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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)