36119 Advanced Topics in Data Science
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Credit points: 4 cp
Subject level:
Postgraduate
Result type: Grade, no marksThere are course requisites for this subject. See access conditions.
Description
This subject delves into the latest developments in academic research and industry practices across different subfields of data science. It aims to extend students’ knowledge and skills in applying data analytics techniques and developing ethical and responsible data solutions to complex problems that benefit stakeholders.
Throughout the learning process, students conduct specialised studies with guided reading and research to further enhance their abilities to translate business problems into research questions, conduct thorough investigations and evaluate the possible implications of their findings. By the end of the subject, students have a comprehensive understanding of latest trends in the chosen topics of the rapidly evolving field of data science.
The topics may vary from year to year depending on student interest and staff availability. The topics covered can be either theoretical or applied aspects of data science.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Identify key problems and formulate research questions properly (CILO 1.1) |
|---|---|
| 2. | Determine and apply advanced skills and methodologies appropriate for the chosen topics in independent project work and research.(CILO 1.2, 1.4) |
| 3. | Explain, discuss and communicate project findings in reporting (CILO 2.2) |
Course intended learning outcomes (CILOs)
This subject contributes specifically to the development of the following course intended learning outcomes:
- Identify and represent the human and technical elements and processes within complex systems and organise them within frameworks of relationships (1.1)
- Explore and test models and generalisations for describing the behaviour of sociotechnical systems and selecting data sources, taking into account the needs and values of different contexts and stakeholders (1.2)
- Use transdisciplinary approaches to seeing and doing to uncover underrepresented, or misrepresented, elements of a system (1.4)
- Explore, analyse, manipulate, interpret and visualise data using data science techniques, software and technologies to make sense of data rich environments (2.2)
Teaching and learning strategies
Blend of online and face to face activities: The subject is offered through a series of teaching sessions which blend online and face-to-face learning. Students learn through interactive lectures and classroom activities making use of the subject materials on canvas. They also engage in individual and collaborative learning activities to understand and apply text analysis techniques in diverse settings.
Authentic problem based learning: This subject offers a range of authentic data science problems to solve that will help develop students’ text analysis skills. They work on real world data analysis problems for broad areas of interest using unstructured data and contemporary techniques.
Transdisciplinary approaches: Starting from an elemental perspective on data and data science, students will approach learning from their specific professional and potential future contexts. As the subject progresses conceptual and philosophical approaches to the association between data and innovation, provocative questions will stimulate analytical engagement across a range of perspectives. Case studies and insights from industry experts will provide 'lived experiences' to accelerate and consolidate student learning.
Content (topics)
1. General research and literature research methods
2. Readings of chosen topics
3. Guest lectures from invited academics and industry professionals
Assessment
Assessment task 1: Literature review
| Objective(s): | This assessment task contributes to the development of course intended learning outcome(s): 1.1 and 2.2 |
|---|---|
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 35% |
Assessment task 2: Project report
| Objective(s): | This assessment task contributes to the development of course intended learning outcome(s): 1.1, 1.2, 1.4 and 2.2 |
|---|---|
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 65% |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.