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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Work-Based Professional Practice C in Data Analytics (INFR10083)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThis course is work-based and is focused on the real-world application of data analytics and the development of self-analytic skills 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. They will also learn how to reflect on their personal development throughout their time in the placement and their own professional development as a data scientist.
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 and machine learning. In addition, this course covers the meta-skills required to operate in a professional environment including teamwork, problem-solving and communication skills and personal self-awareness.

The course is delivered over four months during Semester 2 of year 4. Students are expected to spend around 200 hours in total on this course on data science activities embedded in the work activities the employer will set. The work will directly link to their own contexts in the workplace. Students will be directed in their learning using the Student-Led Individually Created Course (SLICC) approach. They will reflect on and evaluate data analysis studies 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. The final report which forms the summative assessment will draw together the evidence in the reflective journals and portfolios. All this is carried out 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, professional and personal development in the context of data analytics.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements This course is only available to fourth-year Graduate Apprentices on the Data Science (Graduate Apprenticeship) programme.
Course Delivery Information
Academic year 2022/23, Not available to visiting students (SS1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Seminar/Tutorial Hours 8, Supervised Practical/Workshop/Studio Hours 100, Feedback/Feedforward Hours 2, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 86 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Written Exam 0%
Practical Exam 0%
Coursework 100%

Students must maintain a reflective journal with weekly entries and an e-portfolio on which they base their final report. 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.
Feedback Students will receive regular feedback on their reflective journals and the contents of e-portfolios. Students will submit a draft of their final report and will receive formative feedback to enable them to improve their final summative report. They will be given individual advice on their presentation in order to improve the content and professionality of their communication. Students will meet regularly with a tutor to discuss any issues that arise in the workplace and to review their development of data science skills.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. 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.
  2. 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.
  3. 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.
  4. Review, evaluate and reflect upon their personal development of meta-skills in the work-place during their placement.
  5. Maintain a journal reflecting on the development of their meta-skills and data science skills along with an e-portfolio of supporting evidence.
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 - Cognitive skills: problem-solving, critical/analytical thinking, handling ambiguity.
- Responsibility, autonomy, effectiveness: independent learning, self-awareness and reflection, creativity, decision-making, leadership, organization and time management, flexibility and change management, ethical/social/professional awareness and responsibility, entrepreneurship.
- Communication: interpersonal/teamwork skills, verbal and/or written communication, cross-cultural or cross-disciplinary communication.

The development of graduate attributes is a key component of a graduate apprenticeship. Apprentices develop self-awareness and the ability to use self-reflection. During their work placement, they develop organisation and time management skills and ethical/social/professional awareness and responsibility. In addition, they gain problem-solving skills and data analytical skills.
Special Arrangements Only available to fourth-year Graduate Apprentices on the Data Science (Graduate Apprenticeship) programme.
KeywordsData science,Graduate Apprentice,Work-based Learning
Contacts
Course organiserDr Heather Yorston
Tel:
Email: Heather.Yorston@ed.ac.uk
Course secretaryMiss Lori Anderson
Tel: (0131 6)51 4164
Email: lori.anderson@ed.ac.uk
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