| Data: ethics, collection, representation, wrangling, exploration, visualisation and descriptive statistics |
S1 W1
16-20 Sep | Introduction and Logistics (KG) | Data (KG) | Introduction to Jupyter notebooks and Pandas | | Lecture Notes (LN) 1, 2 |
S1 W2
23-27 Sep | Descriptive statistics (KG)
| Introduction to data ethics (KG) | Pandas - Data wrangling I | Task: Preparation for Week 3 Workshop on Ethics. | LN 3, 4
An Introduction to Data Ethics, Parts 1 and 2 |
S1 W3
30 Sep-4 Oct | Exploratory data analysis, data communication visualisation (DS/NR) | Visualisation (NR) | Data representation I - Matplotlib | Workshop: Data ethics discussion | LN 5,
The Big Book of Dashboards, Chapter 1 |
S1 W4
7-11 Oct | No Lecture | Data collection and statistical relationships (KG) | Data representation II: Distributions | Task: Marking visualisations and interpretation in previous CW1 | LN 6 |
| Linear Models |
S1 W5
14-18 Oct | Linear regression I (WL) | Linear regression II (WL) | Data wrangling II | Workshop: Discussing visualisations and interpretation in previous CW1 | LN 7 |
S1 W6
21-25 Oct | No lecture | Coursework Q&A | CW1 Lab | | |
S1 W7
28 Oct-1 Nov | Multiple regression I (WL) | Multiple regression II (WL) | Linear models
and CW1 | | LN 8, 9 |
S1 W8
4-8 Nov | Principal Components Analysis I (WL) | Principal Components Analysis II (WL) | PCA | Task: Preparation for Week 9 workshop | LN 10 |
| Introduction to Machine Learning |
S1 W9
11-15 Nov | No Lecture | Intro to supervised learning: Nearest neighbours (KG) | k-Nearest Neighbours | Workshop: Interpretation of data science study using multiple regression | LN 11 |
S1 W10
18-22 Nov | k-Nearest Neighbours and Evaluation (KG) | Intro to unsupervised learning: K-means (KG) Followed by feedback on CW1 (DS) | K-means | | LN 12, 13 |
S1 W11
25-29 Nov | No lecture | No lecture | | | |
| Statistical inference |
S2 W1
13-17 Jan | Intro to inferential statistics (DS) | Randomness, sampling and simulation (DS) | Randomness, sampling and simulations | | LN 14 |
S2 W2
20-24 Jan | Estimation (DS) | Confidence intervals (DS) | Estimation of confidence intervals with the bootstrap | Task: Problem sheet for S2 Week 2 Workshop | LN 15, 16 |
S2 W3
27-31 Feb | Hypothesis testing and p-values (DS)
| A/B testing (DS)
| No lab | Workshop: Statistical problems 1 Task: Critical evaluation preparation | LN 17, 18,
XKCD comic strip on multiple testing,
A hypothesis is a liability |
| The maximum likelihood principle and regression |
S2 W4
3-7 Feb | Inference of regression coefficients and logistic regression (DS) | Logistic regression (DS) | Logistic regression | Workshop: Critical evaluation | LN 19, 20 |
S2 W5
10-14 Feb | Linear regression and inference (DS) | Generalised linear models (DS) | Web-scraping | Task: Problem sheet for S2 Week 6 Workshop | LN 21 |
S2, FLW
17-21 Feb | | | | | |
S2 W6
24-28 Feb | Project Q&A and Ethical and legal issues in supervised learning (DS) | No lecture | | Workshop: Statistical problems 2 | LN 22, Equality law can disadvantage women in algorithmic credit decisions |
| Project and project skills |
S2 W7
3-7 Mar | Software engineering for data science (DS) | No lecture | | | Good enough practices in scientific computing,
Reproducible Analysis Through Automated Jupyter Notebook Pipelines,
The scientific paper is obsolete |
S2 W8
10-14 Mar | Project writing workshop in lecture slot (DS) | No lecture | | Workshop: Project presentations | |
S2 W9
17-21 Mar | Guest lecture:
"Data Science in Action: Exploring Edinburgh’s Student Housing Crisis"
Toby Wyckoff Smith (FDS alumnus) of
Slurp: Students for Action on Homelessness | No lecture | | Workshop: Project presentations | |
S2 W10
24-28 Mar | No lecture | No lecture | | Workshop: Project presentations | |
S2 W11
31 Mar-4 Apr | | | | | |
