The unit is concentrated on big data oriented learning and practices. In order for organisations to gain a competitive advantage, industry practitioners are required to be able to predict anomalies, disorders or indicators from their business categorical and numerical data that could lead to capture appropriate insights for their effective decision making.
Emerging technologies to various successful analytics systems implementation are continuously evolving to make effective predictions for quick decision support. This unit provides students with the knowledge and skills to utilise predictive analytics and data mining processes and technologies to gain greater insights into various business scenarios. Students will gain an overview of foundational knowledge on various applications of predictive analytics tools and techniques for enabling action-able decision solutions supported by industry case studies and hands-on exercises. Students will learn how data mining and predictive analytics can facilitate business intelligence and build analytical capabilities in organisations in the 21st century.

Unit details

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. Contextualise the art and science of predictive analytics to define clear actions that result in improved decisions and business results for contemporary organisations and dynamic communities locally and globally;  
  2. Validate the selection, preparation, construction, integration, structure, and formatting of data as effective to ensure predictive models meet the business goals;  
  3. Appraise appropriate goals for a predictive analytics implementation in organisations and authenticate recommendations with reference to a specified organisation's strategic priorities and mission and values statements and anticipated changing environment;  
  4. Critically review the use and assist in the selection of industry standard analytics tools and investigate the application of sources of information including social media data, unstructured text and/or big data sets to provide greater insight to business; and;  
  5. Interpret conclusions to inter-disciplinary audiences demonstrating a high level of personal autonomy and accountability in achieving group outcomes.  

Assessment

Assessment type Description Grade
Test In-class test - Problem solving and short answers 25%
Assignment Case studies and predictive modelling 35%
Report Research Report (Group) 40%

Where to next?

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