BAN222 - Visual & Predictive Analytics

Credit Points: 15 credit points

Workload: 36 hours

Prerequisite: BAN211 Business Analytics

Co-requisite: N/A

Aims & Objectives

This is a second-year Core Unit in the Bachelor of Business, Major in Visual & Predictive Analytics, and offered as a recommended Elective Unit from another specialisation stream in the Bachelor of Business, subject to meeting pre- and co-requisites. For Course Learning Outcomes and further information relating to Bachelor of Business programs, please visit our website: http://www.mit.edu.au/study-with-us/programs/bachelor-business.  

Businesses and governments now have access to massive volumes of data and require skills and expertise in making sense of this information for strategic decision-making. This unit provides all the fundamental visual analytics knowledge and makes choices of preferred methods of presenting data to appeal to visual perceptions and systems. The unit will also focus on representing data that appeals to the human visual-perceptual system. This includes utilising visual analytics tools, such as Tableau or R, to explore and analyse complex data sets using multivariate, text-based, geospatial, network, and graph-based data. Students will be given the opportunity to gain knowledge and skills with the most widely used predictive analytics model and use data mining tools in real business scenarios. 

Unit topics include: 

  • Visual Analytics and the Power of Visual Data Stories 
  • Fundamental Data Visualizations   
  • Fundamental Maps 
  • Structuring Analytics for Storytelling 
  • The Roles of Data and Predictive Analytics in Business 
  • Information Extraction: Basic and Advanced Time Series Methods 
  • Information Extraction: Non-Time Series Methods 
  • Introduction to Simulations, Designing and Analysing a Simulation 

Learning Outcomes

  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    

  1. Unit learning outcomes  

  • Apply methods to present complex data relationships and secure data privacy in visualisations. 
  • Apply state-of-the-art statistical packages for managing and presenting data. 
  • Understand and use predictive models to solve forecasting queries. 
  • Develop strategies from processed data using state-of-the-art statistical packages. 
  • Effectively communicate inferences and findings to wide and disparate audiences in an ethical manner. 

Assessment

Assessment Task Due Date A B Learning Outcomes Assessed 
1. Assessment 1: (Individual) – Opinion paper  Week 3 - 10%  a
2. Assessment 2: (Individual) – Recorded Visual Presentation (15 minutes)   Week 8  30%  - a-d 
3. Assessment 3: Contribution and Participation  Week 12  - 10% a-e 
4. Assessment 4: (Group of 2-3 students) – Project Report (3,000 words) (30%) and Video Presentation (20%) Week 12  30%  20% a-e 
TOTALS   60%  40%   

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

Contribution and Participation (10%)

This unit has class participation 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 on the type of assessment tasks and the marking rubrics will be provided in the assessment specification. 

Textbook and Reference Materials

Note: Students are required to purchase the prescribed textbook and have it available each week in class. 

Prescribed Text Book

  • Ryan, L. (2023), Visual Analytics Fundamentals: Creating Compelling Data Narratives with Tableau 1st edition, Addison-Wesley Professional (June 15th 2023). 

Recommended Texts

  • Abdey, J. (2023), Business Analytics:  Applied Modelling and Prediction, SAGE Publishing, 2023. 
  • Paczkowski, R. (2023), Predictive and Simulation Analytics: Deeper Insights for Better Business Decisions, Springer Nature Switzerland AG, ISBN 978-3-031-31886-3 ISBN 978-3-031-31887-0 (eBook), https://link.springer.com/book/10.1007/978-3-031-31887-0#toc 
  • Raviv, G. (2019), Collect, combine, and transform data using Power Query in Excel and Power BI, Pearson Education, 2019. 

The Referencing style for this unit is APA:  

See the MIT Library Referencing webpage: https://library.mit.edu.au/referencing/APA and the Unit Moodle page for additional referencing support material and web links. 

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.