Sample curriculum for Summer 2019

monday tuesday wednesday thursday friday
every week Fellowship-wide Check-in (15-30 minutes in the morning) short updates from every team deep dives - 2 teams every week
  • Code Review
  • External Talk
  • Ethics Discussions
week 1 Software installation and check logins
  • Python for Data Analysis
  • ML Pipeline
  • Communications for the summer
Databases and SQL
week 2
  • Data Maturity Assessment
  • DSSG Project Deliverables
  • Ethics Overview
  • more dbs and ETL
  • Working in a team
  • Good software practices
  • no deep dive - partner session
Data Exploration (two sessions) - viz, pandas, sql, spatial
week 3
  • Policy Problem templates
  • Machine Learning Overview - formulation and validation
Intro to Social Sciences
  • sql
  • Record linkage
  • ML overview - validation
  • case study from previous dssg
week 4
  • Machine Learning overview - methods
  • TBD Non-technical session
Causal Inference with Observational Data Feature Engineering
  • ML overview - methods
  • User interfaces and usability
week 5 Text Analysis ML Pipelines - Deeper Dive Feature Engineering workshop Optimization
week 6
  • Model Validation
  • Model Interpretation
    Communications - speaking Audition and Overview of postmodeling Bias and Fairness
    week 7 Post-modeling Analysis Causal Inference - experiments Case Study Post-Modeling Analysis
    week 8 Recap of what needs to be done for the rest of the summer Experimental design TBD technical session - TBD
    week 9 Recap of what needs to be done Social Science methods TBD technical session - TBD
    week 10 Bias and Fairness in ML Communications - writing Image/Video analysis Network Analysis
    week 11
    week 12