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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Work-Based Professional Practice B in Data Analytics (INFR10075)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits40 ECTS Credits20
SummaryThis 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: ( Introduction to Linear Algebra (MATH08057) AND Proofs and Problem Solving (MATH08059) AND Calculus and its Applications (MATH08058) AND Informatics 1 - Introduction to Computation (INFR08025) AND Informatics 1 - Object-Oriented Programming (INFR08014) AND Informatics 1 - Data and Analysis (INFR08015)) AND ( Several Variable Calculus and Differential Equations (MATH08063) AND Probability (MATH08066) AND Statistics (Year 2) (MATH08051) AND Computing and Numerics (MATH08065) AND Facets of Mathematics (MATH08068) AND Informatics 2C - Introduction to Software Engineering (INFR08019) AND Informatics 2D - Reasoning and Agents (INFR08010) AND Informatics 2 - Introduction to Algorithms and Data Structures (INFR08026) AND Informatics 2B - Learning (INFR08028))
Co-requisites Students MUST also take: Work-Based Professional Practice A in Data Analytics (INFR09052) AND Introductory Applied Machine Learning (INFR10069) AND Statistical Methodology (MATH10095) AND Numerical Linear Algebra (MATH10098)
Prohibited Combinations Other requirements Pre-requisites: Students must have passed the compulsory courses of Year 1 of the Data Science (Graduate Apprenticeship) BSc from academic year 2018/19 and compulsory courses of Year 2 of the Data Science (Graduate Apprenticeship) BSc from academic year 2019/20.

Co-requisites: Students MUST also take the appropriate courses selected form Year 3 of the Data Science (Graduate Apprenticeship) BSc from academic year 2020/21.

This course is not available to other students internally or externally that are not on the Data Science (Graduate Apprenticeship) BSc.
Course Delivery Information
Academic year 2020/21, Not available to visiting students (SS1) 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 )
Additional Information (Learning and Teaching) 15 hours programme level activities / 385 directed and independent learning activities
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Written Exam 0 %, Practical Exam 0 %, 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 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:
  1. Demonstrate an understanding of the cross-disciplinary nature of data science, and the complexities, challenges and wider implications of the contexts in which data science problems occur in the workplace
  2. Draw on and apply relevant data science approaches, tools and frameworks for data enquiry to different settings in real world situations;
  3. Frame and address data science business problems, questions and issues as a data study project, being aware of the environment and context in which the problem exists;
  4. Review, evaluate and reflect upon knowledge, skills and practices in data science.
  5. 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.
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.
KeywordsGraduate Apprenticeship,PwC,Data Science
Course organiserDr Heather Yorston
Course secretaryMrs Michelle Bain
Tel: (0131 6)51 7607
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