Undergraduate Course: Work-Based Professional Practice B in Data Analytics (INFR10075)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 40 |
ECTS Credits | 20 |
| Summary | This course is work-based and is focused on the real-world application of data science in a workplace environment. It includes experiencing how statistical modelling , machine learning and relevant algorithms are applied to conduct data science studies on real data in a commercial environment. Students who do this course will obtain practical experience in the design, implementation, and evaluation of data science approaches. |
| Course description |
*This course is not a stand-alone introduction to applied data analytics and can only be delivered as part of the BSc Hons Graduate Apprenticeship in Data Science.*
This course provides Graduate Apprenticeship students with a holistic approach to business problem solving to support decision making and providing business insights. It is a key stage in the learning and development strategy of the graduate apprenticeship programme in Data Science. It is project based, introduced in the university and facilitated in the workplace around team-based projects.
This is a work-based learning course worth 40-credits. Students undertake an eight-month professional practice period in year 3 over semester 2 and the summer and are expected to spend around 400 hours in total on this course. This is in addition to work activities the employer will be setting. The SLICC will be planned to cover the group of graduate apprenticeship students working with a specific employer and the work will directly link to their own contexts in the workplace.
The main topics are: the application of data science tools and techniques, developing an understanding of the application of machine learning, statistical modelling and algorithms to solve business problems. In addition, this course covers the meta skills required to operate in a professional environment including graduate attributes for: lifelong learning, aspiration and personal development, outlook and engagement, research and enquiry, personal and intellectual autonomy, personal effectiveness and communication in both university and the workplace
The year 3 taught courses in computing and mathematics, particularly those in statistics and machine learning are applied to real world data science problems and projects.
Students will journal their learning using the Student-Led Individually Created Course (SLICC) approach. The SLICC framework requires that students use the generic learning outcomes to articulate their data science learning in the context of their work tasks, reflect frequently in a reflective journal, and collect and curate evidence in an e-portfolio of both their data science learning and metaskills development during their 8 months placement. They will experience different applications of data analytics in different projects during their placement and will have access to a company career coach to aid their professional development. They will also produce a final report in the format required for their employer. All this is with the guidance of a professional practice academic tutor.
The course will encourage appraisal of students' own practical experiences and allow them to reflect on their learning in the context of data analytics.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | Successful completion of Year 2 of the Data Science (Graduate Apprenticeship) degree. This course is not open to students on any other degree. |
Course Delivery Information
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| Academic year 2023/24, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Flexible |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
400
(
Programme Level Learning and Teaching Hours 8,
Directed Learning and Independent Learning Hours
392 )
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| Additional Information (Learning and Teaching) |
15 hours programme level activities / 385 directed and independent learning activities
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Written Exam 0 %, Practical Exam 0 %, Coursework 100 %
The course is assessed via four key components, a self-reflective report, an agreed portfolio of outputs, a presentation and the reflective journal.
Self-critical Final Reflective Report (25% weighting) - The reflective report is a key component of your assessment. You are expected to document and demonstrate active self-critical reflection and responses to your learning throughout your experience. It is essential that your report is linked to and draws upon your e-portfolio of evidence of your learning. The maximum word limit is 3000 words.
E-portfolio of evidence (25% weighting) - At the start of the course, your work-based learning tutor will discuss and agree with you what outputs and information need to be created, collated and submitted in your portfolio. This e-portfolio will support and provide evidence for your learning and development of data science and meta-skills throughout the course. Your portfolio should be constructed throughout the duration of your learning experience, demonstrating evolution, iteration and progress over time. It may contain other evidence, which may take many forms including photographs, documents, reports, feedback, video, podcasts, etc.
A tripartite presentation (25% weighting) of the data skills learned and employed in the placement to an audience of managers and peers.
A self-reflective journal (25% weighting) - An important component of this placement. In addition to your ability to self-critically reflect on your professional experience, is to demonstrate your understanding of your achievements through the reflection of meta-skills and data skills gained in the workplace. This should be maintained weekly and is assessed on the regular depth of reflection on weekly work experiences. Quality and depth of reflection is important rather than lengthy entries. It should identify personal strengths and weaknesses, opportunities for development and potential threats to progress or successfully completing work tasks.
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| Feedback |
Feedback will be provided via the work based learning tutor. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate an understanding of applied data science, and the challenges and wider im-plications of the contexts in which data analysis problems occur in the workplace.
- Draw on and apply relevant data analysis approaches, tools and frameworks from their courses in mathematics and computing in different settings in real world situations.
- Develop and apply skills and attributes to engage effectively on data analysis issues in the workplace, including problem solving, communicating clearly and for reflective thinking.
- Review, evaluate and reflect upon their personal development of meta-skills in the work-place during their placement.
- Maintain a journal reflecting on the development of their meta-skills and data science skills along with an e-portfolio of supporting evidence.
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Reading List
Bolton, G. 2010.Reflective Practice: Writing and Professional Development. 3rd Ed. London: Sage
Boud, D., Keogh, R. and Walker, D. 2005.Reflection: Turning Experience into Learning. Oxon: Routledge Falmer
Fook, J. and Gardner, F. 2007.Practising critical reflection : a resource handbook Maidenhead: Open University Press
Kolb D.A. 1984.Experiential learning : experience as the source of learning and development New Jersey: Prentice Hall
Moon, J.A.. (2006). Learning journals: a handbook for reflective practice and professional development (2nd edition). Abingdon: Routledge. Mumford, J. and
Roodhouse, S. (eds.) (2012). Understanding work based learning. Farnham: Gower.
Tarrant, P. (2013). Reflective practice and professional development. London: SAGE
Williams, K., Woolliams, M. and Spiro, J. 2012. Reflective writing Basingstoke: Palgrave Macmillan |
Additional Information
| Graduate Attributes and Skills |
Development of graduate attributes are a key component of a graduate apprenticeship. In this course there is specific reference to the development and application of skills and attributes to engage effectively on data analysis issues in the workplace, including problem solving, communicating clearly and for reflective thinking. |
| Keywords | Graduate Apprenticeship,PwC,Data Science |
Contacts
| Course organiser | Dr Heather Yorston
Tel:
Email: |
Course secretary | Mrs Michelle Bain
Tel: (0131 6)51 7607
Email: |
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