Schedule¶
A pre-class online week, two in-class modules on campus (seven teaching days), and a post-class online week. Each period is 45 minutes. Mornings are theory; most afternoons end with a hands-on lab where you complete graded work.
Daily timetable on campus¶
| Day | Morning | Afternoon | Late afternoon / evening |
|---|---|---|---|
| Tuesday | 08:10–11:40 Theory | 13:45–15:25 Theory | 15:40–17:20 Lab |
| Wednesday | 08:10–11:40 Theory | 13:45–15:25 Theory | 15:40–20:10 Lab (with a dinner break) |
| Thursday | 08:10–11:40 Theory | 13:45–17:20 Lab | — |
| Monday 16 Nov | 08:10–11:40 Theory | — | — |
Lunch is from 11:40. Friday to Sunday there are no classes, but you work on assignments and can ask for online support.
Pre-class week (online, before Tuesday 10 November)¶
- Follow the setup guide and run the setup check.
- Work through Python and Jupyter basics at your own pace.
- Complete Activity 1: Readiness check, due Monday 9 November.
In-class Module 1¶
Day 1 · Tuesday 10 November¶
Theory
- The course, its outcomes and graded work
- How data and analytics improve decisions
- Descriptive, diagnostic, predictive and prescriptive analytics
- Why statistics alone can mislead
- Exploring data in Python: data quality, descriptive statistics, pivot tables, correlation and confounding
Pages: Data-driven decisions · Exploring data in Python
Lab: Applied Lab 1, data exploration (diamond pricing).
Day 2 · Wednesday 11 November¶
Theory
- Data visualisation principles and misleading charts
- Communicating results: storytelling, dashboards and KPIs
- Marketing analytics
- Building dashboards in Tableau; Assignment 1 briefing
Pages: Visualising and communicating results · Building dashboards in Tableau
Lab: Applied Lab 2, dashboard creation in Tableau. Start Assignment 1.
Day 3 · Thursday 12 November¶
Theory
- Midterm exam (first two periods), covering Days 1–2
- Text mining: unstructured text, cleaning, word frequency and sentiment
- Forecasting: trend, seasonality, smoothing, ARIMA and forecast error
Pages: Text mining · Forecasting
Lab: Applied Lab 3, text mining, and Applied Lab 4, forecasting.
Weekend · Friday 13 – Sunday 15 November¶
- Finish Assignment 1, due Sunday 15 November.
- Online support is available.
In-class Module 2¶
Day 4 · Monday 16 November¶
Theory
- Predictive analytics
- Simple and multiple linear regression
- Judging a model: R², error and testing on new data
- Data mining with text features; Assignment 2 briefing
Pages: Predictive analytics and regression · Data mining with text features · Assignment 2
Day 5 · Tuesday 17 November¶
Theory
- Financial modelling
- Spreadsheet simulation in Excel: random inputs, distributions, Data Tables and scenarios
Pages: Financial modelling and simulation
Lab: Applied Lab 5, predictive linear regression modelling.
Day 6 · Wednesday 18 November¶
Theory
- Business intelligence strategy
- BI case studies, ethics and societal context
- Assignment 3 briefing
Pages: BI strategy and ethics · Assignment 3
Lab: Applied Lab 6, spreadsheet simulation, and Applied Lab 7, BI case study.
Day 7 · Thursday 19 November¶
Theory
- Course review
- Final exam
Pages: Course review
Lab: Capstone (Assignment 4) launch and studio; support for Assignments 2 and 3.
Post-class week and deadlines¶
All deadlines are 23:59 China Standard Time on the date shown. Submit everything on GAP (Moodle).
| Work | Deadline |
|---|---|
| Activity 1 | Monday 9 November |
| Assignment 1 | Sunday 15 November |
| Labs 1–7 | End of the day they are set, unless your facilitator says otherwise |
| Assignment 2 and Assignment 3 | Sunday 22 November |
| Assignment 4 (capstone) | Sunday 29 November |