Course Project

This course is designed to introduce mathematicians and scientific computing students to essential software development tools and practices. No prior programming or Linux experience required.

Learning Path for Mathematics Students

The course is organized in three progressive levels with clear prerequisites and difficulty indicators:

🟢 FOUNDATIONAL

Prerequisites: None

Essential tools every mathematician needs for computational work

🟡 INTERMEDIATE

Prerequisites: Linux basics, Git fundamentals

Collaborative development and project management

🔴 ADVANCED

Prerequisites: All previous modules

High-performance computing and automation

Course Modules by Difficulty Level

🟢 Level 1: Essential Foundations (Weeks 1-4)

For students with no prior programming or Linux experience:

Linux Essentials for Mathematics: Learn command-line basics for mathematical computing. Navigate file systems, manage mathematical datasets, and access remote computing resources.

→ Start Learning Linux
Git for Mathematical Research: Track changes in LaTeX documents, collaborate on mathematical projects, and manage research code versions.

→ Link: Git Basics for Mathematical Research
VS Code for Mathematics: Set up integrated development environment for mathematical work with LaTeX, Python, and Jupyter integration.

→ Start Learning VS Code
Research Project Management: Apply project management principles to mathematical research, thesis projects, and academic collaborations.

→ Start Learning Project Management

🟡 Level 2: Collaborative Development (Weeks 5-8)

For students comfortable with basic command line and version control:

Advanced Git Workflows: Master branching, merging, and collaborative development for complex mathematical research projects.

→ See Git & GitHub module for details
Containers for Mathematics: Create reproducible environments for mathematical computing, share numerical experiments, and ensure research reproducibility.
Agile Research Methods: Apply modern project management methodologies to mathematical research and collaborative investigations.
Automation Basics: Introduce automation concepts for mathematical workflows and computational research.

🔴 Level 3: Advanced Automation (Weeks 9-12)

For students ready for high-performance computing workflows:

HPC Containerization: Deploy mathematical software on high-performance computing systems using advanced container technologies.
Advanced CI/CD for HPC: Implement sophisticated automation workflows for mathematical computing research and deployment.
Production Workflows: Master enterprise-level continuous integration and deployment for mathematical software development.
Research Visualization: Create interactive visualizations and dashboards for mathematical research results and data analysis.

Questions? Comments? Suggestions? > Use Slack

Quick Start for Instructors

👩‍🏫 Teaching this course? Check out the comprehensive 📚 Instructor Guide with:

Week-by-week curriculum for 12-week semester
Assessment strategies for mathematics students
Common challenges and solutions
Mathematical examples and case studies
Differentiated instruction for mixed skill levels

Take the Assessment First!

🎯 Students: Don’t jump straight into modules! Start with the Prerequisites & Self-Assessment to find your optimal starting point and avoid frustration or boredom.

💨 In a hurry? Check out the 🚀 Quick Start Guide for immediate next steps.