420048 Innovation Studio
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Credit points: 12 cp
Subject level:
Postgraduate
Result type: Grade and marksRequisite(s): (230708 Foundation Studio AND 240753 Customer Analytics AND 320146 Data Visualisation and Visual Analytics AND (430031 Python Programming for Data Processing OR 420047 Data Processing Using Python))
There are course requisites for this subject. See access conditions.
Description
The Innovation Studio focuses on a self-directed industry project approached independently by multi-disciplinary teams. Students identify business problems and develop innovative data analytics-based solutions that meet stakeholder needs. They critically evaluate and reflect on their process, and effectively communicate innovative outcomes.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Clearly define an industry problem with appropriate set of objectives. |
|---|---|
| 2. | Assess, adapt, and recommend the most suitable methodology to guide the industry project and plan to achieve defined objectives. |
| 3. | Synthesize a range of tools and techniques in order to design and develop creative and innovative solution to the identified industry problem. |
| 4. | Construct written, spoken, and visual communication with accuracy and clarity to effectively communicate innovative outcomes. |
| 5. | Critically evaluate, peer-review, reflect and communicate the learning process. |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Evaluate the assumptions implicit in data and apply analytical techniques to create innovative business solutions (1.1)
- Collaborate with colleagues and a range of stakeholders to deliver negotiated business outcomes (2.2)
- Critically evaluate data practices and use these insights to improve analytical processes which drive positive and equitable outcomes (3.1)
- Integrate advanced data analysis techniques with business practices to deliver new insights that drive effective decision-making in local and international contexts (4.1)
Teaching and learning strategies
In this online subject, all students will conduct data analytics projects through teamwork. Each team will cover team responsibilities from three categories, including prominent responsibilities (e.g., methodology and project management), supportive responsibilities (e.g., code review and testing), and additional responsibilities (e.g., marketing). The allocation of these responsibilities needs to match the knowledge background of team members, such as IT, financial analytics, banking analytics, government analytics, risk analytics, and service analytics.
This subject offers a series of weekly online-learning topics and 1-hour workshops to help students self-develop their skillsets in facilitating data analytics/machine learning technologies to produce solutions for enterprise problems and innovations. The weekly online-learning topics will be hosted on Canvas for student, with concepts, methodologies, techniques, and examples for enterprise projects. The workshops will include guest seminars (including industry experience, specific strategies for technical developments and project management, etc.) and consultation (e.g., feedback, suggestions, and Q/A). Students are expected to be active in the workshops to enhance their skillsets in developing analytical projects.
Each team will offline define a specific analytical problem as a use case, and collaboratively develop an analytical project plan and design, and an analytical project report. Each member will report their individual performance and peer review other team members. Team members are requested to post their contributions to the project on the Group Discussion Forum, which will be used as the evidence for their individual performance and peer review reports.
Content (topics)
- Introduction to data analytics projects
- Overview of data analytics/machine learning techniques
- Analytics system architecture and method selection
- Analytical solution development
- Evaluation and deployment of actionable analytical results
- Communication of analytical results to stakeholders
Assessment
Assessment task 1: Team Progress Report
| Intent: | This assessment is to provide opportunities for students to communicate with team members and coordinate individual progresses in a team basis, reflecting the team’s learning experience, project development, teamwork, and individual contributions. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): 2.2 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 10% |
| Length: | >100 words for each report |
Assessment task 2: Product 1: Analytics project plan and design
| Intent: | This assessment is to train students to integrate domain-specific knowledge and business problems, and related data and resources with analytics/machine learning methods and processes to design desirable solutions. Through this task, students will develop skills of critical thinking and analysis of appropriate state-of-the-art analytics technologies to address challenging real-world business problems and objectives. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): .2, 1.1, 2.2 and 4.1 |
| Type: | Report |
| Groupwork: | Group, group and individually assessed |
| Weight: | 30% |
| Length: | Part 1: 1,000-3000 words, excluding references |
Assessment task 3: Product 2: Project analytical report
| Intent: | This assessment is to train students with enterprise all-round and hands-on thinking, ability to effectively synergize cross-disciplinary knowledge, processes, skills, and experience to implement a project plan and design (Product 1) into analytical solutions to address enterprise challenges and innovations. Students are expected to make sense of enterprise data, evaluate results, improve the analytical design and strategies, deploy actionable analytical results, and communicate with stakeholders. Students will learn to develop industry-standard analytical systems, solutions, documentation, and communications. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3, 4 and 5 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): .2, 1.1, 2.2, 3.1 and 4.1 |
| Type: | Report |
| Groupwork: | Group, group and individually assessed |
| Weight: | 40% |
| Length: | Part 1: 2,000-4000 words, excluding references. |
Assessment task 4: Self-Reflection and Peer Review
| Intent: | This assessment is to provide opportunities for students to report and justify their individual contributions to the team, with self-reflections of their learning experience, self-development, and teamwork experience, and peer review the contributions of other team members. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 4 and 5 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): 2.2 and 3.1 |
| Type: | Reflection |
| Groupwork: | Individual |
| Weight: | 20% |
| Length: | 1,000-2,000 words |
Minimum requirements
To pass this subject, students must achieve an overall mark of 50% or greater.
References
Albright, S. C., & Winston, W. L. (2014). Business analytics: Data analysis & decision making. Cengage Learning.
Cao, L. (2018). Data Science Thinking: The next scientific, technological and economic revolution. Springer.
Cao, L. (2017). Data science: A comprehensive overview. ACM Computing Surveys, 50(3), 1-42.
Cao, L. (2012). Actionable knowledge discovery and delivery. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(2), 149-163.
Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.
Shmueli, G., Bruce, P. C., Gedeck, P., & Patel, N. R. (2019). Data mining for business analytics: concepts, techniques and applications in Python. John Wiley & Sons.
Cross-industry Standard Process for Data Mining