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:
City Campus
Remote
Study level:
Postgraduate
Credit points:
12
Unit code:
BCO6008

Prerequisites

BCO7000 - Business Analytics and Visualisation

Learning Outcomes

On successful completion of this unit, students will be able to:
  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

This unit is studied as part of the following courses. Refer to the course page for information on how to apply for the course.

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