Data Science Synoptic Project and End Point Assessment Course Descriptor

Course Summary

This module is a data science project, conceived and executed by the learner in an external organisation. The project will demonstrate a high-level of technical and analytical skill, aligned to achieving organisational goals and enabling effective institutional change. The project may focus on any element of the data science workflow and will culminate with a dissertation. The dissertation will combine technical research with organisational needs and project management and will enable the learner to deepen his or her understanding of a particular area of data science. Project assessment includes a compulsory knowledge test and professional discussion (viva voce examination) at the end of the process, as per the end point assessment for apprenticeships (see EPA for Data Scientist [Integrated Degree] apprenticeship, for more details). 

Course Aims

• Give learners the opportunity to carry out independent research and in-depth analysis in data science.
• Train learners to write up their findings and ideas clearly, coherently and to a high-professional standard.
• Train learners to present their own arguments logically and competently, to engage specialist and non-specialist stakeholders.

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

The contact hours on this course are formed predominantly of supervisory meetings, typically 4 x 1 hour.

Learners are expected to carry out independent research into the topic. Readings should include a mix of books, journal articles, policy papers and other relevant documents, depending on the topic and the approach taken in the dissertation.

Course information and supplementary materials are 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 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 (18 days x 7 hours) = 126 hours (e.g. 1 day per week for 18 weeks)
• On-the-job learning (54 days x 7 hours) = 378 hours (e.g. 3 days per week for 18 weeks)
• Private study (5 hours per week for 18 weeks) = 90 hours

Assessment

Formative

Learners will be formatively assessed during the course by means of a project plan review. This will not count towards the final degree but will provide learners with developmental feedback. 

Summative

Assessment will be in two forms:

AE1 must be successfully completed (passed) before AE 2 & AE3 can be attempted. AE2 must be attempted (submitted) before AE3 can be attempted.

Feedback

Learners will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Learners will also attend a formal meeting with their Academic Mentor (and for apprentices, including their Line Manager). These bi- or tri-partite reviews will monitor and evaluate the learner’s progress.

Indicative Reading

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

Books

• Walliman, N., (2004), Your Undergraduate Dissertation: The Essential Guide for Success, London: Sage.
• Swetnam, D., (2001), Writing Your Dissertation: How to Plan, Prepare and Present Your Work Successfully, Begbroke: How To Books Ltd.

Journals

Learners are encouraged to consult relevant journals on their relevant specialism.

Electronics Resources

Learners are encouraged to consult relevant electronic resources on their relevant specialism.

Indicative Topics

• How to solve a technological problem based on an organisation’s problem
• Managing technology projects to a successful outcome
• Using real-world data and scenarios