BDA213 - Data Analytics and Smart Applications

Credit Points: 15 credit points

Workload: 48 hours

Prerequisite: BDA112 Data Science Fundamentals

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: http://www.mit.edu.au/study-with-us/programs/Bachelor-DA. This unit is part of the AQF level 7 (BDA) course.

Students will be able to design, develop and evaluate data analytics applications in smart applications. They will gain knowledge of data analytics model and algorithms, as well as practical skills in applications of data analytics in industry level smart applications.

This unit will cover the following topics:

  • Introduction to smart applications
  • Cloud and IoT powered smart applications
  • Computational intelligence in smart applications
  • Big Data Management on Sensor Wireless network
  • Artificial Neural Network in Big Data Processing
  • Cognitive applications for Smart Cities
  • Smart Building application and Information System
  • Smart sensor network for building safety
  • The IOT and its applications
  • Human centric data visualisation and future trends
     

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: www.mit.edu.au 
4.2 Unit Learning Outcomes
At the completion of this unit students should be able to:
a. Describe use of data analytics models and algorithms in smart applications;
b. Assess suitable data analytics tools and applications to meet the business requirements of the selected smart application;
c. Use appropriate framework for testing usefulness of data analytic applications in a selected smart application;
d. Evaluate performance of data analytic solutions in smart applications in consideration of ethical standards in decision automation;
e. Communicate milestones and results to relevant stakeholders.
 

Weekly Topics

This unit will cover the following content:

Week Topics
1 Introduction to smart applications
2 Cloud computing infrastructure for data intensive applications
3 Manipulation of large data on cloud (e.g. compression of big data)
4 Data collection and management on wireless sensor network
5 Artificial neural network in large data processing
6 Cognitive applications in smart cities
7 Pattern and rule assessment
8 Smart building application and information management system
9 Smart sensor network for building control and safety
10 The IOT and its applications
11 Smart railway based on IOT
12 Future trends in smart applications

Assessment

Assessment Task Due Date A B Learning Outcomes Assessed
Formative Assignment 1 Part A
Assignment 1 Part B		 Week 3
Week 8		 5%
10%		   a-c
Assignment 2 Week 11 35%   c-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 (https://www.mit.edu.au/about-us/governance/institute-rules-policies-and-plans/policies-procedures-and-guidelines/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

Textbook:

  • Hui-Huang Hsu, Chuan-Yu Chang, Ching-Hsien HSU, “Big Data Analytics for Sensor-Network Collected Intelligence”, Fatos Shafa, 2016
  • Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Machine Learning Fundamental Concepts and Algorithms, Cambridge University Press, 2020.

References:

  • 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

Legend

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.