Skip to main content
Back to top
Ctrl
+
K
Course Materials
Important Course information
Syllabus
Course Schedule
Software Setup Guide
Classroom Participation
Generative AI Policy
Semester Project
Project Details
Requirements and Grading Rubric
CMSE 801 Final Project Template
Project Self-Evaluation
Presentation Evaluation Rubric
Homeworks
Homework 1 - Python Fundamentals
Homework 2 - NumPy, Matplotlib, and a Simple ODE
pop_data.csv
Homework 3 - Analyzing Data with Pandas and Solving ODEs
tech_layoffs.csv
stars.csv
Homework 4 - Fitting models to data
sl_global.txt
stars.csv
Homework 5 - Python Objects and ABM
Daily Content
Day 01
Day 1 In-Class Assignment: Introductions and Sorting Algorithms
Day 02
Day 2 Pre-class Assignment: Intro to Python
Day 2 In-class Assignment: Order of Magnitude Modeling
https://docs.google.com/presentation/d/1TQkGJBIcxkIcN0gk_UOlHNmbo2vdEclj9lEv9FKkN6o/edit?usp=sharing
Day 03
Day 3: Pre-Class Assignment: Lists and Loops
Day 3 In-class Assignment: The power of compound interest
Day 04
Day 4: Pre-class Assignment: Boolean logic, if statements, and an introduction to functions
Day 4 In-class Assignment: Savings for Everyone
Day 05
Day 5: Pre-class Assignment: Thinking more about functions
Day 5 In-class Assignment: Practicing with functions (for profit!)
Day 06
Day 6 Pre-Class Assignment: Python Modules: Numpy and Plots
Day 6 In-class assignment: Visualizing population growth
Day 07
Day 7 Pre-Class: Introduction to NumPy
michigan_pop.csv
Day 7 In-class Assignment: Exploring Great Lakes Water Levels using NumPy
lake_erie.csv
lake_michigan_huron.csv
lake_ontario.csv
lake_superior.csv
Day 08
Day 8 Pre-class Assignment: Introduction to Modeling with Ordinary Differential Equations
Day 8: In-class Assignment: Introduction to Modeling with ODEs
Day 09
Day 9: Pre-class Assignment: Modeling with Ordinary Differential Equations
Day 9: In-class Assignment: Modeling with ODEs
Day 10
Day 10 Pre-Class: Viral Kinetics
Day 11
Day 11 Pre-class Assignment: ZOMBIES!!
Day 11 In-Class Assignment: Compartmental Modeling, moving from the model to the code
Day 12
Day 12 Pre-class Assignment: Computational Models Overview and The Pandas Data Analysis Library
Day 12 In-Class: Cleaning and Analyzing Economic Data
GDP_Data.csv
Day 13
Day 13 Pre-class Assignment: Normal Distributions, Masking, and Log Plots
GDP_Cleaned.csv
Day 13: In-class Assignment: Get the Lead Out: Understanding The Water Crisis in Flint, MI
flint_water_data.csv
Day 14
Day 14 Pre-Class Assignment: Fitting models, making predictions, and evaluating fits using data
Day 14 In-Class Assignment: Evaluating Models
biden_ratings_2023.csv
Day 15
Day 15 Pre-Class Assignment: Fitting functions to data (curve-fitting) and thinking more about models
Day 15 In-Class Assignment: (Thoughtfully) fitting models to data
pop200.csv
pop300.csv
Day 16
Day 16 Pre-class Assignment: Introduction to Data Visualization
Day 16 In-Class: Introduction to Data Visualization
Data Visualization Slides (MSU Login - Editable)
Scoring form
Day 17
Day 17 Pre-Class: 2D NumPy Arrays
example.jpeg
cars.csv
Day 17 In-Class Assignment: Image Analysis
beach.jpeg
landscape.jpeg
zebra.png
Day 18
Day 18 Pre-class: Agent-based models and forest fires
Day 18 In-class Assignment: Modeling forest fires with an Agent-based Model (Part 1)
Day 19
Day 19 Pre-class: More practice manipulating 2D Numpy Arrays
Day 19: In-class Assignment: Modeling forest fires with an Agent-based Model (Part 2)
Day 20
Day 20 Pre-class Assignment: introduction to Object Oriented programming (OOP)
Day 20 In-class Assignment: We have a Zoo!
Animal.py
Day 21
Day 21 Pre-class Assignment: Object inheritance and composition
Day 21 In-class Assignment: We have a Zoo! (part 2)
Animals.py (different than Day 20 file!)
Zoo.py
Day 22
Day 22 Pre-Class Assignment: Random Numbers
Day 22 In-class Assignment: Random Walks
Day 24
Day 24 Pre-class: Project Draft Presentation Preparation
Day 24 In-class Assignment: Project Work Day
Day 23
Day 23 Pre-class Assignment: “Monte Carlo” methods
Day 23: In-class Assignment: The Traveling Salesperson Problem - An Application of Monte Carlo
michigan_cities.csv
Day 25
Day 25: Pre-class assignment: Revisiting what it means for a model to “fit” the data
Day 25: In-Class: Markov Chain Monte Carlo Parameter Estimation
Extra Resources
Review and Practice Materials
Reviewing and practicing prior concepts: Python Fundamentals
Practice with Debugging
Practice with: functions
Practice with:
matplotlib
Practice with: Compartmental Models
Practice with: Pandas
weather.csv
Practice with:
solve_ivp
.md
.pdf
Day-25
Day-25
#
