36127 Innovation Lab: Capstone Project
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Credit points: 8 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 innovation lab (iLab) - capstone subject focuses students towards designing and investigating a data-driven prototype in using contemporary techniques and multi-structure data sets. This subject is unique compared to those in other data science courses in that it allows students to work on real world problems using experimental approaches from conceptualisation, design to data search and analytics to proposing solutions.This simulates a ‘virtual’ work environment type scenario making it highly applicable in a real working environment whilst still allowing both disciplinary and transdisciplinary approaches to be pushed to its boundaries towards innovative ideas. Student teams work on diverse data-driven solutions for complex real-world challenges. Using a range of open data sets and suitable case studies, students hone their skills in framing questions useful for embarking upon discoveries, addressing gaps in knowledge or tackling problems in the lab environment, suitable for adaption in a work context. Students communicate the outcomes of their innovative lab investigations to relevant audiences.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Research and apply critically and creatively the knowledge and skills gained to complex challenges by engaging with a variety of data and different analytic techniques, building upon transdisciplinary skills to be an ‘agent of change’ in the data science profession. |
|---|---|
| 2. | Investigate, analyze and evaluate open data sources ethically using problem finding and problem-solving techniques, including human centered approaches to enrich data sense making, inform trends and propose new ideas, products or services. |
| 3. | Translate business issues into a data science project, interpret and present the project results in context meaningfully for effective communication with all stakeholders. |
| 4. | Contribute to effective team dynamics by behaving and managing real-life project tasks professionally. |
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)
- Understand and deal critically and openly with the uncertainty, ambiguity and complexity associated with people, systems and data (2.3)
- Explore, interrogate, generate, apply, test and evaluate problem-solving strategies to extract economic, business, social, strategic or other value from data (3.1)
- Collaborate to develop and refine multimodal communication skills needed to successfully work in data science teams (4.1)
- Explore and craft interpretative narratives that engage key audiences with data analytics and potential significance for action, at a societal, industrial, organisational, group or individual levels (4.2)
- Develop, test, justify and deliver data project propositions, methodologies, analytics outcomes and recommendations for informing decision-making, both to specialist and non-specialist audiences (4.3)
- Interrogate and justify ethical responsibilities related to data selection, access, analysis and governance to create a framework for practice (5.2)
Assessment
Assessment task 1: Progress and review
| Objective(s): | This task addresses the following subject learning objectives: 1, 2 and 4 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 1.2, 2.3, 3.1, 4.1 and 5.2 |
|---|---|
| Type: | Portfolio |
| Groupwork: | Group, individually assessed |
| Weight: | 30% |
Assessment task 2: Project outcomes
| Objective(s): | This task addresses the following subject learning objectives: 1, 2, 3 and 4 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 1.2, 2.3, 3.1, 4.1, 4.2, 4.3 and 5.2 |
|---|---|
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 40% |
Assessment task 3: Presentation and evaluation
| Objective(s): | This task addresses the following subject learning objectives: 1, 3 and 4 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 1.2, 4.1, 4.2, 4.3 and 5.2 |
|---|---|
| Type: | Presentation |
| Groupwork: | Group, individually assessed |
| Weight: | 30% |