BIT313 Data Visualisation

Unit outline

Important Update: Our aim is to provide you with an optimal learning experience, regardless of how this unit is delivered. Teaching will be delivered in line with the most current COVID Safe health guidelines.  This may include a mix of online and face-to-face.  Please check the learning management system for announcements and updates. Thank you for your flexibility and commitment to studying with Sydney Institute of Higher Education. 
Enrolment modes: Year 3, Semester 2
Credit point(s): 12.5
EFTSL value: 0.125
Prerequisite: BUS103 Database Statistics, BIT307 Data Mining
Typical study commitment: Students will on average spend 10 hours per week over the teaching period undertaking the teaching, learning and assessment activities for this unit.
Scheduled learning activities: 4 timetabled hours per week, 6 personal study hours per week
Other resource requirements: Students will need access to lab computers or will need their own laptops in order to carry out lab exercises and assignments. Students will need to use Microsoft Excel and Tableau.

Unit description 

This unit covers the core data visualisation concepts, techniques and technologies to support decision-making processes. Real world data are often difficult to understand and interpret. Data visualisation is the presentation of data in a graphical format to easily identify underlying patterns, hence enabling organisations to contexualise data and extract meaningful patterns and trends effectively. In this unit, students will learn how to extract patterns for effective decision making by visualising data.This unit provides students with essential information and skills in presenting and analysing data in graphical format. It also enables studnets to understand different challenges associated with the visualisation and interpretation of big data.

Unit Outline Outcomes (ULO)

  On the successful completion of this units student will be able to:  
ULO1 Demonstrate an understanding of visualisation principles and techniques.  
ULO2 Identify issues and challenges associated with the use of data visualisation techniques.  
ULO3 Evaluate various data visualisation solutions applicable to current and emerging applications.    
ULO4 Examine and employ a variety of data visualisation techniques.  
ULO5 Create visualisations of data using software tools based on real world datasets.  

Topics to be included  

1. Introduction to data visualisation
2. Mapping data onto aesthetics
3. Coordinate systems and axes
4. Colour scales
5. Directory of visualisations
6. Amounts and distributions
7. Proportions and associations
8. Time series and trends
9. Handling overlapping points
10. Common pitfalls of colour Use
11. Balancing the data and the context
12. Visualisation Software & Revision


Assessment Description Grading and weighting
(% total mark for unit)
Indicative due week 
Assessment 1: Class Participation 10% 12
Assessment 2: Technical Report and Presentation (Group) 30% 8
Assessment 3: Design Project (Individual) 30% 10
Assessment 4: Practical Project (Individual) 30% 12

The assessment due weeks provided may change. Your lecturer will clarify the exact assessment requirements, including the due date, at the start of the teaching period.