A Business Analyst's Introduction To Business Analytics Intro To Bayesian Business Analytics in the R Ecosystem

This textbook goes farther than just showing you how to make computational models using software or mathematical models using statistics. It guides your thinking so you can align computational and mathematical models with real-world scenarios. As you journey through the material, you will feel empowered to effectively collaborate with business stakeholders as you use modern software stacks and modern statistical workflows to discover insight. 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 award-winning professor, software designer, researcher, and industry consultant, Dr. Adam Fleischhacker, you will be led on a journey towards mastery of the business analyst workflow:

• manipulate and model data,
• collaborate with stakeholders using visualizations for data, decisions, models, and insights,
• and successfully advocate for change within an organization with interpretable models and persuasive visualizations.

Book DescriptionA frequent teaching-award winning professor with an analytics-industry background shares his hands-on guide to learning business analytics. It is the first textbook addressing a complete and modern business analytics workflow that includes data manipulation, data visualization, modelling business problems with graphical models, translating graphical models into code, and presenting insights back to stakeholders.Book Highlights

• This up-to-date business analytics textbook (initially published in July 2020 and updated for third printing in August 2021)
• Content that is accessible to anyone, even most analytics beginners. If you have taken a stats course, you are good to go.
• Assumes no knowledge of the R programming language. Provides introduction to R, RStudio, and the Tidyverse.
• Provides a solid foundation and an implementable workflow for anyone wading into the Bayesian inference waters.
• Provides a complete workflow within the R-ecosystem; there is no need to learn several programming languages or work through clunky interfaces between software tools.
• First book introducing two powerful R-packages - `causact` for visual modelling of business problems and `greta` which is an R interface to `TensorFlow` used for Bayesian inference.
• Uses the intuitive coding practices of the `tidyverse` including using `dplyr` for data manipulation and `ggplot2` for data visualization.
• Datasets that are freely and easily accessible.
• Code for generating all results and almost every visualization used in the textbook.

Do not learn statistical computation or fancy math in a vacuum, learn it through this guide within the context of solving business problems.