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