Undergraduate Course: Work-Based Professional Practice A in Data Analytics (INFR09052)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 9 (Year 3 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course is work-based and is focused on the real-world application of data analytics in a workplace environment. It includes experiencing how computation, analysis, mathematical modelling and statistics are applied to conduct data analysis 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 analysis approaches. |
| Course description |
*This is a work-based course and is only available as part of the Graduate Apprenticeship BSc Hons in Data Science.*
The aim of this course is to provide data science graduate apprenticeship students with work-based professional practice in the application of data analysis and statistical techniques. It gives students a practical introduction and understanding of the foundations, concepts and techniques applied to data analytics and provides an opportunity to apply the learning gained in the core courses to address data analysis problems and challenges in the workplace.
The main topics are: the application of data analysis tools and techniques, an introduction to common data quality problems, and the application of statistics. In addition, this course covers the meta skills required to operate in a professional environment including: teamwork, project, problem solving and communication skills.
The course is delivered over two ten-week professional practice periods in the summer at the end of years 1 and 2 and assessed via a final report due in year 3. Students are expected to spend around 200 hours in total on this course, in addition to work activities the employer will be setting. The work will directly link to their own contexts in the workplace. The year 1 core courses in computing and mathematics are applied to real world data analysis problems and projects. This is further developed in year 2 where knowledge of probability, statistics and computer science are applied to
more complex real-world data analysis problems.
Students will be directed in their learning using the Student-Led Individually Created Course (SLICC) approach. They will plan, propose, carry out, reflect on and evaluate a data analysis study from their own work context in data analytics. The SLICC framework requires that students use the generic learning outcomes to articulate their learning in their own defined project, reflect frequently using a blog, and collect and curate evidence of their learning in an e-portfolio. They receive relevant formative feedback on a Midway Reflective Report, which is the same format as the Final Reflective Report, which forms the summative assessment. 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 2022/23, 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:
200
(
Lecture Hours 10,
Supervised Practical/Workshop/Studio Hours 100,
Summative Assessment Hours 40,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
46 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Coursework 100 %
The course is assessed via three key components: a self-reflective report, an agreed portfolio of outputs and a reflective journal.
Self-critical Final Reflective Report (50% weighting) - The reflective report is the 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 evidently 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 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.
Reflective journal (25% weighting) - your reflective journal should contain weekly entries on the development of your data science skills and meta-skills in the context of academic and professional practice. It should identify personal strengths and weaknesses, opportunities for development and potential threats to achieving goals or making progress.
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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 | Data Science,Statistics,Computing |
Contacts
| Course organiser | Dr Heather Yorston
Tel:
Email: Heather.Yorston@ed.ac.uk |
Course secretary | Mrs Michelle Bain
Tel: (0131 6)51 7607
Email: michelle.bain@ed.ac.uk |
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