Computer Science · The Cushman School

Computer Science Math

The Cushman School — Miami, FL

Applied mathematics through computation — students write programs that simulate, forecast, and optimize real-world systems, building tools that actually work.

Overview

About the Course

Computer Science Math bridges quantitative mathematics and software. Instead of doing math for its own sake, students do math by building things — simulations, forecasting models, optimization algorithms — and watch the mathematics come alive in the output.

The course uses Python as its primary language and emphasizes data science libraries that are standard in research and industry. Students learn to work in Jupyter Notebooks, version their code with Git, and publish projects on GitHub. Every major unit produces a working deliverable students can point to.

  • Write Python programs that compute, visualize, and analyze data
  • Build Monte Carlo simulations to model uncertainty and expected outcomes
  • Implement forecasting models using regression and time series methods
  • Apply algorithmic optimization to constrained problems
  • Package and present projects as reproducible GitHub repositories

Curriculum

Units

Unit 1

Python Foundations

Variables, functions, loops, data structures, and writing readable, organized code.

Unit 2

Statistics via Code

Descriptive statistics, distributions, correlation, and visualization with pandas and matplotlib.

Unit 3

Simulation & Monte Carlo

Randomness, probability by simulation, expected value, and modeling complex systems.

Unit 4

Forecasting Models

Linear regression, time series basics, and simple machine learning models for prediction.

Unit 5

Optimization

Greedy algorithms, constraint satisfaction, and basic mathematical programming concepts.

Unit 6

Final Projects

Student-driven data science projects published as GitHub repos with working demos.

Resources

Tools & Technology

Python pandas numpy matplotlib scikit-learn Jupyter Notebooks GitHub VS Code

Student Work

Projects

Design District Pop-Up Forecasting Model

Demand forecasting model predicting foot traffic and revenue for a pop-up shop in Miami's Design District. Combines time series analysis with external variables.

View on GitHub

Lemonade Stand Simulation

Economic simulation exploring pricing models, demand curves, and profit optimization under weather and cost uncertainty.

View on GitHub