This course

Quiz

• Can you think of a real-world context where data and statistics are being used to make a difference?
• 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

Logistics - Website

http://darribas.org/gds18

Logistics - Format

11 weeks of:

• Prep. materials: videos, podcasts, articles… 1h. approx. (most recommended!)
• 1h. Lecture: concepts, methods, examples
• 2h. Computer practical: hands-on, application of concepts, Python (highly employable)
• Further readings: how to go beyond the minimum

Logistics - Content

• Weeks 1-3: “big picture” lectures + introduction to computational tools (learning curve)
• Weeks 4-9: “meat” of the course (lots of concepts packed) + Week 7 break
• Weeks 10-11: catch up + prepare an awesome Computational Esssay

Code

Driving Vs automobile engineering

Python

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 for the labs

• I won’t be leading/lecturing at the computer labs
• Go over the notebooks before the lecture and the computer lab –> If the first time you see a notebook is at the lab, you won’t be able to follow on
• Bring questions, comments, feedback, (informed) rants to class/labs
• Use the forum (link on VITAL)
• 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

Assignments

• In-lab computer tests: W.5 (25%) 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)

The (geo-)data revolution

The (geo-)data revolution

Exciting times to be a:

• Geographer
• Map fan
• Data fan

The world is being “datafied”…

“Datafication”

Quantification of phenomena through the systematic recording of data, “taking all aspects of life and turning them into data” (Cukier & Mayer-Schoenberg)

Examples: credit transactions, public transit, tweets, facebook likes, spotify songs, etc.

“Datafication”

Many implications:

• Window into human behaviour (this course)
• Opportunities for optimization of systems (Industrial IoT, planning systems…)
• Issues with intentionality and privacy
• …

Why now?

The (geo-)data revolution

The confluence of the three (computing, communication and geospatial) is creating large amounts of data.

Now, data in itself is not very valuable:

• Data –> Information –> Knowledge –> Action

Data Science

Data Science

Statistics + …

Some examples…

Geo-Data Science

Geo-Data Science

• A (very) large portion of all these new data are inherently geographic or can be traced back to some location over space.
• Spatial is special.
• Some of the methods require an explicitly spatial treatment –> (Geo-)Data Science

Some examples…