Data Engineering Course Descriptor

Course Summary

This course examines the engineering principles used (general and software) to investigate and manage the design, development and deployment of new data products within business. Learners will understand the techniques applicable to data engineering, data pipelines and the design, development and deployment of scalable data solutions.

Course Aims

• Train learners in the engineering principles: data engineering and software engineering.
• Give learners the technical knowledge and tools to develop data solutions in business.
• Train learners in the use of data pipelines.

Learning Outcomes

On successful completion of the course, learners will be able to:

Knowledge and Understanding

Subject Specific Skills

Transferable and Professional Skills

Teaching and Learning

This is an e-learning course, taught throughout the year. 

This course can be offered as a standalone short course.

Teaching and learning strategies for this course will include: 

• Online learning
• Online discussion groups
• Online assessment

Course information and supplementary materials will be available on the University’s Virtual Learning Environment (VLE).

Learners are required to attend and participate in all the formal and timetabled sessions for this course. Learners are also expected to manage their self-directed learning and independent study in support of the course.

The course learning and teaching hours will be structured as follows:

• Off-the-job learning and teaching (6 days x 7 hours) = 42 hours
• On-the-job learning (12 days x 7 hours) = 84 hours (e.g. 2 days per week for 6 weeks)
• Private study (4 hours per week) = 24 hours

Total = 150 hours

Workplace assignments (see below) will be completed as part of on-the-job learning.

Assessment

Formative

Learners will be formatively assessed during the course by means of set assignments. These will not count towards the final degree but will provide learners with developmental feedback. 

Summative

Feedback

Learners will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Regular tri-partite reviews between the learner (apprentice), their apprenticeship advisor (provider) and workplace line manager (employer) formally monitor and evaluate the learner’s progress.

Indicative Reading

Note: Comprehensive and current reading lists for courses are produced annually in the Course Syllabus or other documentation provided to learners; the indicative reading list provided below is used as part of the approval/modification process only.

Books 

Chan, Y., Talburt, J., and Talley, T.M. (2010). Data Engineering Mining, Information and Intelligence. New York : Springer

Kordon, A. (2020). Applying Data Science How to Create Value with Artificial Intelligence. Cham : Springer

Sommerville, I. (2016). Software engineering. Boston: Pearson.

Journals

Learners are encouraged to read material from relevant journals on data engineering as directed by their Course Leader.

Electronic Resources

Learners are encouraged to consult relevant websites on data engineering.

Indicative Topics

Learners will study the following topics: 

• Data engineering
• Software engineering
• Data pipelines