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.
An overview of all course sessions#
Week |
Lecture |
Topic |
Learning Goals |
Python Libraries |
Labs [1] |
|
|---|---|---|---|---|---|---|
W1 |
L1 |
Introduction to Spatial Data Science |
Anaconda and Jupyter, Numpy |
Lab 0 + 1 |
||
L2 |
Spatial and Urban Data |
“ |
||||
W2 |
L3 |
Data Grammar |
Obtain, Discuss |
Pandas, Seaborn |
Lab 2 |
|
L4 |
Data Engineering |
Manipulate, consolidate |
Pandas |
“ |
||
W3 |
L5 |
EDA and Visualisation |
Discuss, manipulate and Consolidate |
Geopandas, Matplotlib, Rasterio |
Lab 3 |
Assignment 1 |
L6 |
Geo-Visualisation |
Interpret |
“ |
|||
W4 |
L7 |
Networks and Spatial Weights |
Describe, Analyse |
Networkx, Osmnx, Pysal |
Lab 4 |
|
L8 |
Exploratory Spatial Data Analysis |
Describe, Analyze |
“ |
|||
W5 |
L9 |
Machine Learning for Everyone |
Apply |
Sklearn, Scipy, Statsmodels |
Lab 5 |
Assignment 2 |
L10 |
Anatomy of a Learning Algorithm |
Infer |
“ |
|||
W6 |
L11 |
Clustering |
Apply |
Pysal, Sklearn-Cluster |
Lab 6 |
|
L12 |
Dimensionality Reduction |
Apply |
“ |
|||
Winter Break |
||||||
W7 |
L13 |
Spatial Density Estimation |
Infer |
More Sklearn |
Lab 7 |
Assignment 3 |
L14 |
Responsible Data Science |
Create |
“ |
|||
W8 |
Project Preparation |
|||||
W10 |
Final Project [4] |
Format#
Seven weeks of:
At-home Prep. Materials: videos, podcasts, articles… 1h. approx. (most recommended!)
2x 1h. Lectures: concepts, methods, examples, crtiical discussions
2x 2h. Labs: hands-on, application of concepts, Python (highly employable)
Further readings (optional!): how to go beyond this course
Content#
Weeks 1-4: “big picture” lectures + introduction to computational tools (learning curve) + lots and lots of data + lots of visualisation
Weeks 5-7: lots of spatial, network and machine learning concepts + responsibility
Weeks 8-10: wrap up + prepare an awesome final project in groups
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
I won’t be 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 (15%)
Assignment 2 (15%)
Assignment 3 (20%)
Final Project (50%)
A note on exams#
Time-constrained exams do not measure any learning. Putting students under high-stakes environments only benefit those who can recall knowledge under pressure and is a filtering mechanism. In my opinion, that is a uselesss life-skill. This course does not have any exams.
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)