Syllabus#

The course is divided into a set of interactive lectures and labs. Lectures are meant to provide students with concepts and theories. Labs are self-directed for practising programming in Python. Use this time to complete assignments in class with ample support from TAs. See the syllabus on Brightspace for the weekly themes.

Format#

Six weeks of:

  • At-home Prep. Materials: videos, podcasts, articles… 1h. approx. (most recommended!)

  • 2x 1h. Lectures: concepts, methods, examples, crtiical discussions

  • 1x 2h. Labs: hands-on, application of concepts, Python (highly employable)

  • Further readings (optional!): how to go beyond this course

Content#

  • Weeks 1-3: “big picture” lectures + introduction to computational tools (learning curve) + lots and lots of data + lots of visualisation

  • Weeks 4-6: lots of spatial, network and machine learning concepts + responsibility

  • Weeks 7: prepare an awesome final project

Logistics#

  • Course Material: This website only!

  • Recordings of Lectures: Lectures are not recorded.

  • Announcements, Submission + Feedback, Group Formation + Peer Review and Grading: Brightspace

Self-directed learning#

  • Prepare for the lectures and labs

  • The labs will start with a short introduction and a question round but there will be no leading/lecturing at the computer labs. TAs will be present for abundant help and feedback

  • Go over the notebooks before the lecture and the lab

  • If the first time you see a notebook is at the lab, you may struggle to catch up. The best thing to do is to go over the notebooks at home and prepare a set of questions to ask the TAs.

  • Bring questions, comments, feedback, (informed) rants to class/labs. The more you bring, the more we all learn.

  • Collaborate (it’s NOT a zero-sum game!!!)

Assessment#

The summative assessments are graded components and contribute to the final mark for the course as follows:

  • Assignment 1 (10%)

  • Assignment 2 (10%)

  • Assignment 3 (10%)

  • Assignment 4 (10%)

  • Final Project (60%)

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 your favorite browser, search engine, large language model + Stack Overflow (be mindful that chatGPT is a stochastic parrot and cannot replace humans in critical thinking, learning and inference)