INF2-FDS: Schedule | Open Course Materials

Timetable

The course has various learning activities, which are coordinated with each other and the assessment. The timetables are a bit different for Semester 1 and Semester 2:

Weekly activities

We recommend that each week you have a pattern of:

Doing the reading listed before the lectures. Most of the reading is from the FDS lecture notes:

FDS-lecture-notes-2024-04-18.pdf (6.46 MB)

Attend the lectures, which include exercises, discussion, Q&A, demos or feedback on exercises. The lectures are delivered by Kobi Gal (KG), David Sterratt (DS) and Michael Gutmann (MG).
Test yourself with the comprehension questions in Learn Ultra afterwards.
Do the lab notebook - in Weeks 1 to 7 there will be lab sessions in Appleton Tower with demonstrators; after then the labs are self-study
Attend the workshops - preparation the week before is ideal, but if you've not managed to prepare, you should get something from the workshops. Two of the workshops are designed to familiarise you with the coursework released shortly after, including how we mark it.

Coursework

There are 3 courseworks and one class test; you can see the schedule in Learn Ultra or import the data into your calendar and use the coursework calendar file https://course.inf.ed.ac.uk/calendar/inf2-fds.ics to get all the coursework events into your Outlook or Google calendar.

Schedule

Week	Lecture 1	Lecture 2	Lab	Task/workshop	Reading
Data: ethics, collection, representation, wrangling, exploration, visualisation and descriptive statistics
S1 W1 18-22 Sep	Introduction and Logistics (KG)	Data (KG)	Introduction to Jupyter notebooks and Pandas		Lecture Notes (LN) 1 and 2
S1 W2 25-29 Sep	No lecture	Descriptive statistics (KG)	Pandas - Data wrangling	Task: Preparation for Week 3 Workshop on Ethics.	LN 3, An Introduction to Data Ethics, Parts 1 and 2
S1 W3 2-6 Oct	Exploratory data analysis, data communication visualisation (DS)	Visualisation (NR)	Data representation I - Matplotlib	Workshop: Data ethics discussion	LN 5, The Big Book of Dashboards, Chapter 1
S1 W4 9-13 Oct	Intro to data ethics (KG)	Data collection and statistical relationships (KG)	Data representation II: Distributions	Task: Marking visualisations and interpretation in previous CW1	LN 4
Introduction to Machine Learning
S1 W5 16-20 Oct	Intro to supervised learning: Nearest neighbours (KG)	k-Nearest Neighbours and Evaluation (KG)	Data wrangling II	Workshop: Discussing visualisations and interpretation in previous CW1	LN 7
Linear Models
S1 W6 23-27 Oct	Linear regression I (MG)	Linear regression II (MG)	k-Nearest Neighbours		LN 10
S1 W7 30 Oct-3 Nov	No lecture	No lecture	No lab
S1 W8 6-10 Nov	Multiple regression I (MG)	Multiple regression II (MG)	Linear models	Task: Preparation for Week 9 workshop	LN 11 and 12
S1 W9 13-17 Nov	Principal Components Analysis I (MG)	Principal Components Analysis II (MG)	PCA	Workshop: Interpretation of data science study using multiple regression	LN 13
Statistical inference
S1 W10 20-24 Nov	CW1 feedback and intro to inferential statistics (DS)	Randomness, sampling and simulation (DS)	Randomness, sampling and simulations		LN 14
S1 W11 27 Nov-1 Dec	Estimation (DS)	Confidence intervals (DS)	Estimation of confidence intervals with the bootstrap		LN 15 and 16
S2 W1 15-19 Jan	Hypothesis testing and p-values (DS)	A/B testing (DS)	No lab	Task: Preparing for CW2	LN 17 and 18, XKCD comic strip on multiple testing, A hypothesis is a liability
S2 W2 22-26 Jan	Logistic regression (DS)	Logistic regression and CW2 Q&A	Logistic regression	Workshop: Preparing for CW2	LN 19
Introduction to Machine Learning
S2 W3 29 Jan-2 Feb	Intro to unsupervised learning: K-means (DS)	No lecture		Task: Problem sheet for S2 Week 4 Workshop	LN 9
S2 W4 5-9 Feb	Ethical issues with supervised learning (DS)	No lecture	K-means	Workshop: Statistical problems 1	Equality law can disadvantage women in algorithmic credit decisions
Regression and Inference
S2 W5 12-16 Feb	Linear regression and inference (DS)	Generalised linear models (DS)	Web-scraping	Task: Problem sheet for S2 Week 6 Workshop	LN 20
S2, FLW 19-23 Feb
Project and project skills
S2 W6 26 Feb-2 Mar	Software engineering for data science (AH+DS)	No lecture		Workshop: Statistical problems 2	Good enough practices in scientific computing , Reproducible Analysis Through Automated Jupyter Notebook Pipelines , The scientific paper is obsolete
S2 W7 5-9 Mar	Project Q&A (AH+DS)	No lecture
S2 W8 12-16 Mar	Project writing workshop (DS)	No lecture
S2 W9 19-23 Mar		No lecture		Workshop: Mid-project presentation (TA+DS)
S2 W10 26-30 Mar	No lecture	No lecture		Workshop: Mid-project presentation (TA+DS)
S2 W11 2-6 Apr

License