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Geographic Data Science

Introduction [Dani Arribas-Bel](http://darribas.org)

This course

(Self-)Quiz

  • Have you ever used data to make decisions in your life?
  • Have you ever heard the term "Data Science"?
  • Have you ever written a line of computer code?

Philosophy

  • (Lots of) methods and techniques
    • General overview
    • Intuition
    • Very little math
    • Lots of ways to continue on your own
  • Emphasis on the application and use
  • Close connection to "real world" applications

Format

Eight blocks with:

  • Concepts: videos + slides, readings
  • Hands-on: concepts in (interactive) action
  • Do-It-Yourself: practical material to do on your own

Content

  • **Blocks A-C**: "big picture" content + computational tools (learning curve)
  • **Blocks D-H**: "meat" of the course (lots of concepts packed)
  • *Rest of the course*: prepare an awesome Computational Esssay

Logistics - Website

https://darribas.org/gds_course

<iframe src="https://darribas.org/gds_course" width=600 height=400 ></iframe>

Logistics - Teams

Team

Code

<iframe width="853" height="480" src="https://www.youtube.com/embed/M_rfujuRHUU" frameborder="0" allowfullscreen></iframe>

Driving Vs automobile engineering

Python

![](../figs/l01_xkcd-python.png)

Python

    * **General purpose** programming language * Sweet spot between *"proof-of-concept"* and *"production-ready"* * Industry standard: **GIS** (Esri, QGIS) and **Data Science** (Google, Facebook, Amazon, Netflix, The New York Times, NASA...)

Self-directed learning

Prepare

  • This is a **flipped class**: it's like a gym, the "subscription" does not make you fit
  • **Bring** questions, comments, feedback, (informed) rants to Teams/labs
  • **Teams**, **Teams**, **Teams**
  • **Collaborate** (it's **NOT** a zero-sum win!!!)

More help!!!

This course is much more about "learning to learn" and problem solving rather than acquiring specific programming tricks or stats wizardry

  • Learn to **ask** questions (but don't expect exact answers all the time!!!)
  • **Help** others as much as you can (the best way to learn is to teach)
  • **Search** heavily on Google + Stack Overflow

Workflow - Before a Lab

  1. Go over the Concepts and Hands-on sections of a block
  2. Get started on the DIY
  3. Record questions and post them on Teams prior to the lab

Workflow - Online Labs

  1. Come work on the DIY sections
  2. Live answers to questions posted
  3. Support from demonstrators and module lead

Assignments

Assignments

  • Computer tests: W.5 (20%) and W.10 (25%)
  • Computational essay (W.12, 50%)
    • Equivalent to 2,500 word
    • Report (notebook) with code, figures (e.g. maps), and text
  • Discussion board (5%)

NOTE: recommendation letters only for great students (>70)

Creative Commons License
A Course on Geographic Data Science by Dani Arribas-Bel is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.