Course Overview
This course introduces the basic concepts in business analytics implemented in spreadsheet models and shows how data can be used to solve business problems. We discuss methods used extensively in business organizations to solve large, structured problems that support decision-making across all levels of the organization.
This course carries the Quantitative Reasoning flag. A substantial portion of your grade will come from using quantitative skills to analyze real-world problems.
What will I learn? The course covers risk analysis using spreadsheets, sensitivity analysis, and Monte Carlo simulation. Students will use decision trees to analyze projects with management flexibility and solve real options problems. We will also use the Excel Solver for optimization problems from business operations and finance, including supply chain and portfolio optimization.
Pre-requisite: STA 301 · Format: In-person · Software: Microsoft Excel with DADM add-in (provided free)
Schedule of Lectures
The following schedule is tentative and subject to change. Assignments marked with * are due Tuesday 8:00 am of the listed week unless otherwise noted.
| Week | Date | Topic | Due* |
|---|---|---|---|
| 1 | 1/13 | Decision Modeling, Risk & Sensitivity Analysis | — |
| 2 | 1/20 | Introduction to Monte Carlo Simulation and Correlations | HW 1 |
| 3 | 1/27 | Class Canceled | HW 2 |
| 4 | 2/3 | Bootstrapping: Simulation from Historical Data | HW 3 |
| 5 | 2/10 | Simulation Optimization and Multi-Period Models | — |
| 6 | 2/17 | Exam 1 (In-Class) | Case 1 |
| 7 | 2/24 | Decision Trees for Sequential Decisions | — |
| 8 | 3/3 | Value of Information and Bayesian Updating | HW 4 |
| 9 | 3/10 | Multi-Armed Bandits | HW 5 |
| 10 | 3/17 | Spring Break | |
| 11 | 3/24 | More Multi-Armed Bandits and Its Extensions | HW 6 |
| 12a | 3/31 | Introduction to Optimization | Case 2 |
| 12b | 4/1 | Exam 2 | — |
| 13 | 4/7 | Types of Optimization Problems and Applications | HW 7 |
| 14 | 4/14 | Mixed-Integer Linear Programs | HW 8 |
| 15 | 4/21 | Nonlinear Programming and Course Wrap-Up | HW 9 |
| 16 | Case 3 |
Grading
| Component | Details | % of Grade |
|---|---|---|
| Class Exercises | Roughly one per class; lowest 2 dropped | 10% |
| Homeworks | 8-9 assignments, lowest 2 dropped | 20% |
| Group Projects | 3 case studies | 30% |
| Exams | 2 in-class exams | 40% |
| Total | 100% |
Policies
- Homework Posted Thursdays at 4:00 pm on Canvas; due Tuesdays at 8:00 am. No late submissions accepted. Lowest two grades dropped.
- Class Exercises Submitted via Canvas Quizzes (3 attempts, highest score kept). Due by 11:59 pm the Sunday following class. Lowest two grades dropped.
- Group Projects Groups of 4–5 (randomly assigned). Each group submits a 10-minute presentation and Excel analysis. Individual Canvas quiz required from each member.
- Exams In-person, on your own laptop via Canvas. One-hour time limit. Screen sharing via Zoom required throughout. Failure to share screen results in a grade of 0.
- Regrading Requests must be submitted within 7 days of receiving the initial grade.
- Generative AI Submitting AI-generated text for any assignment or exam is not permitted unless explicitly allowed. AI may be used for research and preparation, but all submitted work must be written by the student.
- Textbook (not required) Business Analytics, Data Analysis and Decision Making, 7e (Cengage) — available via the Longhorn Textbook Access program through Canvas.
- Canvas All materials, deadlines, and announcements are on Canvas at utexas.instructure.com. Canvas deadlines are the enforced deadlines.