36126 Innovation Lab: Research Project
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Credit points: 8 cp
Subject level:
Postgraduate
Result type: Grade, no marksRequisite(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:
| 1. | Review the literature and critically evaluating the state-of-the-art of selected Data Science research topics. |
|---|---|
| 2. | Conduct independent research into a given topic in Data Science. |
| 3. | Prepare a research report summarizing the aims, background, methods, results and conclusions of independent research into a given topic in Data Science. |
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, analyse, manipulate, interpret and visualise data using data science techniques, software and technologies to make sense of data rich environments (2.2)
- Collaborate to develop and refine multimodal communication skills needed to successfully work in data science teams (4.1)
- Engage in active, reflective practice that supports flexible navigation of assumptions, alternatives and uncertainty in professional data science contexts (5.1)
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 academic experts will provide 'lived experiences' to accelerate and consolidate student learning.
Assessment
Assessment task 1: Research progress
| Objective(s): | This task addresses the following subject learning objectives: 1, 2 and 3 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 2.2, 4.1 and 5.1 |
|---|---|
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 20% |
Assessment task 2: Research report
| Objective(s): | This task addresses the following subject learning objectives: 1, 2 and 3 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 2.2, 4.1 and 5.1 |
|---|---|
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 80% |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.