Skip to content

DSSG-EUROPE/dssg-europe-syllabus

master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code

Overview and Software Setup

This repository contains teaching resources that we will use over the fellowship. It is supplementary to the DSSG Hitchiker's Guide and heavily sourced from it, which is an invaluable resource for doing any DSSG project. This repository is tailored specifically to the tutorials/classes we will be giving over the 2018 summer fellowship in Lisbon.

Technical support:

  • Nuno Brás - lead technical mentor
  • Qiwei Han - technical mentor
  • William Grimes - junior technical mentor
  • Iñigo Martínez de Rituerto de Troya - infrastructure and technical support
  • João Fonseca - infrastructure support

Technical mentor’s role:

  • Project mentor/consultant on technical side
  • Core infrastructure maintenance (data, computing resources)
  • Technical training/support

Ask us anything about technical stuff. We will try our best to help you address the difficulties or direct you to the right person whenever necessary.

Local software setup for tutorials and projects

For more detail on the software setup have a look here.

Try it out! You should give all installed software a quick spin to check that it did install. For your Python packages, try to import them. Type Python in your shell, and then once you are in your Python session, try for example import numpy, import matplotlib, and so on. (You can quit with exit().) Also try iPython and jupyter notebook in your terminal, and see if you get any errors.

Working in the cloud

Project work over the summer will be done in a cloud computing environment, where each project will have a seperate server (AWS EC2 instance) as their main server for large-scale data processing tasks, and a database to securely store the data. This is advantageous since data is maintained in one place, teams can collaborate easily, and you have access to scalable computing resources.

stack

Good news: DSSG is supported by Amazon Web Service Cloud Credits for Research program and Microsoft Azure for Research awards! Amazon Web Service (AWS) https://aws.amazon.com/

Each fellow will be assigned a user account that allows you to make use of AWS service

Checklist

  1. Have all software installed, running, and tested locally
  2. Have a Github account created
  3. Join the two DSSG github organisations:
    • DSSG Chicago - input your github username at this link.
    • DSSG Europe - you will have received an invite through e-mail.
  4. Try to SSH into the training instance using your saved private key
    ssh -i ~/path/to/pemfile.pem username@35.176.252.186

Technical Syllabus

This summer we go through a set of modules that will help you starting and/or growing as DataScientists for Social Good. Each session is briefly identified in square brackets in the calendar, like

[All] [TERMINAL] 1 command line basics

which means

All to attend, Module TERMINAL, First lesson, about command line basics

1. Terminal Module

A set of lessons that introduce you and helps you to be productive while working in the terminal. This is specially important when working in virtual machines. The sessions are:

Sessions Week
Command line basics w1
Software versioning with git w1
SSH and the cloud w1

2. SQL Module

A set of lessons to make you dominate simple and advanced SQL (PostGres) and also some relevant database concepts.

Sessions Week
SQL basics w1
SQL advanced w3
Databases theory w3

3. Python Module

A module to make all on the same pace. We work with things like

  • Dictionaries and other structures
  • Functions, Classes and Objects, numpy, matplotlib
  • Python Code best practices
Sessions Week
Python beyond scripting w2

4. Data Module

Handling of data using Pandas; feature extraction, transformation, selection;

Sessions Week
Feature engineering w4

5. Machine Learning Module

From general introduction to machine learning concepts up to a set of algorithms adapted to your problems.

Sessions Week
ML intro w3
Quantitative Social Science w4
Causal inference w4
ML models 1 w4
ML models 2 w6

6. ETL Module

How to bring data science solutions to production architectures. Workflows and data streamings; Data Warehouses and Data Lakes.

Sessions Week
csvtodb and other simple data handling w2
DAGs and other workflow systems w5

Final Syllabus

Modules Sessions Week
TERMINAL Command line basics w1
TERMINAL Software versioning with git w1
TERMINAL SSH and the cloud w1
SQL SQL basics w1
PYTHON Python beyond scripting w2
ETL csvtodb and other simple data handling w2
TERMINAL git advanced w2
SQL SQL advanced w3
SQL Databases theory w3
ML ML intro w3
DATA Feature engineering w4
ML Quantitative social science w4
ML Causal inference w4
ML ML models 1 w4
ETL DAGs and other workflow systems w5
ML ML models 2 w6

Extra: Special Sessions

  • This sessions should be given in order to fulfill lack of knowledge in specific areas that could make a huge difference to some groups;
  • They are not compulsory.
  • They actually can be given by fellows!

here are some examples:

Special Sessions
Web Scrapping
GIS analysis
Network Analysis
Text Analysis
Record Linkage
Optimization

About

Teaching materials for DSSG Europe 2018, supplementary to the Hitchhiker's Guide.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published