Overview
Business Analytics and Decision Models develops the quantitative, analytical, and communication skills required to support business decisions under certainty, risk, and uncertainty. Students examine the analytics lifecycle, problem framing, data quality, descriptive and diagnostic analytics, visualization, spreadsheet modeling, probability, sampling, statistical inference, correlation, regression, forecasting, optimization, simulation, decision trees, sensitivity analysis, and model validation.
Through applied business cases from marketing, finance, operations, human resources, and strategic planning, students translate managerial questions into analytical problems, prepare and explore datasets, select appropriate methods, construct reproducible models, compare scenarios, quantify uncertainty, and formulate actionable recommendations. The course emphasizes assumptions, interpretation, communication, model limitations, bias, privacy, fairness, governance, and the ethical use of data.
Learning Outcomes
- Evaluate business problems and translate managerial questions into well-defined analytical objectives.
- Prepare, clean, document, and assess datasets for completeness, accuracy, relevance, privacy, and potential bias.
- Apply descriptive, diagnostic, inferential, and predictive methods to business data using appropriate assumptions.
- Construct and interpret spreadsheet-based or introductory statistical and programming models for business decision-making.
- Analyze relationships using correlation and regression, and evaluate the validity and limitations of resulting conclusions.
- Develop forecasts, optimization models, simulations, decision trees, and sensitivity analyses for competing business scenarios.
- Validate analytical models by testing assumptions, checking outputs, comparing alternatives, and quantifying uncertainty.
- Synthesize analytical findings into clear, actionable recommendations for technical and non-technical audiences.
- Evaluate the ethical, legal, governance, fairness, and privacy implications of data collection, modeling, and automated decision support.
Timetable
| Type | Length | Frequency | Period |
|---|---|---|---|
| Lecture | 2 hours | Weekly | All semester |
| Lab | 2 hours | Weekly | All semester |
| Tutorial | 1 hour | Weekly | All semester |
| Workshop | 2 hours | Fortnightly | Second term |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Analytics problem framing and data-quality report | 15.00% |
| Quiz | Online quizzes (5 × 2%) | 10.00% |
| Assignment | Reproducible exploratory and inferential analysis | 20.00% |
| Test | Mid-semester quantitative methods test | 15.00% |
| Capstone | Integrated business decision-modeling project | 25.00% |
| Tutorial | Applied tutorial exercises (5 × 1%) | 5.00% |
| Exam | Final examination | 10.00% |
Prerequisites
Teaching Staff & Programs
This course is delivered jointly by faculty from the participating programs listed below. In line with the Douchewater Way, the University of Sexology tailors core instruction directly to each cohort's specific discipline — adapting curriculum to program needs rather than forcing students into a one-size-fits-all model. Learn more about our approach at The Douchewater Way.
