Schedule

Advanced Data Science I

Week 1

• Class 1: Intro and Linux & Shell – Present (intro, basics)
  
• Class 2: Linux & Shell - Practice (hands-on commands)

Homework 1

• Homework 1: Homework 1

Week 2

• Class 3: SLURM and the Cluster
• Class 4: SLURM and the Cluster - Practice

Week 3

• Class 5: Power and Sample Size
• Class 6: Power – Practice

Week 4

• Class 7: R packages
• Class 8: Grants

Week 5

• Class 9: Present P1 (see Presentations)
• Class 10: SQL Basics and Hurdles

Week 6

• Class 11: Data Visualization – Lecture
  
• Class 12: Data Visualization (EDA) – Application

Week 7

• Class 13: (pushed from before) SQL Basics and Hurdles
  
• Class 14: App Dashboard – Setup & Workflow

Week 8

• Class 15: App Dashboard – Application
• Class 16: Final Presentations

Advanced Data Science II

Week 1

• Class 1: Performance Metrics Measurement & Models – Lecture
  
• Class 2: Tidymodels

Week 2

• Class 3: Tidymodels Workthrough
• Class 4: Prediction Competition – Presentation

Week 3

• Class 5: Missing Data
• Class 6: Missing Data – Application

Week 4

• Class 7: APIs + Pulling
• Class 8: Language Models – API (overview, usage)

Week 5

• Class 9: Pipelining Techniques – Lecture
  
• Class 10: Pipelining Techniques – Application

Week 7

• Class 11: Practical Bayes – Guest Lecture
• Class 12: Practical Bayes – Application

Week 6

• Class 13: Complex Data - Lecture
• Class 14: Complex Data - Application

Week 8

• Class 15: Presentation prep
• Class 16: Final Presentations (Data Applications)