gh-bradleyboehmke-brads-marketplace-course-builder/skills/courses/bana-4080/course-profile.md

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