Courses

Description and link to my courses.

ACC 3053 Systems 2

Introduction to Accounting Data Analytics

Students will use SQLite and DBbrowser for ETL (extraction, transformation, and loading data). Students will create a relational database with SQLite and use SQL query language to interact within this database. They will also connect this database with R statistical software. Students will use R statistical software for ETL. Specifically, R and RStudio (GUI for R) will be used in conjunction with Tidyverse R packages for basic data manipulation and visualizations. Using R forces students to use critical thinking in order to program and trouble shoot errors. Students will also learn Markdown which is a framework for presenting interactive reports will rich visuals. This will help students learn the value of presenting information in a business analytics context. Excel will be used to compare and contrast R and SQL programming. Students will perform advanced Excel tasks such as, but not limited to, pivot tables, vlookup, nested formulas, and dashboard creation.

ACC 3053 LINK

Sample Final Projects

Justin’s Final Project

McKenzie’s Final Project

Chad’s Final Project

ACC 8143

Accounting Data Analytics

Students will use R statistical software for ETL (extraction, transformation, and loading data). Specifically, R and RStudio (GUI for R) will be used in conjunction with Tidyverse R packages for advanced manipulation and visualizations. Students will learn to use Git and GitHub. Git and GitHub are extensively used for data analytics in practice. GitHub will show the students the importance of version control and the value of coding with care. Students will also learn Markdown which is a framework for presenting interactive reports, dashboards, and creating websites. Students will use RStudio, GitHub, Markdown, and Distill r package to make a professional website for showcasing their work in the class. This will help students learn the value of presenting information in a business analytics context and presenting their own work for others to see. Using Rstudio the students will learn how popular machine learning algorithms work, the difference between parametric and nonparametric algorithms, supervised and unsupervised algorithms, the bias-variance trade-off, and overfitting and underfitting algorithms with hands on examples in business contexts.

ACC 8143 LINK

Sample Final Projects

Karis’s Website

Matthew’s Website

Siwei’s Website