BDA321 - Smart Industry Automations

Credit Points: 15 credit points

Workload: 48 hours

Prerequisite: BDA213 Data Analytics in Smart Applications

Co-requisite: N/A

Aims & Objectives

This is an elective 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 smart applications to meet the latest industry demands . For further course information refer to: This unit is part of the AQF level 7 (BDA) course.

Smart industry and manufacturing are composed of fully integrated, collaborative manufacturing systems that incorporate data-driven technologies to respond to the changing demands and conditions within the production and supply environment. The smart industry covers sectors such as smart grids, smart buildings and smart cities, smart logistics and supply chains. The applications can also stem into smart mobility and traffic, smart workplaces and environments, and smart services.  The smart side of manufacturing directly deals with the role of data in industry and automation. The data preparation and processing technologies that revolutionized the industry to be smart include the Internet of Things (IoT), cloud computing, big data, autonomous robots, augmented reality, cybersecurity, realistic simulation, additive manufacturing and so on.

In this unit, students will learn to plan, design, and evaluate smart industry automation using data analytics tools and methodologies.  They will gain knowledge of tools and methodologies in data analytics solutions for smart industry automations.  Students will also gain practical experience of deploying and testing smart applications that are relevant to industry market demands.

This unit will cover the following topics:

  • Robotics and automations
  • Simulation and augmented reality
  • Cognitive computing devices
  • IoT: components, topology and design considerations
  • IoT strategy and implementation for system quality assurance and business continuity
  • IoT in real life applications
  • Future trends in IoT

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. Understand the use of data analytics tools and methodologies in smart industry automation applications;
b. Select and use appropriate data analytic solutions in smart industry automation to meet business requirements;
c. Deploy, test and evaluate data analytic solutions in smart industry automation with demonstrated understanding of its impact on the business landscape and ethical standards;
d. Evaluate the performance of smart industry automation with clear understanding of its business relevance;
e. Discuss smart industry automation in the context of industry standards and best practices.

Weekly Topics

This unit will cover the following content:

Week Topics
1 Smart manufacturing (Industry 4.0)
2 Control and automation of industry applications
3 Big data driven control applications
4 Big data analysis for pattern identification
5 Machine intelligence in industry applications
6 Real-time control and system reliability
7 Smart control of industry applications
8 Vertical and horizontal system integration
9 Cybersecurity and system protection
10 Robotics and automations
11 Simulation and virtual/augmented reality
12 Future trends in smart applications


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 25%   d-e
Laboratory participation & submission Week 2 - 11 10%   a-e
Final Examination (2 hours) End of trimester   50% a-e
TOTALS   50% 50%  

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


  • Uthayan Elangovan, Smart Automation to Smart Manufacturing Industrial Internet of Things, Momentum Press (May 7, 2019)


  • Bengio, Yoshua, Ian Goodfellow, and Aaron Courville, Deep learning, MIT press, 2016.
  • Brownlee, Jason, Deep learning with python: Develop deep learning models on theano and tensorflow using keras, Machine Learning Mastery, 2016.
  • Di, Wei, Anurag Bhardwaj, and Jianing Wei, Deep Learning Essentials: Your hands-on guide to the fundamentals of deep learning and neural network modelling, Packt Publishing, 2018.
  • Ghatak, Abhijit, Deep Learning with R, Springer, Singapore, 2019
  • Beysolow II, Taweh, Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R, Apress, 2017. 
  • Charniak, Eugene, Introduction to deep learning, The MIT Press, 2019. 
  • Michelucci, Umberto, Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks, Apress, 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.