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DSSG Hitchhickers guide DSSG Locations

dssg/hitchhikers-guide

DSSG Hitchhickers guide

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What is in this curriculum?
DSSG Locations DSSG Locations
Table of contents
- Host cities
DSSG Manual (for summer fellows)
DSSG Manual (for summer fellows)
Curriculum
Curriculum
- What is in this curriculum
- Sample curriculum for Summer 2019
- Skills you need throughout a project
  Skills you need throughout a project
  - Ethics, Bias, Fairness
  - Communication
    Communication
    
    Intro
    
    Presentations
    
    Writing reports
    
    Visualization
    
    User interface
  - Good Software Practices
    Good Software Practices
    
    Intro
    
    Good repos, good code
    
    Legible, good code
    
    Writing tests
    
    Reproducible software
    
    Pimp my dotfiles!
  - Domain Understanding
- Project Scoping
  Project Scoping
- Project deliverables
- Tech setup
  Tech setup
  - Software you need
  - Software setup session
  - Command line intro
  - Git and github
    Git and github
    
    What is it?
    
    Basic tutorial
    
    Group tutorial
    
    Advanced notes
    
    Git Workflow
    
    Git branching
  - Python
    Python
    
    Basic Python
    
    Python, Pandas and Viz
    
    Python and SQL
  - SQL
    SQL
    
    SQL Basics
    
    Postgres Tips and Pitfalls
  - Good repos
  - Technical workflow
- Getting, storing, and linking data
  Getting, storing, and linking data
  - Intro
  - Data security
  - Get data
    Get data
    
    APIs and scrapping
    
    Working with images
    
    Working with text
    
    Flat files
  - Store data
    Store data
    
    ETL - cleaning, loading
    
    DBs
    DBs
    
    Why a DB?
    
    Designing a DB
    
    Getting data in
    
    Getting data out
    
    Analyzing data (SQL)
    
    Other types of DBs
  - Link data
    Link data
    
    Record linkage
- Data Exploration
  Data Exploration
- Computational and Data Analysis Methods
  Computational and Data Analysis Methods
- Problem Templates in Social Good and Public Policy
- Practical Steps in Using Machine Learning to Solve Social Problems
  Practical Steps in Using Machine Learning to Solve Social Problems
  - Building an ML Pipeline
    Building an ML Pipeline
    
    ML pipeline I
  - Set up problem
    Set up problem
    
    ML problem formulation
    
    ML Checklist
    
    Templates of policy problems
  - Labels/Outcomes
    Labels/Outcomes
    
    One or many
    
    Implications of a label
  - Features/Predictors
    Features/Predictors
    
    Feature engineering
    
    Workshop on feature engineering
    
    Case studies
  - Validation Methodology
    Validation Methodology
    
    Process and goal
    
    K-fold cross-validation
    
    Temporal cross-validation
    
    Field trials
  - Evalution Metrics
    Evalution Metrics
    
    Overview
    
    Examples
  - Models/Methods
    Models/Methods
    
    Machine learning methods
    
    Practical tips on how to use them, parameters, etc.
  - Model selection
    Model selection
    
    Audition
    
    What metrics do we care about?
    What metrics do we care about?
    
    Performance
    
    Stability
    
    Interpretability
    
    Bias
  - Postmodeling
    Postmodeling
    
    Model understanding
    
    Feature importance
    
    Comparing different models
    
    Comparing lists
    
    Error analysis
    
    Bias analysis
  - Experimental design
    Experimental design
    
    Experiment design
    
    Case studies
- Deployment and Maintenance
  Deployment and Maintenance

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DSSG Locations

Host cities#

Year	Host City	Country
2013	Chicago	USA
2014	Chicago	USA
2015	Chicago	USA
2016	Chicago	USA
2017	Lisbon	Portugal
2018	Lisbon	Portugal
2018	Chicago	USA
2019	Warwick	UK
2019	London	UK
2022	Pittsburgh	USA