Введение в бизнес-аналитику для бизнес-аналитика: программирование на R для

A Business Analyst's Introduction to Business Analytics: Coding in R for Fast, Easy, and Visual Bayesian Inference

• Autor: Fleischhacker Ph.D., Adam J.
• Wydawnictwo: Independently published
• Rok wydania: 2023
• Liczba stron: 310
• Wymiary: 25.4 x 20.3 x 1.9
• Język: English: Published; English: Original Language; English
• ISBN: 9798857923412

The second edition of "A Business Analyst's Introduction To Business Analytics" is the fastest, easiest, and most visual entry point into `R` programming and computational Bayesian inference. Bookauthority.org declares the first edition one of the "Top 20 Bayesian Statistics Books of All Time". Find out why the book is so acclaimed as you learn to: use `R` to manipulate and model data with tools from the `tidyverse` like `dplyr` and `ggplot`, gently be introduced to the math and code supporting computational Bayesian inference, leverage the `causact` package and its visual DAG-based interface to `numpyro` for fast, easy, and visual inference, and learn to successfully advocate for change within an organization with data-driven insights, interpretable models, and persuasive visualizations. Book Description A frequent teaching-award winning professor with an analytics-industry background shares his hands-on guide to learning R programming and computational Bayesian inference (a great entry point into data science). 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 The second edition has been updated in August 2023 to be consistent with the latest R code. 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 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 `causact` for visual DAG-based modelling of Bayesian models and the fast, automated, inference `causact` provides via seemless integration to the `numpyro` package. 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.