MIT8203 Fundamentals of Data Science
Unit Code
MIT8203
Level
Postgraduate
Campus
Sydney
Prerequisites
MIT8103
Credit Points
10
Subject Description
MIT8203 Fundamentals of Data Science introduces students to the concepts of data analysis, statistics, machine learning (ML), deep learning (DL), and visual analytics to uncover meaningful insights from complex datasets. Students will learn to apply various techniques for collecting, cleaning, and analysing data, detecting bias, utilising statistical methods, ML, and DL algorithms to extract valuable patterns and trends. The subject also provides insights into effective data visualisation techniques to communicate findings. Through hands-on projects, students will gain practical experience leveraging data to make informed decisions and drive impactful outcomes across diverse industries.
Subject Type
☒ Core
Study Mode: Face to face on site
Study Load: Full-time
Learning Topics
- Data science processes
- Data and big-data management
- Statistical methods
- Machine learning and deep learning
- Data visualisation, dashboards and storytelling
Subject Learning Outcomes
- Integrate the fundamentals of data collection, cleaning, and pre-processing so as to work with raw datasets effectively.
- Critically compare and apply statistics, machine learning and deep learning algorithms, including classification, and regression and analyse their suitability for different datasets.
- Build an interactive and dynamic visualisation system to explore and communicate complex datasets.
- Generate clear, informative and summative visual data representations applying data visualisation techniques using standard industry tools.
- Demonstrate the ability to collaborate, communicate, and critically think, applying data science and visualisation techniques to address complex problems across various domains and industries.
Assessment
AIIHE uses a variety of assessment tools to guide and assess each student’s achievement of the subject learning outcomes. In this subject, students undertake portfolios it involves six portfolios showcasing students’ understanding, skills, critical thinking, and self-reflection experiences in various problems, in-class test (individual) data visualisation, and individual report & presentation. These tasks assess the knowledge, applied skills, critical analysis, communication and collaborative capabilities relevant to the subject.
Summary of Assessment
- Students must attempt all assessment tasks, and achieve an overall mark of at least 50%, to pass this subject.
- Assessment in this subject is consistent with and informed by the AIIHE Assessment Policy and Procedure.
| Assessment Type | Due Date | Weighting |
|---|---|---|
| Assessment 1: Portfolios | Weeks 2, 4, 6, 8, 11, 12 | 30% |
| Assessment 2: In-class Test (Individual) Data Visualisation | Week 10 | 25% |
| Assessment 3: Individual Report & Presentation | Week 12, 13 | 45% |
Learning Resources
AIIHE will update the topic coverage of the subject and the available relevant learning resources at the time of subject delivery. In the interim, AIIHE provides the following indicative list of relevant reference material. AIIHE also subscribes to eLearning Resources through the Canvas portal, and where relevant material is available, AIIHE will seek to source reference material from this system for staff and student access
Prescribed Reading
- So, A., Joseph, T., John, R., Worsley, A., Asare, S., 2020, The Data Science Workshop: A New, Interactive Approach to Learning Data Science. Packt Publishing, Limited.
- James, G., Witten, D., Hastie, T., Tibshirani, R., Taylor, J., 2023, An Introduction to Statistical Learning, Springer. Available from https://www.statlearning.com/ [Accessed 23rd June 2024].
- Wilke, C.O., 2019, Fundamentals of data visualization: a primer on making informative and compelling figures, O'Reilly Media.