36102 iLab: Research Project
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Credit points: 12 cp
Result type: Grade, no marks
Requisite(s): 36100 Data Science for Innovation AND 36103 Statistical Thinking for Data Science AND 36106 Machine Learning Algorithms and Applications
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Description
This subject gives students an opportunity to conduct a self-guided research investigation relevant to data science to develop an understanding of research process and prepare a research paper having potential to be published in peer-reviewed conferences or journals. Students must have an academic supervisor and an agreed research topic approved by the subject coordinator at the beginning of the semester.
Students are required to read relevant papers and the literature, come up with a research problem and develop research questions, and then work with the supervisor to refine the questions, design research and data collection methods to answer the questions, conduct the research, analyse the data and write a report.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| SLO1. | Review the literature and critically evaluating the state-of-the-art of selected Data Science research topics |
|---|---|
| SLO2. | Conduct independent research into a given topic in Data Science |
| SLO3. | Prepare a research report summarizing the aims, background, methods, results and conclusions of independent research into a given topic in Data Science |
Contribution to the development of graduate attributes
The subject addresses the following graduate attributes (GA):
GA 1 Sociotechnical systems thinking
GA 2 Creative, analytical and rigorous sense making
GA 3 Create value in problem solving and inquiry
GA 4 Persuasive and robust communication
GA 5 Ethical citizenship and leadership
Teaching and learning strategies
Authentic problem based learning: This subject offers a range of authentic data
science problems to solve that will help develop students’ 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
Assessment
Assessment task 1: Research progress
| Type: | Report |
|---|---|
| Groupwork: | Individual |
| Weight: | 20% |
Assessment task 2: Research report
| Type: | Report |
|---|---|
| Groupwork: | Individual |
| Weight: | 80% |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.