The course is divided into a set of interactive lectures, labs and discussions. Lectures are meant to provide students with concepts and theories. Labs are for practising programming in Python and will be self-directed. Discussions are an extremely important part of learning, and students usually bring some great insights from reading research papers.
An overview of all course sessions
|Week||Lecture + Discussion||Topic||Learning Goals||Python Libraries||Labs and Homework 1||Assessment 23|
|W1||L1||Introduction to Urban Data Science||Anaconda and Jupyter, Numpy||Lab 0 + 1|
|L2||Spatial and Urban Data|
|W2||L3 + D1||Data Grammar||Obtain, Discuss||Pandas, Seaborn||Lab 2|
|L4||Data Engineering||Manipulate, consolidate||Pandas||Assignment 1|
|W4||L5 + D2||EDA and Visualisation||Discuss, manipulate and Consolidate||Geopandas, Matplotlib, Rasterio||Lab 3|
|W5||L7 + D3||Networks and Spatial Weights||Describe, Analyse||Networkx, Osmnx, Pysal||Lab 4|
|L8||Exploratory Spatial Data Analysis||Describe, Analyze||Assignment 2|
|W6||L9 + D4||Machine Learning for Everyone||Apply||Sklearn, Scipy, Statsmodels||Lab 5|
|L10||Anatomy of a Learning Algorithm||Infer|
|W7||L11 + D5||Clustering||Apply||Pysal, Sklearn-Cluster||Lab 6|
|L12||Dimensionality Reduction||Apply||Assignment 3|
|W8||L13 + D6||Spatial Density Estimation||Infer||More Sklearn||Lab 7|
|L14||Responsible Data Science||Create|
|W10||Final Project 4|
Seven weeks of:
- Prep. Materials: videos, podcasts, articles… 1h. approx. (most recommended!)
- 2x 1h. Lectures: concepts, methods, examples
- 1h. Paper Discussions: reading a paper and debating a set of questions with your peers in small groups. (extremely important if you are interested in applying concepts to real-world problems). Please read the paper before coming to the discussion session so you can have a more informed and informal debate with your peers.
- 2h. Computer labs: hands-on, application of concepts, Python (highly employable)
- Further readings: how to go beyond this course
- 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
- Course Material: This website
- Recordings of Lectures: Lectures are not recorded.
- Announcements, Discussion Forums, Submission + Feedback, Group Formation + Peer Review and Grading: Brightspace
- 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!!!)
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
This course has no exams. Time-constrained exams do not measure learning. Putting students under high-stakes environments only benefit those who can recall knowledge under pressure. In my opinion, that is a uselesss life-skill.
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
- Discussion Forum: students are encouraged to contribute meaningfully to the online discussion forum set up for the course on Brightspace. Meaningful contributions include both questions and answers that demonstrate the student is committed to make the forum a more useful resource for the rest of the group. This also enables your peers to learn from your questions and help each other.
In-class Labs are interactive Jupyter notebooks for learning how to program. Each lab is accompanied by homework exercises for practice. The only the way to learn how to program is by practicing it. Homeworks are not graded, but your peers could give you feedback. We will provide some tips to facilitate this. Constructive feedback from other people is an excellent way to learn. ↩︎
Graded Assignments are individual activities and are released two weeks prior on Monday. Due at the end of the specified working week on Friday at 2330. ↩︎
Grades and feedback released two weeks after on Wednesday at 1800. ↩︎
Final Project is a group activity. ↩︎