42081 Complex Data Analysis and Design
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Credit points: 6 cp
Subject level:
Postgraduate
Result type: Grade and marksRequisite(s): 42082 Introduction to Complex Systems
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Description
Industrial systems are getting increasingly complex due to the presence of technical, social and environmental subsystems. As such complex data analyses and design play an important role in supporting critical decisions. This subject introduces advanced analytical techniques required for modern day smart information modelling approach. Discussion and hands-on exercises related to these topics equip students to meet data analysis challenges in complex environments. This also helps students to appreciate the added challenges of dealing with unstructured data with appropriate commercial tools.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Identify and analyse organisational problems through data collection and analysis methods. (B.1) |
|---|---|
| 2. | Design complex data analyses and their application in the organisational context. (C.1) |
| 3. | Identify and apply quantitative tools and techniques to support decisions related to the design, development and operation of organisational systems (e.g. logistics, retail, utility). (D.1) |
| 4. | Collaboratively make evidence-based decisions, considering interactions among industrial systems/departments, to measure the organisational level impacts that flow from those decisions. (E.1) |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Socially Responsible: FEIT graduates identify, engage, and influence stakeholders, and apply expert judgment establishing and managing constraints, conflicts and uncertainties within a hazards and risk framework to define system requirements and interactivity. (B.1)
- Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
- Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
- Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating autonomously within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)
Teaching and learning strategies
Teaching strategies for this subject include both (i) face-to-face in-class lectures; and (ii) online learning activities including practice examples/problems, reading material and curated reading list.
A 3-hour face-to-face workshop containing both theory and practice-based learning activities will be delivered by the teaching team. These are designed to seamlessly introduce and reinforce theoretical concepts, tools, and methods. Then, learning activities will transition towards designing and synthesising these concepts to address organisational problems. Students are encouraged to attend and actively participate in the in-class activities to improve their understanding of the key concepts discussed in the class.
Lectures include a range of audio, visual teaching methods combined with a set of written materials which will be posted online. In-class activities are designed each week to support students to work collaboratively, with the support of knowledgeable lecturers, solving case studies via
- enhanced thought process to deal with system wide nuances and design issues in appropriate problem definition; and
- application of a set of commercially available (or open-source) software tools to deal with the technical challenges that flow from the problem definitions and data analysis and designs.
Several activities are planned to encourage translation of organisational issues to a problem which can be solved appropriately using theories/techniques/tools discussed in the class. In-class activities are designed so that students can benefit from it to properly complete assessment tasks through appropriate guidance from the lecturers in the class.
Content (topics)
- Introduction to organisational analytics (data and design)
- Developing explanations from data-driven models
- Social media analysis
- Optimisation for complex organisational problems
- Advanced approaches in complex modelling
- Advanced topics (Blockchains, Internet of Things, Choice models, Fair awareness in data models, Automated ML)
Assessment
Assessment task 1: Individual report
| Intent: | This assessment will evaluate student learning towards the design, data collection and application of a data analytical model, providing appropriate managerial recommendations. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2 and 3 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): B.1, C.1 and D.1 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 30% |
| Length: | 1500 words |
Assessment task 2: Mid-term test
| Intent: | This assessment will evaluate general understanding about the complex data analytics and optimisation techniques and situations where these could be applicable. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2 and 3 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): B.1, C.1 and D.1 |
| Type: | Quiz/test |
| Groupwork: | Individual |
| Weight: | 30% |
| Length: | 2 hours |
Assessment task 3: Case study project
| Intent: | This assessment will assess student ability and skills to design and address a set of business challenges from a case study and report and present their findings. |
|---|---|
| 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): B.1, C.1, D.1 and E.1 |
| Type: | Project |
| Groupwork: | Group, individually assessed |
| Weight: | 40% |
| Length: | 4000 words |
Minimum requirements
To pass this subject, students must achieve an overall mark of 50% or greater.
Required texts
Essentials of Business Analytics, Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson, Dennis J. Sweeney, ISBN 9781305627734, Edition 2
Recommended texts
- Business Statistics and Analytics in Practice, 9th Edition, Bruce Bowerman and Anne M. Drougas and William M. Duckworth and Amy G. Froelich and Ruth M. Hummel and Kyle B. Moninger and Patrick J. Schur, ISBN10: 1260187497
- The Analytics Edge, Bertsimas, D., Allison, K. O., & Pulleyblank, W. R., Dynamic Ideas LLC, ISBN-10: 098991089X
- Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies, Kelleher, J D, B M Namee, and A D’Arcy, The MIT Press, ISBN-10: 0262029448