gh-bradleyboehmke-brads-marketplace-course-builder/skills/pedagogy/teaching-principles.md

Data Science Teaching Principles

General pedagogical principles for teaching data science, machine learning, AI, and MLOps.

Core Teaching Philosophy

1. Hands-On Learning

Data science is learned by doing, not just reading:

• Provide working code examples
• Encourage experimentation and iteration
• Use real or realistic datasets
• Build confidence through successful execution
• Learn from errors and debugging

2. Visual Learning

Leverage visualizations to build intuition:

• Plot data before analyzing
• Visualize model behavior
• Show distributions and patterns
• Use interactive plots when possible
• Make abstract concepts concrete through visuals

3. Incremental Complexity

Build understanding step by step:

• Start with simple, concrete examples
• Introduce one concept at a time
• Build on previously learned material
• Avoid overwhelming with too many details upfront
• Progress from intuition to theory

4. Theory Meets Practice

Balance conceptual understanding with application:

• Explain why, not just how
• Connect math to code implementation
• Show real-world applications
• Validate theoretical concepts through code
• Make abstract concepts tangible

5. Active Learning

Engage students in the learning process:

• Include practice problems
• Design checkpoints for self-assessment
• Encourage exploration and "what if" questions
• Provide opportunities for discovery
• Foster experimentation

Content Structure Guidelines

For Chapters

1. Start with motivation - Why does this topic matter?
2. Provide intuition - Concrete examples before formalism
3. Introduce concepts incrementally - One idea at a time
4. Show, don't just tell - Working examples with code
5. Practice and validate - Exercises to reinforce learning
6. Summarize and connect - Key takeaways and next steps

For Assessments

1. Test understanding, not memorization - Focus on concepts
2. Include application problems - Not just recall
3. Mix question types - Theory, code, interpretation
4. Provide partial credit opportunities - Show reasoning
5. Include real-world scenarios - Make it relevant

For Notebooks

1. Clear learning objectives - What will students learn?
2. Executable from top to bottom - No hidden dependencies
3. Mix explanation and code - Interleave markdown and code cells
4. Include validation - Help students check their work
5. Encourage exploration - Provide extension opportunities

For Slides

1. One main idea per slide - Don't overwhelm
2. Visual over text - Use diagrams and examples
3. Live coding when possible - Show the process
4. Build complexity gradually - Layer concepts
5. Include discussion prompts - Engage the audience

Level Differentiation

Undergraduate Students

• Assume less mathematical background
• Start with concrete before abstract
• Provide more scaffolding and structure
• Focus on intuition and practical application
• Use relatable, accessible examples
• Emphasize building confidence

Graduate Students

• Can handle more mathematical rigor
• Expect understanding of prerequisites
• Include theoretical foundations
• Balance theory with implementation
• Use research and industry examples
• Encourage independence and depth

Tool and Library Guidance

Choosing the Right Tool

• Beginners: Start with high-level libraries (pandas, sklearn)
• Intermediate: Introduce lower-level concepts (numpy operations)
• Advanced: Implement from scratch to understand internals

Code Quality

• Write clean, readable code
• Include comments explaining non-obvious parts
• Follow conventions (PEP 8 for Python)
• Show both "quick and dirty" and "production-quality" approaches when relevant
• Emphasize reproducibility

Common Pitfalls to Avoid

1. Math before intuition - Build understanding first
2. Too much at once - Break down complex topics
3. Passive learning - Always include active elements
4. Disconnected theory - Link concepts to practice
5. Ignoring prerequisites - Know your audience
6. One-size-fits-all - Adapt to student level
7. No real-world context - Show why it matters