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Taking a broad perspective, this microcredential helps participants 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.
This microcredential aligns with the 2 credit point subject, Data Engineering Foundations (42893) in the Graduate Certificate in Professional Practice (C11298), Graduate Diploma of Professional Practice (C06136), Master of Professional Practice (C04404), Graduate Certificate in Technology (C11301), Graduate Diploma in Technology (C06137) and Master of Technology (C04406).
This microcredential may qualify for recognition of prior learning at this and other institutions.
This course is accessible to professionals from a wide range of sectors and backgrounds who are new to the field of data engineering.
UTS microcredentials are developed for professionals with a capacity to undertake postgraduate tertiary education.
Full price: $1,595.00 (GST-free)*
*Price subject to change. Please check price at time of purchase.
During this course you will meet and work with a dedicated course facilitator, who will support your learning and engagement with teaching resources designed by the lead academic and team of experts in the Faculty of Engineering and Information Technology.
This course is structured into four modules. Each module comprises self-study materials and facilitated online sessions. The four modules and key topics covered are:
Module 1 - Data value chain
Module 2 - Data infrastructure and data pipelines
Module 3 - Data Curation
Module 4 - Realising value from data
This course is delivered in a scheduled format over ten weeks.
Each week (during weeks 1 to 8) you will participate in an online session where you will have the chance to apply what you've learned, ask questions and hear from other participants who are taking the course with you. The workshops are led by the course facilitator.
Weeks nine and ten are planned to give you time to complete the final assignment, with support and scheduled Q&A sessions provided.
This course includes weekly, live, one-hour online workshops facilitated by an expert UTS academic, supporting self-study and online learning activities. Case studies of real-world business illustrate applications of data mining techniques. The workshop sessions focus on hands-on experience in data mining and data analytics tools, and understanding and interpretation of the results. Regular formative quizzes throughout the microcredential will allow participants to gauge their progress.
Upon successful completion of this microcredential, you will be able to apply basic data engineering principles to plan infrastructure and strategies that allow organisations to derive value from their data.
In the assessment task, participants will work in teams of two or three to apply basic data engineering principles to a case study scenario. The goal is to illustrate how data engineering infrastructure and strategies covered in the course can be used to help organisations derive greater value from their data, for example, through the development of new data-driven products, better business decision-making or improved data management capabilities.
Participants work in small teams, however each participant will be assessed individually on their work in the task.
Participants individually produce a five-minute video centred on the provided case study, applying principles learned during the course. To accompany the video, participants are required to submit a one-page summary of their individual learning experience. Each participant in the team should present a different aspect of the case study work, so that the team’s videos are complementary and can be combined to form a larger, overall presentation. The team videos should collectively demonstrate the participants’ technical knowledge of data engineering infrastructure platforms, reflection upon practical skills covered, knowledge of key issues such as data quality, security, monitoring and governance and an overarching appreciation of how these elements work together to benefit an organisation and its various stakeholders. A more detailed marking rubric will be provided on the LMS.
After submitting the combined team video, participants will be invited to a live Q&A session where they can engage with academic staff and ask questions about their video. The Q&A session should be around 10-15 minutes per team.
Participants will also be asked to asynchronously peer review one other team’s video presentation and provide feedback.
In order to pass the microcredential, participants must achieve an overall mark of 50% or more.
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