42893 Data Engineering Foundations
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Subject handbook information prior to 2025 is available in the Archives.
Credit points: 2 cp
Result type: Grade and marks
Description
This microcredential provides a comprehensive overview of the foundations of data engineering – a set of infrastructure platforms and capabilities that allows organisations to acquire, transform, store and curate data with the ultimate goal of realising value from data. Taking a broad perspective, the microcredential helps learners from diverse backgrounds to be better informed when working with data engineering teams or planning for data engineering as part of their team’s projects or operations. Beginning with an overview of a typical data value chain, the microcredential then introduces data infrastructure and data pipelines, alongside examples of implementation technologies. A range of issues around data quality, security, monitoring and governance are explored, with the ultimate goal of demonstrating how data engineering helps organisations extract and realise value from their data assets.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Apply basic data engineering principles to plan infrastructure and strategies that allow organisations to derive value from their data. (D.1) |
|---|---|
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- 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)
Teaching and learning strategies
This microcredential includes theoretical and practical components. Theoretical content will be presented as online learning modules for self-study in the LMS, supported by weekly, synchronous, one-hour online workshops facilitated by UTS academics. Regular formative quizzes embedded throughout the microcredential allow learners to gauge their progress and receive feedback on each module.
Small hands-on activities and case studies relating to the weekly topics will be provided to allow students to gain a deeper understanding of the theoretical concepts by engaging with data sets, case study examples, and working with some of the tools used in data engineering.
Content (topics)
- Overview of the data value chain, stakeholders and data as a business asset.
- Introduction to data pipelines and integrated data infrastructure engineering platforms.
- Data curation, including large-scale data storage and management, and technologies for data security, monitoring and governance.
- Realising value from data: an introduction to data optimisation, feature engineering, and packaging and delivery of data for different business users.
Assessment
Assessment task 1: Data Engineering Case Study Presentation
| Intent: | Demonstrate basic data engineering principles as applied to a case study scenario. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 |
| Type: | Presentation |
| Groupwork: | Group, individually assessed |
| Weight: | 100% |
| Length: | 5-minute video, followed by Q&A |
Minimum requirements
In order to pass the microcredential, a learner must achieve an overall mark of 50% or more.