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DSSG Hitchhickers guide
Data stories concept and code
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DSSG Hitchhickers guide
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Home
What is in this curriculum?
DSSG Locations
DSSG Manual (for summer fellows)
DSSG Manual (for summer fellows)
What is in this manual
Goals of the Fellowship
The DSSG Environment
Summer overview
Code of conduct
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
Scoping overview
Project workflow
Data Maturity evaluation
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
Introduction to EDA
Visualization
SQL
Python/Pandas
GIS
Text
Network
Tableau
Data stories concept and code
ML as a data exploration tool (Clustering)
Computational and Data Analysis Methods
Computational and Data Analysis Methods
Intro
Machine Learning
Causal inference methods
Social science methods
Other statistical analysis methods
OR/optimization 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
How to deploy
Monitor
Update
Advanced pipelines
Data stories concept and code