TESS VU - MUSA 5080 PORTFOLIO
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WEEKLY NOTES
ALL NOTES
WEEK 1: COURSE INTRO
WEEK 2: DYPLR BASICS
WEEK 3: EXPLORATORY DATA ANALYSIS (EDA) & VISUALIZATION
IN-CLASS LAB 3
WEEK 4: SPATIAL DATA & GIS OPERATIONS IN R
IN-CLASS LAB 4
WEEK 5: PREDICTIVE MODELING WITH LINEAR REGRESSION
IN-CLASS LAB 5
WEEK 6: SPATIAL MACHINE LEARNING & ADVANCED REGRESSION
IN-CLASS LAB 6
WEEK 7: MODEL DIAGNOSTICS & SPATIAL AUTOCORRELATION
IN-CLASS LAB 7
WEEK 8: MIDTERM PROJECT
WEEK 9: CRITICAL PERSPECTIVES ON PREDICTIVE POLICING
IN-CLASS LAB 9
WEEK 10: SPATIAL MACHINE LEARNING & ADVANCED REGRESSION
IN-CLASS LAB 10
LABS
ALL LABS
LAB 1: CENSUS DATA
LAB 2: HEALTHCARE ACCESS AND EQUITY
LAB 3: MIDTERM PROJECT
LAB 4: SPATIAL PREDICTIVE ANALYSIS
LAB 5: SPACE-TIME PREDICTION OF BIKE SHARE DEMAND
MIDTERM PROJECT
MIDTERM PROJECT
MIDTERM SLIDES
MIDTERM APPENDIX
FINAL PROJECT
FINAL PROJECT
FINAL SLIDES
FINAL APPENDIX
30-DAY MAP CHALLENGE
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Key Concepts Learned
Coding Techniques
Questions & Challenges
Reflection
LOGISTIC REGRESSION FOR BINARY OUTCOMES
WEEK 10 NOTES
Author
Tess Vu
Published
November 10, 2025
Key Concepts Learned
Coding Techniques
Questions & Challenges
Reflection