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skills/content-templates/templates.md

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+# Content Templates
+
+Structural templates for different types of educational content.
+
+## Chapter Template
+
+```markdown
+# Chapter [X]: [Topic Name]
+
+## Learning Objectives
+By the end of this chapter, you will be able to:
+- [Objective 1 - action verb + specific skill]
+- [Objective 2]
+- [Objective 3]
+
+## Introduction
+[1-2 paragraphs motivating the topic]
+- Why is this important?
+- What real-world problems does it solve?
+- How does it connect to previous chapters?
+
+## [Section 1: Core Concept]
+
+### Intuition
+[Explain the concept using analogies or simple examples]
+
+### Formal Definition
+[Mathematical or technical definition]
+
+### Example
+[Concrete example with code if applicable]
+
+```python
+# Code example
+```
+
+### Visualization
+[Description of diagram or plot to include]
+
+## [Section 2: Application]
+[Show how to use the concept]
+
+## [Section 3: Advanced Topics]
+[Optional: deeper dive for interested students]
+
+## Practice Problems
+1. [Problem testing basic understanding]
+2. [Problem requiring application]
+3. [Challenge problem]
+
+## Summary
+- Key takeaway 1
+- Key takeaway 2
+- Key takeaway 3
+
+## Further Reading
+- [Resource 1]
+- [Resource 2]
+```
+
+## Quiz Template
+
+```markdown
+# Quiz: [Topic Name]
+
+**Instructions:** [Time limit, allowed resources, submission format]
+
+## Part 1: Conceptual Understanding
+
+### Question 1 (X points)
+[Multiple choice, short answer, or true/false]
+
+**Answer:** [For instructor use]
+
+### Question 2 (X points)
+[Conceptual question]
+
+## Part 2: Application
+
+### Question 3 (X points)
+[Code-based or problem-solving question]
+
+```python
+# Starter code if applicable
+```
+
+**Expected output:** [Description]
+
+## Part 3: Analysis
+
+### Question 4 (X points)
+[Interpretation or explanation question]
+
+---
+
+## Answer Key
+
+[Detailed answers and grading rubric]
+
+**Total Points:** XX
+```
+
+## Jupyter Notebook Template (Companion)
+
+```markdown
+# [Topic Name] - Companion Notebook
+
+**Learning Objectives:**
+- [Objective 1]
+- [Objective 2]
+
+---
+
+## Setup
+```python
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Configuration
+%matplotlib inline
+plt.style.use('seaborn')
+```
+
+## Section 1: [Concept Name]
+
+[Markdown explanation matching chapter]
+
+```python
+# Executable example from chapter
+```
+
+**Try it yourself:** [Suggested modifications to explore]
+
+## Section 2: Interactive Exploration
+
+```python
+# Code for students to experiment with
+```
+
+**Questions to explore:**
+1. What happens if you change X?
+2. Try different values for Y
+3. Visualize the results
+
+## Section 3: Practice Exercises
+
+### Exercise 1
+[Description]
+
+```python
+# TODO: Your code here
+```
+
+**Validation:**
+```python
+# Check your answer
+assert ..., "Check failed!"
+print("✓ Correct!")
+```
+
+## Summary
+
+[Key points reinforced in this notebook]
+```
+
+## Jupyter Notebook Template (Lab)
+
+```markdown
+# Lab [X]: [Topic Name]
+
+**Estimated Time:** X hours
+**Difficulty:** [Beginner/Intermediate/Advanced]
+
+## Learning Objectives
+- [Objective 1]
+- [Objective 2]
+
+---
+
+## Problem Statement
+
+[Real-world problem description]
+
+## Dataset
+
+[Description and source of data]
+
+```python
+# Load or generate dataset
+```
+
+**Explore the data:**
+```python
+# TODO: Examine the dataset
+# - Check dimensions
+# - Look at first few rows
+# - Check for missing values
+```
+
+## Task 1: [Subtask Name]
+
+[Instructions]
+
+```python
+# TODO: Your code here
+
+
+# Solution will go here
+```
+
+**Checkpoint:** [How to verify this step]
+
+## Task 2: [Next Subtask]
+
+[Instructions building on Task 1]
+
+```python
+# TODO: Your code here
+```
+
+## Task 3: [Final Analysis]
+
+[Open-ended analysis task]
+
+## Bonus Challenge (Optional)
+
+[Extension for advanced students]
+
+## Reflection Questions
+
+1. What was the most challenging part?
+2. What did you learn?
+3. How could you extend this analysis?
+```
+
+## Slides Template (Markdown)
+
+```markdown
+---
+title: [Topic Name]
+author: [Your Name]
+date: [Date]
+---
+
+# [Topic Name]
+
+## Learning Objectives
+
+- Objective 1
+- Objective 2
+- Objective 3
+
+---
+
+## Why This Matters
+
+[Motivation - 1-2 bullet points with visual]
+
+**Real-world application:** [Example]
+
+::: notes
+[Speaker notes: Hook students with interesting context]
+:::
+
+---
+
+## [Concept 1]: Intuition
+
+[Visual diagram or simple example]
+
+- Key point 1
+- Key point 2
+
+::: notes
+[Teaching tips, common misconceptions to address]
+:::
+
+---
+
+## [Concept 1]: Formal Definition
+
+[Mathematical notation or technical definition]
+
+**In plain English:** [Simplified explanation]
+
+---
+
+## Example: [Concrete Case]
+
+```python
+# Code example
+```
+
+**Output:**
+```
+[Expected output]
+```
+
+---
+
+## Practice Problem
+
+[Quick problem for students to try]
+
+**Think-Pair-Share:** Discuss with your neighbor
+
+::: notes
+[Give 2-3 minutes, walk around, call on students]
+:::
+
+---
+
+## Key Takeaways
+
+1. [Main point 1]
+2. [Main point 2]
+3. [Main point 3]
+
+**Next time:** [Preview next topic]
+
+---
+
+## Questions?
+
+[Contact info or office hours]
+```
+
+## Usage Guidelines
+
+### When to Use Each Template
+
+- **Chapter**: Comprehensive coverage of a topic for reading/study
+- **Quiz**: Assess understanding of covered material
+- **Companion Notebook**: Follow along with chapter, explore interactively
+- **Lab Notebook**: Apply concepts to solve realistic problems
+- **Slides**: Support lecture or presentation
+
+### Customization
+
+These templates should be adapted based on:
+- Student level (undergrad vs grad)
+- Course philosophy (theory vs applied)
+- Time available
+- Prerequisites
+- Tools and libraries used
+
+Always reference the specific course profile for customization guidance.

skills/courses/bana-4080/course-profile.md

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+# BANA 4080 - Course Profile
+
+**Course:** UC BANA 4080: Introduction to Data Mining with Python
+**Instructor:** Brad Boehmke
+**Level:** Undergraduate
+**Base Profile:** intro-to-data-mining
+
+## Course Overview
+
+This is the same course as "Intro to Data Mining" - all content, philosophy, and standards from that profile apply. This profile adds BANA 4080-specific lab structure and requirements.
+
+## Lab Structure (Thursday Sessions)
+
+**Duration:** 75 minutes exactly
+**Format:** Two-part structure with collaborative learning
+
+### Part A: Guided Reinforcement (30 minutes)
+**Purpose:** TA walks students through concepts to reinforce Tuesday's lecture and weekly readings
+
+**Structure:**
+- Section A1: Concept review and setup (5-7 minutes)
+- Section A2: Systematic practice of key skills (12-15 minutes)
+- Section A3: Professional techniques demonstration (8-10 minutes)
+- Section A4: Integration and advanced concepts (5-8 minutes)
+
+**Key Principles:**
+- Students follow along and execute code together
+- TA explains rationale and connects to business applications
+- Multiple opportunities for questions and clarification
+- Gradual release of responsibility toward independence
+
+### Class Q&A: Transition (5-10 minutes)
+- Address questions from Part A
+- Clarify confusing concepts
+- Preview independent challenges
+
+### Part B: Independent Group Challenges (35-40 minutes)
+**Purpose:** Students apply learned concepts independently in groups of 2-4
+
+**Structure:**
+- Challenge 1: Basic application (6-8 minutes)
+- Challenge 2: Intermediate skills (6-8 minutes)
+- Challenge 3: Complex integration (6-8 minutes)
+- Challenge 4: Advanced application (6-8 minutes)
+- Challenge 5: Creative problem-solving (6-8 minutes)
+- Challenge 6: Extension/synthesis (5-7 minutes)
+
+**Key Principles:**
+- Groups work collaboratively with minimal TA intervention
+- Challenges require integration of multiple concepts
+- Business context makes problems meaningful and engaging
+- Different groups can progress at different paces
+- **NO AI tools allowed** - students write code themselves
+
+### Wrap-up: Reflection (3-5 minutes)
+- Accomplishments summary
+- Reflection questions
+- Connection to homework and next steps
+
+## Lab Requirements
+
+**Template:** `/Users/b294776/Desktop/UC/uc-bana-4080/planning/templates/lab_notebook_template.ipynb`
+**Usage Guide:** `/Users/b294776/Desktop/UC/uc-bana-4080/planning/templates/lab_template_usage_guide.md`
+
+**Naming Convention:**
+- Student lab: `XX_wkX_lab.ipynb` (e.g., `03_wk3_lab.ipynb`)
+- TA guidance: `ta_guidance_wkX.ipynb` (e.g., `ta_guidance_wk3.ipynb`)
+
+**Content Alignment:**
+- Every lab must directly reinforce concepts from Tuesday's slides
+- Labs based on weekly assigned chapter readings
+- Part A systematically reviews Tuesday lecture material
+- Part B challenges integrate multiple chapter concepts
+
+**Pedagogical Standards:**
+- Business context for every concept and exercise
+- Progressive complexity from guided to independent work
+- Real-world datasets (prefer chapter data and exercise data)
+- Clear learning objectives (3-4 specific, measurable outcomes)
+- Built-in reflection and metacognitive elements
+
+## TA Guidance Requirements
+
+Every lab must include a comprehensive TA guidance notebook with:
+
+**Pre-Lab Preparation Section:**
+- Overview of learning objectives and key concepts
+- Connection to Tuesday slides and weekly readings
+- Setup instructions and common technical issues
+- Grouping strategies and classroom management tips
+
+**Part A Detailed Instructions:**
+- Section-by-section teaching guidance with timing
+- Key concepts to emphasize at each step
+- Common student questions and suggested responses
+- Code demonstrations and explanation strategies
+- Transition techniques between concepts
+
+**Part B Facilitation Guide:**
+- Challenge-by-challenge overview with learning goals
+- Common student difficulties and targeted hints
+- Complete solutions for all challenges
+- When and how to provide assistance
+- Strategies for different pacing among groups
+
+**Assessment and Wrap-up:**
+- Key concepts students should have mastered
+- Reflection questions to check understanding
+- Connections to upcoming content and homework
+- Troubleshooting guide for common issues
+
+## Dataset Strategy
+
+**Default Approach:**
+- **Part A (guided section):** Use primary dataset from chapter readings
+- **Part B (challenges):** Use dataset from end-of-chapter exercises
+- Always confirm dataset choices with instructor
+- Allow for alternative datasets based on specific lab needs
+
+## Quality Standards
+
+**Before finalizing any lab:**
+- [ ] All code tested and functional in Google Colab
+- [ ] Tuesday slide alignment verified
+- [ ] Chapter reading integration confirmed
+- [ ] 75-minute timing validated
+- [ ] Part A/B balance appropriate (30 min / 35-40 min)
+- [ ] Business context realistic and motivating
+- [ ] TA guidance comprehensive with complete solutions
+- [ ] All `[PLACEHOLDERS]` filled with specific content
+- [ ] Colab badge updated with correct filename
+- [ ] Learning objectives align with activities
+
+## Reference Materials
+
+For full pedagogical approach and lab development process, refer to:
+- Base course profile: `intro-to-data-mining/course-profile.md`
+- Lab template: Path specified above
+- Usage guide: Path specified above

skills/courses/bana-4080/lab-template-guide.md

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+# BANA 4080 Lab Template Guide
+
+This guide provides the structure and requirements for creating Thursday lab notebooks for BANA 4080.
+
+## Template Location
+
+**File:** `/Users/b294776/Desktop/UC/uc-bana-4080/planning/templates/lab_notebook_template.ipynb`
+**Usage Guide:** `/Users/b294776/Desktop/UC/uc-bana-4080/planning/templates/lab_template_usage_guide.md`
+
+## Lab Structure (75 minutes)
+
+### Header Section
+- Week number and descriptive lab title
+- Colab badge with correct filename
+- Lab description and context (2-3 sentences)
+- **Learning Objectives:** 3-4 specific, measurable outcomes starting with action verbs
+- **This Lab Reinforces:** List of chapter/reading references
+- **Estimated Time & Structure:** Clear breakdown with realistic time estimates
+- **Why This Matters:** Business context and real-world relevance
+
+### Setup Section
+- Required imports (only necessary libraries)
+- Data loading code
+- Quick preview/verification of loaded data
+
+### Part A: Guided Reinforcement (~30 minutes)
+
+**Part 1** — [Section 1 Title] (Time estimate)
+- Section description and context
+- Subsection with explanation/instructions
+- Step-by-step instructions (numbered)
+- Code examples or starter code
+- "🧠 Your Turn" exercise with tasks
+- Empty code cell for practice
+- "✅ Check Your Understanding" with questions and expected results
+
+**Part 2** — [Section 2 Title] (Time estimate)
+- Similar structure to Part 1
+- Guided example with explanation
+- Demonstration code
+- "🧪 Practice Exercise" with business scenario
+- Step-by-step approach
+- Code cell for solution
+
+**Class Discussion/Q&A** (5-10 minutes)
+- Discussion prompts
+- Common blockers and clarifications
+
+### Part B: Independent Group Challenges (~35-40 minutes)
+
+**Intro markdown:**
+```markdown
+For the next several challenges:
+* You will not be given starter code
+* **DO NOT USE AI** to generate code
+* Work in groups of 2-4 students
+* Feel free to ask questions or seek help from instructor
+* We'll stop and walk through each challenge together after each time block
+```
+
+**Challenge 1** — [Title] (6-8 minutes)
+- Business question
+- Additional context if needed
+- Empty code cell with comment: `# Your turn: write code here to [description]`
+
+**Challenge 2** — [Title] (6-8 minutes)
+- Business question
+- Strategic hint (not code)
+- Empty code cell
+
+**Challenge 3-6** — Similar structure with progressive difficulty
+
+### Optional Extension Activities
+
+**Extension 1-2:** Advanced challenges for early finishers
+**Extension 3:** "Brainstorm - What else is interesting?" with example questions
+
+### Lab Wrap-Up & Reflection (3-5 minutes)
+
+- **What You Accomplished:** List of accomplishments
+- **Reflection Questions:** 2-3 metacognitive prompts
+- **Connection to Course Goals:** How this lab connects to broader learning
+- **Next Steps:** Homework reference, next week preview, optional resources
+- Save your work instruction
+
+### Troubleshooting & Common Issues
+
+- Issue 1-3 with solutions
+- General debugging tips
+
+## Required Placeholders to Fill
+
+All items in `[BRACKETS]` must be replaced:
+
+| Placeholder | Example |
+|-------------|---------|
+| `[X]` | Week number (e.g., `6`) |
+| `[LAB_TITLE]` | `Control Flow and Functions in Practice` |
+| `[FILENAME]` | `06_wk6_lab` |
+| `[LAB_DESCRIPTION_AND_CONTEXT]` | Description of what students will do |
+| `[OBJECTIVE_1]` | `Write conditional statements for business logic` |
+| `[Reading/Chapter Reference 1]` | `Chapter 7: Control Flow` |
+| `[TIME_ESTIMATE]` | `15-20` |
+| `[BUSINESS_CONTEXT_AND_REAL_WORLD_RELEVANCE]` | Why this matters in real work |
+| `[SECTION_X_TITLE]` | Name of major section |
+| `[SUBSECTION_X_TITLE]` | Name of subsection |
+| `[EXPLANATION_OR_INSTRUCTIONS]` | Teaching content |
+| `[STEP_X]` | Individual step in process |
+| `[DESCRIPTIVE_COMMENT]` | What the code does |
+| `[CODE_EXAMPLE_OR_STARTER]` | Actual code |
+| `[EXERCISE_TITLE]` | Name of practice exercise |
+| `[EXERCISE_DESCRIPTION]` | What students should do |
+| `[TASK_X]` | Individual task |
+| `[HELPFUL_HINT_IF_NEEDED]` | Strategic guidance |
+| `[MINI_ASSESSMENT_OR_DISCUSSION_QUESTIONS]` | Comprehension check |
+| `[QUESTION_X]` | Specific question |
+| `[WHAT_STUDENTS_SHOULD_SEE]` | Expected output/result |
+| `[CONCRETE_BUSINESS_EXAMPLE]` | Real scenario |
+| `[DEMONSTRATION_CODE]` | Working example |
+| `[REALISTIC_BUSINESS_CONTEXT]` | Business scenario for exercise |
+| `[CLEAR_TASK_DESCRIPTION]` | What to accomplish |
+| `[CHALLENGE_TITLE]` | Name of challenge |
+| `[BUSINESS_QUESTION]` | Question to answer |
+| `[ADDITIONAL_CONTEXT_IF_NEEDED]` | Extra info |
+| `[CHALLENGE_DESCRIPTION]` | What the code should do |
+| `[STRATEGIC_HINT_NOT_CODE]` | Approach guidance |
+| `[EXTENSION_TITLE]` | Name of extension |
+| `[ADVANCED_CHALLENGE_DESCRIPTION]` | Extension task |
+| `[EXAMPLE_QUESTION_X]` | Sample brainstorm question |
+| `[ACCOMPLISHMENT_X]` | What was learned |
+| `[HOW_THIS_LAB_CONNECTS_TO_BROADER_LEARNING]` | Big picture |
+| `[HOMEWORK_REFERENCE]` | Link to assignment |
+| `[NEXT_WEEK_PREVIEW]` | What's coming |
+| `[OPTIONAL_RESOURCES]` | Additional materials |
+| `[SPECIFIC_SHARING_INSTRUCTIONS]` | How to share work |
+| `[COMMON_PROBLEM]` | Issue students face |
+| `[SOLUTION_APPROACH]` | How to fix |
+| `[TIP_X]` | Debugging tip |
+
+## Content Development Process
+
+### Phase 1: Content Analysis
+1. Review assigned chapter(s) for the week
+2. Identify key concepts that need hands-on practice
+3. Map concepts to Part A (guided) and Part B (challenges)
+4. Define 3-4 specific learning objectives
+
+### Phase 2: Part A Design (Guided Reinforcement)
+- Systematically review key concepts from readings
+- Provide hands-on practice with instructor guidance
+- Include "Your Turn" exercises for immediate application
+- Build confidence before independent work
+
+**Principles:**
+- Students follow along and execute code together
+- Explain rationale and connect to business applications
+- Multiple opportunities for questions
+- Gradual release of responsibility
+
+### Phase 3: Part B Design (Independent Challenges)
+- Create 6 challenges with progressive difficulty
+- Each challenge: clear business question, minimal code scaffolding
+- Strategic hints rather than direct solutions
+- Require integration of multiple concepts
+
+**Principles:**
+- Groups work collaboratively with minimal intervention
+- Business context makes problems meaningful
+- Different groups can progress at different paces
+- No AI tools allowed - students write code themselves
+
+### Phase 4: Dataset Selection
+**Default Strategy:**
+- Part A (guided): Primary dataset from chapter readings
+- Part B (challenges): Dataset from end-of-chapter exercises
+- Always confirm with instructor and allow alternatives
+
+### Phase 5: Quality Validation
+- [ ] All code tested in Google Colab
+- [ ] Chapter alignment verified
+- [ ] 75-minute timing realistic
+- [ ] Part A/B balance appropriate (~30 min / ~35-40 min)
+- [ ] Business context realistic
+- [ ] Learning objectives align with activities
+- [ ] All placeholders replaced
+- [ ] Colab badge updated
+
+## TA Guidance Requirements
+
+Each lab requires a companion `ta_guidance_wkX.ipynb` with:
+
+**Pre-Lab Preparation:**
+- Learning objectives and key concepts overview
+- Connection to readings
+- Setup instructions and common issues
+- Classroom management tips
+
+**Part A Teaching Guidance:**
+- Section-by-section instructions with timing
+- Key concepts to emphasize
+- Common student questions and responses
+- Teaching strategies
+
+**Part B Facilitation Guide:**
+- Complete solutions for all 6 challenges
+- Common difficulties and targeted hints
+- When and how to provide assistance
+- Pacing strategies
+
+**Assessment and Wrap-up:**
+- Key concepts to verify mastery
+- Reflection questions
+- Connection to upcoming content
+- Troubleshooting guide
+
+## Business Context Standards
+
+Every concept and exercise must have clear business relevance:
+- Real-world scenarios students can relate to
+- Authentic business questions and problems
+- Professional applications and use cases
+- Connection to career skills
+
+**Good examples:**
+- Customer segmentation analysis
+- Marketing campaign performance
+- Retail transaction patterns
+- Product recommendation systems
+- Sales forecasting
+
+**Avoid:**
+- Abstract mathematical exercises without context
+- Toy problems with no real-world connection
+- Examples that don't relate to business analytics
+
+## Common Lab Types by Week
+
+**Weeks 1-3 (Fundamentals):**
+- More guided examples, slower pacing
+- Simple, clear-cut problems
+- Accessible business scenarios
+- Building basic confidence
+
+**Weeks 4-6 (Skill Application):**
+- Less guidance, more problem-solving
+- Multi-step business problems
+- Realistic data analysis scenarios
+- Integration of concepts
+
+**Weeks 7+ (Advanced Integration):**
+- Open-ended exploration
+- Complex, multi-faceted problems
+- Comprehensive case studies
+- Professional-level analysis
+
+## Remember
+
+- Labs directly reinforce Tuesday lecture concepts
+- Based on weekly assigned chapter readings
+- 75 minutes exactly with strategic time allocation
+- Two-part structure: guided (30 min) + independent (35-40 min)
+- Business context for everything
+- No AI tools in Part B challenges
+- Always create companion TA guidance notebook

skills/courses/bana-6043/course-profile.md

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+# BANA 6043 - Course Profile
+
+**Course:** UC BANA 6043: Statistical Computing
+**Level:** Graduate
+**Base Profile:** statistical-computing
+
+## Course Overview
+
+This is the same course as "Statistical Computing" - all content, philosophy, and standards from that profile apply.
+
+This profile serves as a mapping to enable course-builder commands to recognize "BANA 6043" as referring to the statistical-computing course profile.
+
+For all content creation, refer to:
+- Base course profile: `statistical-computing/course-profile.md`

skills/courses/bana-7075/course-profile.md

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+# BANA 7075 - Course Profile
+
+**Course:** UC BANA 7075: ML in Business
+**Level:** Graduate
+**Base Profile:** ml-in-business
+
+## Course Overview
+
+This is the same course as "ML in Business" - all content, philosophy, and standards from that profile apply.
+
+This profile serves as a mapping to enable course-builder commands to recognize "BANA 7075" as referring to the ml-in-business course profile.
+
+For all content creation, refer to:
+- Base course profile: `ml-in-business/course-profile.md`

skills/courses/intro-to-data-mining/course-profile.md

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+# Intro to Data Mining - Course Profile
+
+**Course:** UC BANA 4080: Introduction to Data Mining with Python
+**Instructor:** Brad Boehmke
+
+## Audience
+
+**Student Level:** Undergraduate (juniors/seniors)
+
+**Background:**
+- Business students with foundation in calculus, statistics, and possibly regression
+- May have Excel experience and basic VBA exposure
+- Variable programming backgrounds - many are complete coding beginners
+- Understand business operations, customer behavior, and quantitative thinking
+- Know how to think critically but lack experience turning theory into practice with code
+
+**Prerequisites:**
+- Quantitative methods and statistical inference courses
+- No prior programming experience required or expected
+- Course explicitly designed for beginners
+
+**Key Challenge:** Bridging the gap between classroom theory and real-world data analysis
+
+## Learning Philosophy
+
+**Core Approach:** Hands-on, immersive learning through doing
+
+**Key Principles:**
+1. **Practice over theory** - Students learn by working with real, messy datasets
+2. **Build confidence through action** - Focus on getting students comfortable with tools before perfection
+3. **Close the theory-practice gap** - Move from "knowing concepts" to "applying skills"
+4. **AI as assistant, not autopilot** - Use GenAI tools (ChatGPT, Claude, Copilot) to help learn, but emphasize understanding
+5. **Collaborative learning** - Build community; encourage students to help each other
+6. **Growth mindset** - Normalize struggle; coding is a new language that takes time
+
+**Teaching Style:**
+- Conversational, relatable tone (see intro chapter example)
+- Use storytelling and scenarios (e.g., "Taylor the intern")
+- Address student concerns directly (e.g., "Why learn this when AI exists?")
+- Set realistic expectations about difficulty
+- Encourage persistence and resilience
+
+**Unique Aspects:**
+- Explicitly addresses role of GenAI in learning process
+- Balances AI assistance with foundational skill building
+- Uses real-world business contexts students can relate to
+
+## Technical Stack
+
+**Core Environment:**
+- **Python** (chosen for beginner-friendliness + professional power)
+- **Jupyter Lab/Notebooks** (primary development environment)
+- **Google Colab** (cloud-based option for students)
+- **Quarto** (for textbook and slides)
+- **Virtual environment** (venv for package management)
+
+**Primary Libraries (Weeks 1-6):**
+- **pandas** - data manipulation and DataFrames
+- **numpy** - numerical computation
+- **matplotlib** - basic visualization
+- **seaborn** - statistical visualization
+
+**Machine Learning Libraries (Weeks 8-13):**
+- **scikit-learn** - all ML models and evaluation
+
+**Additional Tools:**
+- CSV/Excel file handling
+- Basic SQL concepts (joins in pandas)
+- Git/GitHub for assignment submission
+
+**File Formats:**
+- Quarto markdown (.qmd) for book chapters
+- Jupyter notebooks (.ipynb) for examples, labs, homework
+- Real datasets (CSV, Excel) in `/data/` directory
+
+## Content Style
+
+**Writing Style:**
+- **Conversational and approachable** - Not dry or overly academic
+- **Student-focused** - Addresses "you" directly
+- **Motivational** - Builds confidence, normalizes struggle
+- **Practical** - Always tied to real-world application
+- **Honest** - Acknowledges difficulties, doesn't sugar-coat challenges
+
+**Explanation Approach:**
+1. **Start with WHY** - Motivate the topic before diving in
+2. **Use analogies and stories** - Make abstract concepts concrete
+3. **Show, don't just tell** - Working code examples over theory
+4. **Progressive complexity** - Start simple, build gradually
+5. **Address common questions** - Anticipate student concerns
+
+**Examples:**
+- Use **relatable business scenarios** (customer data, marketing analytics, retail transactions)
+- Work with **messy, real-world datasets** (not clean, perfect examples)
+- Include **visual aids** heavily (plots, diagrams, screenshots)
+- Provide **executable code** that students can run and modify
+
+**Pedagogical Elements:**
+- **Callout boxes** for tips, warnings, reflections, and examples
+- **Student reflection prompts** to encourage metacognition
+- **Exercises** that build on chapter concepts
+- **Code comments** that explain what's happening
+- **Error messages and debugging guidance**
+
+**Depth:**
+- Prioritize **intuition over mathematical rigor**
+- Show code implementation before heavy theory
+- Balance "just enough math" with practical application
+- Focus on **interpretation and application** over derivations
+
+## Key Topics
+
+**Module 1: Python Fundamentals (Week 1)**
+- Course intro + motivation
+- Variables, data types, basic operators
+- Why Python? Why not just use AI?
+- Setting up environment
+
+**Module 2: Jupyter & Data Structures (Week 2)**
+- Jupyter notebooks and reproducible workflows
+- Lists, dictionaries, tuples
+- Pandas introduction
+- Importing CSV data
+
+**Module 3: Data Wrangling (Week 3)**
+- DataFrame manipulation
+- Filtering and subsetting
+- Aggregating data
+- GroupBy operations
+
+**Module 4: Advanced Data Manipulation (Week 4)**
+- Working with dates and times
+- String operations
+- Relational data and joins (SQL-style in pandas)
+- Merging DataFrames
+
+**Module 5: Data Visualization (Week 5)**
+- Matplotlib basics
+- Seaborn for statistical plots
+- Exploratory data analysis with visuals
+- Best practices for effective visualization
+
+**Module 6: Writing Efficient Code (Week 6)**
+- Control flow (if/else, loops)
+- Functions and modularity
+- List comprehensions
+- Code efficiency and readability
+
+**Week 7: Midterm Project**
+- Application of Modules 1-6
+- Work with messy, real datasets
+- Open-ended analysis problem
+
+**Module 7: Machine Learning Intro (Week 8)**
+- What is ML and when to use it?
+- Train/test split
+- Features and labels
+- Model building process
+
+**Module 8: Regression (Week 9)**
+- Correlation analysis
+- Linear regression with scikit-learn
+- Model evaluation (R², RMSE)
+- Interpretation
+
+**Module 9: Classification (Week 10)**
+- Logistic regression
+- Classification metrics (accuracy, precision, recall, F1)
+- Confusion matrices
+- When to use classification vs regression
+
+**Module 10: Tree-Based Models (Week 11)**
+- Decision trees
+- Random forests
+- Feature importance
+- Model interpretation
+
+**Module 11: Model Optimization (Week 12)**
+- Feature engineering
+- Cross-validation
+- Hyperparameter tuning (GridSearchCV)
+- Model selection
+
+**Module 12: Advanced Topics (Week 13)**
+- Unsupervised learning (clustering, PCA)
+- Deep learning overview
+- Introduction to LLMs and GenAI concepts
+
+**Week 14: Final Project**
+- Comprehensive data science project
+- Full pipeline from data cleaning to modeling
+
+## Assessment Approach
+
+**Grading Components:**
+- **Labs** - Weekly hands-on activities (Thursdays)
+- **Homework** - Applied assignments (with answer keys for instructor)
+- **Midterm Project** - Comprehensive application of Modules 1-6
+- **Final Project** - End-to-end data science project
+- **Quizzes** - Knowledge checks (materials in `/planning/quizzes/`)
+
+**Student Support:**
+- Canvas discussion boards for peer collaboration
+- Office hours
+- Answer keys provided for labs and homework (instructor use)
+- Multiple formats (notebook, HTML, PDF) for accessibility
+
+**GenAI Policy:**
+- **Encouraged** to use ChatGPT, Claude, Copilot as learning aids
+- **Required** to understand code, not just copy it
+- Emphasis on using AI to learn, not to avoid learning
+- Students asked to reflect on AI tool use and limitations
+
+**Project Structure:**
+- Templates provided for major assignments
+- Rubrics included in `/planning/projects/`
+- Real-world datasets required
+- Open-ended problems that require creative problem-solving
+
+## Content Format
+
+**Textbook:** Quarto book with modules 1-6 + appendices
+**Slides:** Weekly presentations using Quarto + Reveal.js
+**Examples:** Numbered sequence of Jupyter notebooks (01-17)
+**Labs:** Weekly hands-on activities with answer keys
+**Homework:** Individual assignments with solutions in multiple formats
+**Datasets:** Real-world data in `/data/` directory (retail, airlines, housing, etc.)
+
+## Course Materials Repository
+
+All materials maintained in Git repository with structure:
+- `/book/` - Textbook chapters
+- `/slides/` - Weekly presentations
+- `/example-notebooks/` - Companion code examples
+- `/labs/` - Hands-on activities
+- `/homework/` - Assignments
+- `/data/` - Datasets
+- `/planning/` - Instructor resources (Canvas docs, rubrics, quizzes)

skills/courses/ml-in-business/course-profile.md

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+# ML in Business - Course Profile
+
+**Status:** 🚧 Placeholder - To be filled from syllabus
+
+## Audience
+[To be filled: student level, background, prerequisites]
+
+## Learning Philosophy
+[To be filled: teaching approach, pedagogical style]
+
+## Technical Stack
+[To be filled: libraries, tools, environment]
+
+## Content Style
+[To be filled: explanation style, depth, examples]
+
+## Key Topics
+[To be filled: weekly or unit breakdown]
+
+## Assessment Approach
+[To be filled: how students are evaluated]
+
+---
+
+**Next step:** Provide the course syllabus to populate this profile.

skills/courses/statistical-computing/course-profile.md

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+# Statistical Computing - Course Profile
+
+**Status:** 🚧 Placeholder - To be filled from syllabus
+
+## Audience
+[To be filled: student level, background, prerequisites]
+
+## Learning Philosophy
+[To be filled: teaching approach, pedagogical style]
+
+## Technical Stack
+[To be filled: libraries, tools, environment]
+
+## Content Style
+[To be filled: explanation style, depth, examples]
+
+## Key Topics
+[To be filled: weekly or unit breakdown]
+
+## Assessment Approach
+[To be filled: how students are evaluated]
+
+---
+
+**Next step:** Provide the course syllabus to populate this profile.

skills/pedagogy/teaching-principles.md

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+# 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