MBA512 - Business Data Analysis

Credit Points: 20 credit points

Workload: 60 hours

Prerequisite: N/A

Co-requisite: N/A

Aims & Objectives

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

Students will gain skills and knowledge on data analysis process and analyse both structured and unstructured data. Students will explore data processing methods and techniques with real-world business case studies.

This unit will cover the following topics:

  • Business Data
  • Data analytical tools
  • Data sources and models
  • Data patterns and trends
  • Data wrangling
  • User-centric design
  • Data clustering

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. Acquire and represent structured and unstructured business data.
  2. Apply principles of data ethics and privacy in the design of business data model and their debugging schemes.
  3. Analyse, transform and propose business visualisation solutions on structured and unstructured data.
  4. Model and evaluate business decision data models using data analytics tools.
  5. Analyse analytical application tools to evaluate models, in consideration of human factors.

Weekly Topics

This unit will cover the content below:

Week Topics
1 Foundation of Business Data – Skills Roles, and Applications
2 Principles of Data Ethics, Privacy and Protection
3 Data acquisition and data representation
4 Structured and Unstructured Data
5 Data Wrangling
6 Data and Cybersecurity for Business Understanding
7 Decision models
8 Data Clustering
9 Data Visualisation Schemes
10 Real Time Data Analytics Schemes
11 Analytics solutions for business case studies
12 Future Trends in Data Science, Review


Assessment Task Due Date Release Date A B Learning Outcomes Assessed
Assignment 1 Week 3 Week 1 5%   a
In-class test Week 6 Week 6   10% a-b
Assignment 2 Week 11 Week 7 30%   b-d
Laboratory and Problem Based Learning participation & submission Week 2-11 Week 2-11 10%   a-e
Final Examination (3 hours)       45% a-e
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


  • M. Taddy, Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions, SAGE McGraw-Hill Education; 1 edition, 2019
  • F J. S. Saltz and J. M. Stanton, An Introduction to Data Science, SAGE Publications, Inc., Melbourne, AU, 2017


  • S. Cooper, Data Science from Scratch: The #1 Data Science Guide for Everything a Data Scientist Needs to Know; Python, Linear Algebra, Statistics, Coding, Applications, Neural Networks, and Decision Trees, Createspace Independent Pub, 2018
  • M. Taddy, Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions, McGraw-Hill Education; 1st ed., New York, USA, 2019
  • V. K. B. Deshpande, Data Science: Concepts and Practice, 2nd ed., Morgan Kaufmann, MA, USA, 2018
  • W. Mackinney, Python for Data Analysis, O'Reilly Media, Inc, USA; 2018
  • Cady, The Data Science Handbook, John Wiley & Sons, 2017.
  • J. D. Kelleher and B. Tierney, Data Science, The MIT Press Essential Knowledge series, 2018
  • Y. Liu, Python Machine Learning By Example: The easiest way to get into machine learning, Packt Publishing, UK, 2017

Internet references:


  • 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.