Syllabus
Course Information
Course: MATH 346 Mathematical and Statistical Software
Credits: 3
Contact hours: 3 lecture hours per week
Prerequisites: COSC 114; MATH 204 or MATH 211; MATH 242 or MATH 243 or MATH 244
Catalog Description
This course introduces mathematical and statistical programming using MATLAB and R. Topics include data science, numerical computation, tidy data, exploratory data analysis, plotting, and symbolic computation.
Course Framing
The course follows the approved MATLAB and R syllabus. The teaching approach emphasizes reproducible, AI-aware computational work:
- formulate a mathematical or statistical problem;
- implement a solution in MATLAB or R;
- validate numerical, graphical, and statistical output;
- communicate the result in a reproducible report;
- document any generative AI assistance.
Course Learning Outcomes
By the end of the course, students should be able to:
- Write code for mathematical and statistical applications using appropriate software.
- Present mathematical and statistical results using appropriate software.
- Solve problems using mathematical and statistical software.
- Analyze data using mathematical and statistical software.
Assessment
| Assessment | Weight | Notes |
|---|---|---|
| Quizzes | 20% | Four quizzes; lowest quiz dropped |
| Projects | 20% | MATLAB Project 1 and R Project 2, 10% each |
| Semester examination | 25% | Around Week 8 |
| Final examination | 35% | Cumulative |
Tentative Weekly Plan
| Week | Topic |
|---|---|
| 1 | MATLAB arrays, scripts, and 2D plotting |
| 2 | MATLAB programming and user functions |
| 3 | MATLAB curve fitting and interpolation |
| 4 | MATLAB numerical analysis and 3D plotting |
| 5 | MATLAB symbolic math |
| 6 | R Markdown and data visualization |
| 7 | R workflow and data transformation |
| 8 | Exploratory data analysis |
| 9 | Tibbles, data import, and tidy data |
| 10 | Relational data, strings, factors, dates, and times |
| 11 | Pipes and functions |
| 12 | Vectors and iteration |
| 13 | Model basics and model building |
| 14 | Handling many models |
| 15 | Visualization, communication, and project synthesis |
Project Structure
Project 1: MATLAB numerical investigation using a group-specific synthetic building-energy dataset. The exact due date is being aligned with the end of the MATLAB block.
Project 2: R data analysis, modeling, and validation using richer training data and a withheld validation file, due around Week 15.
Students submit a PDF report together with executable source code and required machine-readable results. They are not required to find or collect a dataset, write Quarto, or write HTML. All synthetic data must be identified as synthetic, and every project includes a responsible AI-use appendix and group-contribution statement.
Projects are group-assessed. Individual competence is established through the quizzes, semester examination, and final examination. Any group member may be invited to a brief project verification, selected randomly or because submitted work requires clarification. Selection does not imply suspected misconduct, and a project–examination grade difference is not evidence by itself.