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Sample curriculum for Summer 2019

	monday	tuesday	wednesday	thursday	friday
every week	Fellowship-wide Check-in (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	Prpject Scoping How does a project go over the summer	technical environment setup, cmd line, workflow git Interacting with project partners	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