DATA 300: Data Driven Decision Making¶
DATA 300 for the China classroom: a pre-class online week, seven intensive teaching days on campus, and a post-class online week, covering descriptive, predictive and prescriptive analytics for better business decisions.
Course materials
All pre-class and teaching-day pages are ready. The course starts on Tuesday 10 November 2026.
What the course covers¶
The course moves from describing what happened, to predicting what will happen, to deciding what to do. You will use Python in Jupyter, Tableau and Excel.
| Theme | You will learn to |
|---|---|
| Descriptive analytics | Explore and clean data, summarise it, and find patterns |
| Communicating results | Build clear charts and dashboards that support a decision |
| Text mining | Turn customer reviews into measurable sentiment and themes |
| Forecasting | Project a time series forward and say how sure you are |
| Predictive modelling | Build and judge a linear regression model |
| Simulation | Model uncertainty in a spreadsheet and compare decisions |
| Business intelligence | Combine everything into a managerial recommendation, with ethics in mind |
Course learning outcomes¶
By the end of the course you will be able to:
- CLO 1: Describe the principles of descriptive, predictive and prescriptive analytics to solve business data challenges.
- CLO 2: Design and use data visualisation and modelling tools to communicate user-centred analytics for strategic decisions.
- CLO 3: Apply computational problem-solving to perform data and text mining, discovering relationships in structured and unstructured data.
- CLO 4: Develop predictive models using time series forecasting, linear regression and spreadsheet simulation to project future outcomes.
- CLO 5: Evaluate the impact of business intelligence tools within a comprehensive case study to generate managerial reports that consider ethical and societal contexts.
How the course is organised¶
| Module | When | Where |
|---|---|---|
| Pre-class | The week before 10 November | Online: setup, Python basics, Activity 1 |
| In-class Module 1 | Tuesday 10 – Thursday 12 November | On campus |
| In-class Module 2 | Monday 16 – Thursday 19 November | On campus |
| Post-class | The week after 19 November | Online: capstone and remaining assignments |
Graded work¶
| Module | Item | Weight |
|---|---|---|
| Pre-class | Activity 1: Self-assessment and readiness check (JupyterLab and Tableau) | 4% |
| Module 1 · Day 1 | Lab 1: Data exploration | 3% |
| Module 1 · Day 2 | Lab 2: Dashboard creation | 3% |
| Module 1 · Day 3 | Assignment 1: Data visualisation and descriptive analytics report | 6% |
| Module 1 · Day 3 | Lab 3: Text mining application | 3% |
| Module 1 · Day 3 | Midterm exam | 15% |
| Module 1 · Day 3 | Lab 4: Forecasting | 3% |
| Module 2 · Day 1 | Assignment 2: Predictive data mining report | 6% |
| Module 2 · Day 1 | Lab 5: Predictive linear regression modelling | 2% |
| Module 2 · Day 2 | Lab 6: Advanced spreadsheet simulation | 2% |
| Module 2 · Day 3 | Assignment 3: Spreadsheet simulation and decision model | 6% |
| Module 2 · Day 3 | Lab 7: BI case study implementation | 2% |
| Module 2 · Day 4 | Final exam | 20% |
| Post-class | Assignment 4: Final comprehensive assignment (capstone) | 25% |
| Total | 100% |
See the schedule for when each item happens, and setup for what to install before the course starts.
Main textbook¶
Gordon, M. E. (2023). Business analytics: Combining data, analysis and judgement to inform decisions. SAGE Publications.