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. |
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| 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
| 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.