MDA611 - Predictive Analytics

Credit Points: 20 credit points

Workload: 60 hours

Prerequisite: MDA511 Mathematical and Statistical Methods

Co-requisite: N/A

Aims & Objectives

This is a core unit out of a total of 12 units in the Master of Data Analytics (MDA) and Master of Business Analytics (MBA). This unit addresses the course learning outcomes and complements other units in a related field by developing students’ specialised knowledge of predictive tools and technologies. For further course information refer to: and This unit is part of the AQF level 9 (MDA and MBA) courses.

Students will gain an insight of various statistical techniques such as data mining, predictive modelling and machine learning to analyse current and historical data to predict future trends, behaviour or activity. The pattern of the findings can be used to identify risks and opportunities for future. Students will explore the predictive analysis process expressed as” define project, data collection, data analysis, statistics, modelling, deployment, and model monitoring”. This unit also covers applications of predictive analytics. 

This unit will cover the following topics:

  • Predictive modeling process
  • Data wrangling and model tuning
  • Regression models: linear and non-linear
  • Classification models: Measuring performance, analysis, application
  • Compare and contrast various models

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. Develop knowledge of the predictive modelling process.
  2. Analyse requirements of complex data-driven problems and implement predictive analytics solutions using a modern programming language.
  3. Analyse and interpret data using an ethically responsible approach.
  4. Critically review the use of and assist in the selection of industry standard analytics tools.
  5. Appraise appropriate goals for a predictive analytics implementation in organisations.

Weekly Topics

This unit will cover the content below:

Week Topics
1 Introduction to prescriptive, descriptive and predictive models
2 Predictive modelling process
3 Data pre-processing (Data Exploration)
4 Information-based Learning (Decision Trees)
5 Similarity-based-Learning (Nearest Neighbours)
6 Probability-based Learning (Naive Bayes)
7 Error-based Learning (Regression Methods)
8 Deep Learning, Recurrent, Convolutional Neural Networks and applications
9 Data Dimensionality Reduction
10 Case Study 1
11 Case Study 2
12 Review and future trends


Assessment Task Due Date Release Date A B Learning Outcomes Assessed
Assignment 1 (Formative) Week 3 Week 1 5%   a
In-class test Week 6 Week 1   10% a-b
Assignment 2 Week 11 Week 7 25%   c-d
Laboratory and Problem Based Learning participation & submission Week 2-11 Week 2-11 10%   a-e
Final Examination (3 hours)       50% a-e
TOTALS     40% 60%  

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 

Textbook and Reference Materials


  • J. D. Kelleher, B. M. Namee, A. D’Arcy, Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies, The MIT Press, 2nd Ed., 2021.
  • B. Lantz. Machine learning with R: expert techniques for predictive modeling. Packt publishing ltd., 2019.


  • M. Kuhin, K. Johnson, Applied Predictive Modeling, Springer, USA, 1st Ed., 2016.
  • J. Babcock, Mastering Predictive Analytics with Python, Packt Publishing, UK, 2016
  • R. Klimberg, B. D. McCullough, Fundamentals of Predictive Analytics with JMP, 2nd Ed., SAS Institute, 2016


  • IEEE Transactions on Neural Networks and Learning Systems
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Foundations and Trends in Machine Learning
  • Journal of Machine Learning Research
  • Artificial Intelligence
  • International Journal of Machine Learning and Cybernetics
  • International Journal of Artificial Intelligence
  • Intelligent Data Analysis
  • Applied Artificial Intelligence
  • Journal of Experimental and Theoretical Artificial Intelligence
  • Journal of Artificial Intelligence

Adopted Reference Style: IEEE

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.