| Part I: Data: ethics, collection, representation, wrangling, exploration, visualisation and descriptive statistics |
S1 W1
21-25 Sep | Introduction and Logistics (KG) | Data (KG) | Introduction to Jupyter notebooks and Pandas | | Lecture Notes (LN) 1, 2 |
S1 W2
28 Sep - 2 Oct | Descriptive statistics (KG)
| Introduction to data ethics (KG) | Pandas - Data wrangling I | Preparation for data ethics workshop | LN 3, 4
An Introduction to Data Ethics, Parts 1 and 2 |
S1 W3
5 - 9 Oct | Data collection and statistical relationships (KG) | No lecture | Data wrangling II | Workshop: Data ethics discussion Formative Visualisation Exercise Released | LN 6 |
S1 W4
12-16 Oct | Visualisation (DS) | Exploratory data analysis, data communication visualisation (DS) | Visualisation - Exploratory Data Analysis | Preparation for visualisation workshop
Formative Visualisation exercise Submission | LN 5,
The Big Book of Dashboards, Chapter 1 |
| Part II: Linear Models |
S1 W5
19-23 Oct | Linear regression I (BI) | Linear regression II (BI) | Visualisation - Data communication | Workshop: Discussing visualisations
Visualisation exercise | LN 7 |
S1 W6
26 - 30 Oct | Visualisation exercise feedback Q&A (BI) | No lecture | | | |
S1 W7
2-6 Nov | Multiple regression I (BI) | Multiple regression II (BI) | Linear models | Workshop: Linear regression
Formative Visualisation and interpretation exercise released | LN 8, 9 |
S1 W8
9-13 Nov | Principal Components Analysis I (BI) | Principal Components Analysis II (BI) | PCA | Task: Preparation for Week 9 workshop | LN 10 |
| Part III: Introduction to Machine Learning |
S1 W9
16-20 Nov | Intro to supervised learning: Nearest neighbours (BI) | k-Nearest Neighbours and Evaluation (BI) | k-Nearest Neighbours | Workshop: Critical evaluation and multiple regression
Formative Visualisation and interpretation exercise submitted | LN 11, 12 |
S1 W10
23-27 Nov | Intro to unsupervised learning: K-means (BI) | No lecture | K-means | | LN 13 |
S1 W11
30 Nov - 4 Dec | Formative exercise 2 feedback (BI) | No lecture | | | |
| Part IV: Statistical inference |
S2 W1
11-15 Jan | Intro to inferential statistics (BI) | Randomness, sampling and simulation (BI) | Randomness, sampling and simulations | Task: Critical evaluation preparation | LN 14 |
S2 W2
18-22 Jan | Estimation (BI) | Confidence intervals (BI) | Estimation of confidence intervals with the bootstrap | Workshop: Critical evaluation | LN 15, 16 |
S2 W3
25-29 Jan | Hypothesis testing and p-values (BI)
| A/B testing (BI)
| 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
1-5 Feb | Inference of regression coefficients and logistic regression (KG) | No lecture | Logistic regression | Workshop: Statistical problems 1 | LN 19, 20 |
S2 W5
8-12 Feb | Logistic regression (KG) | Ethical and legal issues in supervised learning (KG) | | Task: Problem sheet for S2 Week 6 Workshop | LN 22, Equality law can disadvantage women in algorithmic credit decisions |
S2, FLW
15-19 Feb | | | | | |
S2 W6
22-26 Feb | Project Q&A and Regression and inference (BI) | Generalised linear models (BI) | | Workshop: Statistical problems 2 | LN 21 |
| Part VI: Project and project skills |
S2 W7
1-5 Mar | Project writing workshop in lecture slot (BI) | No lecture | | | |
S2 W8
8-12 Mar | Guest lecture | No lecture | | | LN 23 |
S2 W9
15-19 Mar | No lecture | No lecture | | Workshop: Project presentations | |
S2 W10
22-26 Mar | No lecture | No lecture | | Workshop: Project presentations | |
S2 W11
29 Mar - 2 Apr | No lecture | No lecture | | | |
| Revision week | Revision session | | | | |
