi.org/10.21105/joss.04415.)), an interactive and visual R-based interface to Python’s numpyro package (Phan, Pradhan, and Jankowiak (⊕2019Phan, Du, Neeraj Pradhan, and Martin Jankowiak. 2019. “Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro.” arXiv Preprint arXiv:1912.11554.)) for very fast Bayesian inference.
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/).
The perspective of a teaching-award winning analytics professor who has had a successful corporate career in analytics and software product management.
All datasets in the book are free and easily accessed.
Cloud computing options using Posit Cloud make all code in this book available to anyone with internet browser access.

1.2 Accompanying Videos and Online Materials

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. Some of the older videos which accompanied the first edition of this book use library(greta) and dag_greta() functions; when you see these commands, just delete the library(greta) command and replace dag_greta() with dag_numpyro(). To get the videos, 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/).

1.3 Notes About This Book

At its heart, this is an introduction to R accompanied by an introduction to the math of data science through the lens of computational Bayesian inference.\(^{**}\) ⊕** Computational Bayesian inference is only recently feasible due to better sampling techniques using adaptations of 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 the datasets and functions that accompany this book, readers should install the causact R package and some Python dependencies. The installation process is typically very smooth and additional information about the package and its installation can be found here in subsequent chapters and also here: https://github.com/flyaflya/causact.

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.