Foundations of Data Science for Data Analysis Professionals

CLASS INFO

• Semester: Fall, 2026
• Class time/ Class room: 18:35-21:20/ Mirae Building
• Text book: -

CONTENTS

• Practice links
  1. [Week 2] Python programming
  2. [Week 2 supplementary] Conditions, control flow, and loops
  3. [Week 3] Examples of machine learning models
  4. [Week 4] Introduction to regression models
  5. [Weeks 5–6] Further regression model exercises
  6. [Week 7] Fundamentals of classification analysis
  7. [Week 8] Introduction to classification modeling
  8. [Extra] Fitting a default prediction model
  9. [Extra] Fitting a default prediction model (example)
  10. [Week 10] Multiclass classification exercise 1
  11. [Week 10] Multiclass classification exercise 2
  12. [Week 11] Computation with batch data
  13. [Extra] Image preprocessing
  14. [Week 12] Building a CNN classifier
  15. [Week 12-1] MNIST dataset I
  16. [Week 12-2] MNIST dataset II
  17. Numeric data
  18. [Week 13] CNN exercise 1
  19. [Week 13] CNN exercise 2
  20. [Week 13] CNN exercise 3
  21. [Week 13] CNN exercise 4
  22. [Week 14] CNN exercise 5