BDA311 - Big Data Applications

Credit Points: 15 credit points

Workload: 48 hours

Prerequisite: BDA213 Data Analytics and Smart Applications

Co-requisite: N/A

Aims & Objectives

This is a core unit out of a total of 24 units in the Bachelor of Data Analytics (BDA). This unit addresses the BDA course learning outcomes and complements other courses in related fields by developing students’ specialised knowledge of data analytics in Information Technology. For further course information refer to: This unit is part of the AQF level 7 (BDA) course.

Students will learn how to plan, design, develop and troubleshoot big data analytic applications. They will gain knowledge of big data storage and management systems as well as practical skills in model construction and testing for big data analytic applications.

This unit will cover the following topics:

  • Big data models in smart applications
  • Big data storage and management systems
  • Big data analytics on the cloud
  • Big data analytics and applications
  • System reliabilities in big data applications

Learning Outcomes

4.1 Course Learning Outcomes
The Course learning outcomes applicable to this unit are listed on the Melbourne Institute of Technology’s website: 

4.2 Unit Learning Outcomes
At the completion of this unit students should be able to:
a. Describe the use of big data in industry applications;
b. Evaluate suitable data storage, management, and processing solutions in big data applications;
c. Assemble big data systems in a selected industry application in consideration of ethical standards and cultural awareness regarding data sources and management;
d. Evaluate and review the performance of big data models in a selected industry application; 
e. Recommend appropriate big data solutions for businesses.

Weekly Topics

This unit will cover the following content:

Week Topics
1 Understanding big data
2 Motivations for big data applications – and cultural awareness of data sources and data manipulations
3 Big data architecture framework
4 Big data storage concepts and technologies (e.g. Hadoop)
5 Big data processing concepts and technologies (e.g MapReduce)
6 Big data analytics
7 Online and incremental learning
8 Big data visualisation
9 Big data for financial analytics
10 Big data for IoT applications
11 Big data ethics, security and system reliability
12 Future trends in big data analytics


Assessment Task Due Date A B Learning Outcomes Assessed
Formative Assignment 1 Part A
Assignment 1 Part B
Week 3
Week 8
Assignment 2 Week 11 35%   d-e
Laboratory participation & submission Week 2 - 11 10%   a-e
Final Examination (2 hours) End of trimester   40% a-e
TOTALS   60% 40%  

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

Class Participation and Contribution
This unit has class participation and student contribution as an assessment. The assessment task and marking rubric will follow the Guidelines on Assessing Class Participation ( Further details will be provided in the assessment specification on the type of assessment tasks and the marking rubrics.

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

Textbook and Reference Materials


  • K. Krishnan, Building Big Data Applications, Academic Press, 2020.


  • Erl, W. Khattak, P. Buhler, Big Data Fundamentals: Concepts, Drivers & Techniques, Prentice Hall Service Technology Series from Thomas Erl, 2016.
  • C. Coronel, S. Morris, Database Systems, Design, Implementation, and Management, Cengage, 2016
  • N. Dasgupta, Practical Big Data Analytics, Packt Publishing, 2018
  • Maheshwari, Data Analytics Made Accessible, Packt Publishing, 2018

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.