Timetable¶
Seven teaching days on campus in two modules, with an online week before and after. Each period lasts 45 minutes. Theory comes first each day; most days end with a lab in which you complete graded work.
Periods¶
| Period | Time |
|---|---|
| P1 | 08:10–08:55 |
| P2 | 09:05–09:50 |
| P3 | 10:05–10:50 |
| P4 | 10:55–11:40 |
| Lunch | |
| P5 | 13:45–14:30 |
| P6 | 14:40–15:25 |
| P7 | 15:40–16:25 |
| P8 | 16:35–17:20 |
| Dinner | |
| P9 | 18:30–19:15 |
| P10 | 19:25–20:10 |
| Day | Periods used |
|---|---|
| Tuesdays (10 and 17 November) | P1–P8 |
| Wednesdays (11 and 18 November) | P1–P10 |
| Thursdays (12 and 19 November) | P1–P8 |
| Monday 16 November | P1–P4 |
Friday to Sunday there are no classes; use the time for assignments. The facilitator may move a topic by a period to suit the group; announcements in class take precedence over this page.
Pre-class week (online, before Tuesday 10 November)¶
| Do | Page |
|---|---|
| Set up JupyterLab, Tableau and Excel | Setup |
| Refresh Python, Jupyter and pandas | Python and Jupyter refresher |
| Read Chapter 1 of the reading packet (a first look) | Reading packet |
| Complete Activity 1 by Monday 9 November | Activity 1 guide |
Module 1¶
Day 1 · Tuesday 10 November¶
Reading: Chapter 1.
| Period | Time | Subject |
|---|---|---|
| P1 | 08:10–08:55 | Welcome: outcomes, graded work and the three environments (Foundations of data-driven decisions, Parts 1–2) |
| P2 | 09:05–09:50 | The analytics hierarchy: descriptive, diagnostic, predictive, prescriptive (Foundations of data-driven decisions, Part 3) |
| P3 | 10:05–10:50 | Framing a decision: from a business question to a statistic to an insight (Foundations of data-driven decisions, Parts 4–6) |
| P4 | 10:55–11:40 | Exploring data: data quality, missing values and summary statistics (Exploring data, Parts 1–3) |
| P5 | 13:45–14:30 | Pivot tables, grouped summaries and correlation (Exploring data, Parts 3–5) |
| P6 | 14:40–15:25 | Confounding and over-plotting; Lab 1 briefing (Exploring data, Parts 6–8; Lab 1 guide) |
| P7 | 15:40–16:25 | Lab 1: data exploration (diamonds) (Lab 1 guide) |
| P8 | 16:35–17:20 | Lab 1 continued; submit on GAP (Lab 1 guide) |
Day 2 · Wednesday 11 November¶
Reading: Chapter 2.
| Period | Time | Subject |
|---|---|---|
| P1 | 08:10–08:55 | Choosing a chart for the question (Visualising and communicating data) |
| P2 | 09:05–09:50 | Perception and misleading charts (Visualising and communicating data) |
| P3 | 10:05–10:50 | Communicating results: storytelling, KPIs and dashboards (Visualising and communicating data) |
| P4 | 10:55–11:40 | Marketing analytics (Visualising and communicating data) |
| P5 | 13:45–14:30 | Tableau fundamentals: data source, dimensions and measures, marks (Tableau concepts and dashboards) |
| P6 | 14:40–15:25 | Tableau dashboards; Lab 2 briefing (Tableau concepts and dashboards) |
| P7 | 15:40–16:25 | Lab 2: dashboard (Tableau Superstore) (Lab 2 guide) |
| P8 | 16:35–17:20 | Lab 2 continued; submit on GAP |
| P9 | 18:30–19:15 | Assignment 1 briefing (tips report) (Tableau concepts and dashboards, Part 10; Assignment 1 guide) |
| P10 | 19:25–20:10 | Assignment 1 studio (Assignment 1 guide) |
Day 3 · Thursday 12 November¶
Reading: Chapters 3 and 4.
| Period | Time | Subject |
|---|---|---|
| P1 | 08:10–08:55 | Midterm exam |
| P2 | 09:05–09:50 | Midterm exam |
| P3 | 10:05–10:50 | Text mining: tokens, stop words, word frequency and VADER sentiment (Text mining) |
| P4 | 10:55–11:40 | Time series and forecasting: stationarity, train and test, SARIMA, RMSE and MAE (Time series and forecasting) |
| P5 | 13:45–14:30 | Lab 3: text mining (OmniPad reviews) (Lab 3 guide) |
| P6 | 14:40–15:25 | Lab 3 continued; submit on GAP (Lab 3 guide) |
| P7 | 15:40–16:25 | Lab 4: forecasting (CO₂) (Lab 4 guide) |
| P8 | 16:35–17:20 | Lab 4 continued; submit on GAP (Lab 4 guide) |
Weekend · Friday 13 – Sunday 15 November¶
Finish Assignment 1, due Sunday 15 November.
Module 2¶
Day 4 · Monday 16 November¶
Reading: Chapter 5.
| Period | Time | Subject |
|---|---|---|
| P1 | 08:10–08:55 | Predictive regression: from a line of best fit to a business prediction (Predictive regression) |
| P2 | 09:05–09:50 | Judging a model: train and test, R², error measures and residuals (Predictive regression) |
| P3 | 10:05–10:50 | Multicollinearity, and text as a feature (Predictive regression; Text as a feature) |
| P4 | 10:55–11:40 | Assignment 2 briefing (UrbanTrend house prices with a text score) (Text as a feature, Part 10; Assignment 2 guide) |
Day 5 · Tuesday 17 November¶
Reading: Chapters 5 and 6.
| Period | Time | Subject |
|---|---|---|
| P1 | 08:10–08:55 | Reading a regression for a manager (Simulation under uncertainty, Part 1) |
| P2 | 09:05–09:50 | Spreadsheet modelling: inputs, calculations and outputs (Simulation under uncertainty, Parts 2–3) |
| P3 | 10:05–10:50 | What-If analysis with Data Tables (Simulation under uncertainty, Parts 4–5) |
| P4 | 10:55–11:40 | Monte Carlo simulation in a spreadsheet (Simulation under uncertainty, Parts 6–9) |
| P5 | 13:45–14:30 | Risk measures: ranges, percentiles and the chance of a loss (Simulation under uncertainty, Parts 10–13) |
| P6 | 14:40–15:25 | Lab 5 briefing (Simulation under uncertainty, Parts 14–15; Lab 5 guide) |
| P7 | 15:40–16:25 | Lab 5: regression (mpg) (Lab 5 guide) |
| P8 | 16:35–17:20 | Lab 5 continued; submit on GAP (Lab 5 guide) |
Day 6 · Wednesday 18 November¶
Reading: Chapters 6 and 7.
| Period | Time | Subject |
|---|---|---|
| P1 | 08:10–08:55 | Business intelligence strategy and architecture (BI strategy, forecasting and ethics, Parts 1–3) |
| P2 | 09:05–09:50 | Integrating a BI case: from data to a recommendation (BI strategy, forecasting and ethics, Parts 3–5) |
| P3 | 10:05–10:50 | Excel Forecast Sheet (BI strategy, forecasting and ethics, Parts 6–7) |
| P4 | 10:55–11:40 | Ethics and the societal context of analytics (BI strategy, forecasting and ethics, Parts 8–10) |
| P5 | 13:45–14:30 | Lab 6 and Lab 7 briefing (EcoDrive) (BI strategy, forecasting and ethics, Parts 11–15; Lab 6 guide; Lab 7 guide) |
| P6 | 14:40–15:25 | Assignment 3 briefing (SmartHome) (BI strategy, forecasting and ethics, Parts 16–17; Assignment 3 guide) |
| P7 | 15:40–16:25 | Lab 6: spreadsheet simulation (EcoDrive) (Lab 6 guide) |
| P8 | 16:35–17:20 | Lab 6 continued; submit on GAP (Lab 6 guide) |
| P9 | 18:30–19:15 | Lab 7: BI case (EcoDrive phase 2, 12 quarters) (Lab 7 guide) |
| P10 | 19:25–20:10 | Lab 7 continued; submit on GAP (Lab 7 guide) |
Day 7 · Thursday 19 November¶
Reading: Chapter 8.
| Period | Time | Subject |
|---|---|---|
| P1 | 08:10–08:55 | Course review: descriptive and visual analytics (Course review and integration, Parts 1–3) |
| P2 | 09:05–09:50 | Course review: predictive and prescriptive analytics (Course review and integration, Parts 3–9) |
| P3 | 10:05–10:50 | Final exam |
| P4 | 10:55–11:40 | Final exam |
| P5 | 13:45–14:30 | Capstone launch (EcoVate) (Course review and integration, Part 11; Capstone guide) |
| P6 | 14:40–15:25 | Capstone studio (Capstone guide) |
| P7 | 15:40–16:25 | Capstone studio; support for Assignments 2 and 3 (Capstone guide) |
| P8 | 16:35–17:20 | Capstone studio; support for Assignments 2 and 3 (Capstone guide) |
Post-class week¶
Finish Assignments 2 and 3 and the capstone. Reading: Chapter 8.
Deadlines¶
All deadlines are 23:59 China Standard Time on the date shown. Submit on GAP (Moodle).
| Work | Deadline |
|---|---|
| Activity 1 | Monday 9 November |
| Lab 1 | Tuesday 10 November |
| Lab 2 | Wednesday 11 November |
| Labs 3 and 4 | Thursday 12 November |
| Assignment 1 | Sunday 15 November |
| Lab 5 | Tuesday 17 November |
| Labs 6 and 7 | Wednesday 18 November |
| Assignments 2 and 3 | Sunday 22 November |
| Assignment 4 (capstone) | Sunday 29 November |