# 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