Teaching

BUSQOM 1080 - Data Analysis for Business Materials

BUSQOM 1080 serves as the first introduction to (base) R, and data analysis using R, for upper level business undergraduates at the University of Pittsburgh. It covers a variety of basic statistical methods and their implementations. For a complete list of topics please see my Fall ‘20 Syllabus.

The course is required for the Certificate in Business Analytics and is a necessary prerequiste for BUSSCM 1760 - DATA MINING which is taught in the Spring.

In the table below I’ve collected all of the Powerpoint Slides, associated R Notebooks, and Datasets (after the table!) for my course. I hope that they will serve as an instructive reference. Please don’t hesistate to email me if you have comments, questions, or corrections.

Data Analysis for Business (Fall ‘20)

LectureSlides (.pptx)Notebooks (.Rmd)
Lecture 1 - Introduction to Business Analytics[Slides], [Installation Guide]-
Lecture 2 - Introduction to R[Slides][Notebook]
Lecture 3 - Introduction to R Programming[Slides][Notebook]
Lecture 4 - More on R Programming[Slides][Notebook]
Lecture 5 - Introduction to Plotting![Slides][Notebook]
Lecture 6 - More on Plotting![Slides][Notebook]
Lecture 7 - Comparing Groups[Slides][Notebook]
Lecture 8 - Comparing Groups II[Slides][Notebook]
Lecture 9 - Simple Linear Regression[Slides][Notebook]
Lecture 10 - Simple Linear Regression II[Slides][Notebook]
Lecture 11 - Multiple Regression I[Slides][Notebook]
Lecture 12 - Multiple Regression II[Slides][Notebook]
Lecture 13 - Model Selection[Slides][Notebook]
Lecture 14 - Model Selection Pitfalls[Slides][Notebook]
Lecture 15, 16 - Midterm Review, Midterm  
Lecture 17 - Data Processing I[Slides][Notebook]
Lecture 18 - Data Processing II[Slides][Notebook]
Lecture 19 - Logistic Regression I[Slides][Notebook]
Lecture 20 - Logistic Regression II[Slides][Notebook]
Lecture 21 - Supervised Learning I[Slides][Notebook]
Lecture 22 - Supervised Learning II[Slides][Notebook]
Lecture 23 - Unsupervised Learning I[Slides][Notebook]
Lecture 24 - Unsupervised Learning II[Slides][Notebook]

Datasets