Undergraduate Course: Work-Based Professional Practice A in Data Analytics (INFR09052)
|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
|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.
*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.
Entry Requirements (not applicable to Visiting Students)
||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
|Academic year 2020/21, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|Additional Information (Assessment)
||Coursework 100 %
A SLICC is assessed via three key components, a self-reflective report, an agreed portfolio of outputs and a formative self-assessment.
Self-critical Final Reflective Report (100% 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 linked to and draws upon your e-portfolio of evidence of your learning. Maximum word limit is 3000 words.
E-portfolio of evidence - At the proposal approval stage for your SLICC, your tutor/advisor 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 skills throughout your SLICC. Your portfolio should be constructed throughout the duration of your learning experience, demonstrating evolution, iteration and progress over-time. It must include a regular reflective blog diary. It may contain other evidence, which may take many forms including photographs, documents, reports, feedback, video, podcasts, etc.
Formative Self-Assessment - An important component of your final submission, in addition to your ability to self-critically reflect on your experience, is to demonstrate your understanding of your achievements through graded self-assessment. In your self-assessment you are required to demonstrate the alignment of the grades given by you for each learning outcome to the justification for them, and where this is evidenced within your portfolio.
||Feedback will be provided via the work based learning tutor.
|No Exam Information
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.
|- 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
|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.
|Course organiser||Dr Heather Yorston
|Course secretary||Mrs Michelle Bain
Tel: (0131 6)51 7607