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 worth 20-credits. It is not a stand-alone course and can only be delivered as part of the Graduate Apprenticeship BSc Hons in Data Science.*
It is delivered over two ten-week professional practice periods in the summer at the end of years 1 and 2 and students are expected to spend around 200 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 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 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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Course Delivery Information
| Not being delivered |
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 implications 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.
- Frame and address data analysis problems, questions and issues as a data analysis study, being aware of the environment and context in which the problem exists.
- Review, evaluate and reflect upon knowledge, skills and practices in data analytics.
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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,Graduate Apprenticeship,PwC |
Contacts
| Course organiser | Mrs Alison Heather Yorston
Tel:
Email: Heather.Yorston@ed.ac.uk |
Course secretary | |
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