Initial commit

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

@@ -0,0 +1,139 @@
+# 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