Schedule

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: 

  • Attending 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 Borislav (Bobby) Ikonomov (BI).
  • Testing yourself with the comprehension questions in Learn afterwards.
  • Doing the lab notebook - in S1 Weeks 1 and 2 there will be lab sessions in Appleton Tower with demonstrators, to get you started. After then, the labs are self-study, but there will be support from lab demonstrators in InfBase.
  • Attending the workshops - preparation the week before is ideal, but if you've not managed to prepare, you should get something from the workshops. All the workshops are designed to help you learn what we'll be assessing in the coursework and exam.

Coursework

There is 1 coursework. You can see the schedule in the "Coursework Planner" in Learn 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.

Office hours

On Wednesdays with lectures, the office hour will be at 12 noon, after the lecture in the Appleton Tower Cafe.

On Wednesdays without lectures, the office hour will be at 11:10; meet outside LT5 in Appleton Tower.

Schedule

WeekLecture 1Lecture 2LabTask/workshopReading
Part I: Data: ethics, collection, representation, wrangling, exploration, visualisation and descriptive statistics
S1 W1    
15-19 Sep
Introduction and Logistics (KG)Data (KG)Introduction to Jupyter notebooks and Pandas Lecture Notes (LN) 1, 2
S1 W2    
22-26 Sep
Descriptive statistics (KG)    
 
Introduction to data ethics (KG)Pandas - Data wrangling IPreparation for data ethics workshopLN 3,  4
An Introduction to Data Ethics, Parts 1 and 2
S1 W3    
29 Sep - 3 Oct
Exploratory data analysis, data communication visualisation (DS)No lectureVisualisation - Exploratory Data AnalysisWorkshop: Data ethics discussionLN 5,  
The Big Book of Dashboards, Chapter 1
S1 W4
6-10 Oct
Visualisation (DS)Data collection and statistical relationships (KG)Visualisation - Data communicationPreparation for visualisation workshop
Visualisation exercise
LN 6
Part II: Linear Models
S1 W5    
13-17 Oct
Linear regression I (BI)Linear regression II (BI)Data wrangling IIWorkshop: Discussing visualisations
Visualisation exercise
LN 7
S1 W6
20-24 Oct
Visualisation exercise  feedback Q&ANo lecture   
S1 W7    
27-31 Oct
Multiple regression I (BI)Multiple regression II (BI)Linear modelsWorkshop: Linear regressionLN 8, 9
S1 W8    
3-7 Nov
Principal Components Analysis I (BI)Principal Components Analysis II (BI)PCATask: Preparation for Week 9 workshopLN 10
Part III: Introduction to Machine Learning
S1 W9    
10-14 Nov
Intro to supervised learning: Nearest neighbours (DS)k-Nearest Neighbours and Evaluation (DS)k-Nearest NeighboursWorkshop: Interpretation of data science study using multiple regressionLN 11, 12
S1 W10
17-21 Nov
Intro to unsupervised learning: K-means (DS)Formative exercise 2 feedback (BI)K-means LN 13
S1 W11    
24-28 Nov
No lectureNo lecture   
Part IV: Statistical inference
S2 W1
13-17 Jan
Intro to inferential statistics (DS)Randomness, sampling and simulation (DS)Randomness, sampling and simulations

Task: Critical evaluation preparation

 

LN 14
S2 W2
20-24 Jan
Estimation (DS)Confidence intervals (DS)Estimation of confidence intervals with the bootstrapWorkshop: Critical evaluationLN 15, 16
S2 W3
27-31 Jan
Hypothesis testing and p-values (DS)

 
A/B testing (DS)
 
No lab

Task: Problem sheet for S2 Week 4 Workshop

 

 

LN 17, 18,  
XKCD comic strip on multiple testing,  
A hypothesis is a liability
Part V: The maximum likelihood principle and regression
S2 W4
3-7 Feb
Inference of regression coefficients and logistic regression (KG)Logistic regression (KG)Logistic regressionWorkshop: Statistical problems 1LN 19, 20
S2 W5    
10-14 Feb
Ethical and legal issues in supervised learning (KG)Software engineering for data science (DS)Web-scrapingTask: Problem sheet for S2 Week 6 Workshop

LN 22, 23, Equality law can disadvantage women in algorithmic credit decisions

Good enough practices in scientific computing,  
Reproducible Analysis Through Automated Jupyter Notebook Pipelines,  
The scientific paper is obsolete

S2, FLW    
17-21 Feb
     
S2 W6    
24-28 Feb
Linear regression and inference (DS)Generalised linear models (DS) Workshop: Statistical problems 2LN 21
Part VI: Project and project skills
S2 W7
3-7 Mar
Project writing workshop in lecture slot (DS)No lecture   
S2 W8    
10-14 Mar
Guest lecture?No lecture Workshop: Project presentations 
S2 W9    
17-21 Mar
Guest lecture?No lecture Workshop: Project presentations 
S2 W10    
24-28 Mar
No lectureNo lecture Workshop: Project presentations 
S2 W11    
31 Mar - 4 Apr
     
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