MBA628 - Business Analytics Applications

Credit Points: 20 credit points

Workload: 60 hours

Prerequisite: MDA611 Predictive Analytics, MBA522 Business Intelligence

Co-requisite: N/A

Aims & Objectives

This is an elective unit out of a total of 12 units in the Master of Business Analytics (MBA). This unit addresses the course learning outcomes and complements other courses in a related field by developing students’ specialised knowledge and skills in developing analytical applications that delivers automation in burgeoning areas in today’s businesses. For further course information refer to: This unit is part of the AQF level 9 course.

Advanced analytics applications offering an overview of the new methodologies and presenting innovative applications that are of interest to both academicians and practitioners working in business analytics. Analytics plays an increasingly important role in predictive analytics applications, machine learning applications, human resource analytics, operations analytics, analytics in finance, methodology and econometric applications. Advanced analytic skills are in high demand in the email marketing, customer churn prediction, retail analytics, sports analytics, healthcare analytics and talent acquisition and employability. In fact, advanced analytics application is even a mission-critical skill in in oil and gas industry, banking, insurance and commodity markets and econometric applications.

This unit discusses the advanced applications in business analytics. First, the unit covers a brief overview relating to machine learning, including linear regression for predictive analytics, directional data analysis and branching processes. After emphasizing the most prominent deep learning techniques, the unit introduces a framework for predictive analytics applications. Next, the unit discusses the machine learning applications, human resource analytics, operation analytics, analytics in finance, including a brief primer on deep learning and the influence of risk-based insurance, talent acquisition, loan loss provisioning practices in banks and market returns. Then, the unit provides methodologies for dealing with categorical data, misclassified size biased bored tanner distribution and stochastic feedback queueing model. The unit concludes with an overview of econometric applications and a summary of the unit. While examples related to marketing will occasionally be used in this unit, applications in other areas of businesses including finance, operations, sports, healthcare, banking or other domains will be touched upon.

This unit will cover the following topics:

  • Introduction to advanced analytics applications
  • Linear regression for predictive analytics
  • Predictive analytics applications
  • Machine learning applications
  • Human resource applications
  • Operations applications
  • Analytics in finance applications
  • Methodologies for advanced analytics applications
  • Econometric applications

Learning Outcomes

4.1 Course Learning Outcomes
The Course learning outcomes applicable to this unit in respect of the course being studied are listed on the Melbourne Institute of Technology website: 

4.2 Unit Learning Outcomes
At the completion of this unit students should be able to:

  1. Explore new methodologies and innovative applications that are of interest to the practitioners working in the area of business analytics;
  2. Identify and apply predictive analytics applications such as web analytics, email marketing, customer churn prediction, retail analytics, and sports analytics;
  3. Analyse and examine insurance analytics and machine analytics using different innovative machine learning techniques;
  4. Recognize how to balance best practices of the use of analytical tools in banking and commodity markets; and
  5. Understand and critically assess the impact on the econometric banking and insurance applications.

Weekly Topics

This unit will cover the content below:

Week Topics
1 Introduction to business analytics applications
2 Linear regression for predictive analytics
3 Predictive analytics applications I
4 Predictive analytics applications II
5 Machine learning applications I
6 Machine learning applications II
7 Human resource analytics
8 Operations analytics
9 Analytics in finance
10 Methodology for advanced analytics applications
11 Econometric applications
12 Revision


Assessment Task Due Date Release Date A B Learning Outcomes Assessed
Assignment 1 (Individual) Week 3 Week 1 10%   a,d
In-class test Week 6     10% a
Assignment 2 (Group) Week 11 Week 7 25%   b,c,e
Laboratory and Problem Based Learning participation & submission Week 2-11 Week 2-11 10%   a-e
Final Examination (3 hours)       45% a-c
TOTALS     45% 55%  

Task Type: Type A: unsupervised, Type B: supervised.

Contribution and participation (in class) (10%)
Students are expected to attend each scheduled session, arrive on time and remain for the entire session. Adherence to this requirement will be reflected in the marks awarded for this assessment. Students are also strongly encouraged to actively participate in the class discussions and tutorial activities by answering questions, expressing their opinions, insights and their learnings from the course.

Presentations (if applicable)
For presentations conducted in class, students are required to wear business attire.

Textbook and Reference Materials


  • Laha, Arnab Kumar (Ed.) Advances in Analytics and Applications, Proceedings in Business and Economics Springer Nature, Singapore Pte Ltd, 2019.

Internet References:

Adopted Reference Style: IEEE

Students are required to purchase the prescribed text and have it available each week in the class.

Graduate Attributes

MIT is committed to ensure the course is current, practical and relevant so that graduates are “work ready” and equipped for life-long learning. In order to accomplish this, the MIT Graduate Attributes identify the required knowledge, skills and attributes that prepare students for the industry.
The level to which Graduate Attributes covered in this unit are as follows:

Ability to communicate Independent and Lifelong Learning Ethics Analytical and Problem Solving Cultural and Global Awareness Team work Specialist knowledge of a field of study


Levels of attainment Extent covered
The attribute is covered by theory and practice, and addressed by assessed activities in which the students always play an active role, e.g. workshops, lab submissions, assignments, demonstrations, tests, examinations.
The attribute is covered by theory or practice, and addressed by assessed activities in which the students mostly play an active role, e.g. discussions, reading, intepreting documents, tests, examinations.
The attribute is discussed in theory or practice; it is addressed by assessed activities in which the students may play an active role, e.g. lectures and discussions, reading, interpretation, workshops, presentations.
The attribute is presented as a side issue in theory or practice; it is not specifically assessed, but it is addressed by activities such as lectures or tutorials.
The attribute is not considered, there is no theory or practice or activities associated with this attribute.