The unit is focused on using big data to identify future outcomes and best-assessment of future actions using predictive analytics solutions. Predictive analytics creates a critical competitive advantage for current businesses using data as their most valuable business asset. Using open source environments (R/Python), collaborative development practices and version control (Git/Github) students are exposed to algorithms and machine learning techniques to capture important trends, predict anomalies and forecast business outcomes. The unit is based upon using real-world scenarios and business data. It equips students with solid theoretical foundation of business analytics and builds practical skills. The unit exposes students to industry-based predictive analytics data from Kaggle.
Unit details
Location:
Study level:
Postgraduate
Credit points:
12
Unit code:
BCO6008
Prerequisites
BCO7000 - Business Analytics and Visualisation
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Learning Outcomes
| 1. | Critically analyse the use of predictive analytics approaches in decision making for business organisations to deconstruct complex business problems; | ||
| 2. | Design reproducible predictive analytics solutions using open source environments (R/Python) and implement collaborative development practices and version control (Git/Github); | ||
| 3. | Evaluate the selection, implementation and business use of predictive analytics solutions using open source environments (R/Python) in regards to statistical rigour and addressing business goals; and, | ||
| 4. | Advocate the devised predictive analytics solutions to stakeholders and interpret outcomes to inter-disciplinary audiences. |
Assessment
| Assessment type | Description | Grade |
|---|---|---|
| Test | Practical exercises (5%,10%, 10%) | 25% |
| Assignment | Data analytics project: a Kaggle style data analysis (data pre-processing, visualisation and model building) | 35% |
| Assignment | Group: working with a real-world data set to develop and evaluate a data model - research report (25%) presentation (15%) | 40% |
Where to next?
As part of a course
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