ࡱ>  0Z=bjbj11 p[gp[g{4+nn))P)P)P)d)d)d)8)0*d)CD+,,,,57\k70C C C C C C C$EHb-CP)7?5@577-C)),,BC{:{:{:7)8,P),C{:7C{:{:rOBT8)B,)8~BBXC0CBH9jHBHP)B477{:77777-C-C:j777C7777H777777777nX ':  Programme Details 1. Programme titleData Science2. Programme codeINFU063. QAA FHEQ levelLevel 64. FacultySocial Sciences5. DepartmentInformation School6. Other departments providing credit bearing modules for the programmeNot applicable7. Accrediting Professional or Statutory BodyNot applicable8. Date of production/revisionNovember 2021 AwardsType of awardDuration9. Final awardBachelor of Science with Honours (BSc Hons)3 years full-time10. Intermediate awards Not applicable Programme Codes 11. JACS code(s) Select between one and three codes from the  HYPERLINK "https://www.hesa.ac.uk/support/documentation/jacs/jacs3-principal" \h HESA website.I900: Others in Computer Sciences I260: Data Management12. HECoS code(s) Select between one and three codes from the  HYPERLINK "https://www.hesa.ac.uk/innovation/hecos" \h HECoS vocabulary.100755 - Data Management - 50% 100366 - Computer Science - 50% Programme Delivery 13. Mode of study Full-time 14. Mode of delivery Taught, face-to-face 15. Background to the programme and subject area This BSc Data Science has two strong themes which differentiate it from other Data Science degrees. These themes are data translation and responsible data science. They capitalise on the extensive multidisciplinary expertise and real-world experience of the iSchools network to improve data science as a discipline and on our own expertise in FATES (Fairness, Accountability, Transparency, Ethics and Security), social justice and sustainable futures. Data Translators There is increasing evidence that data scientists need to be better equipped to ask the right questions, draw on theory and skills from a variety of disciplines, and implement a critical and reflective decision-making framework to apply technical data science skills in organisational and societal contexts. These skills align with the emergence of a new role within the data profession sometimes labelled a Data Translator. A Data Translator is someone who can bridge the gap in expertise between employees with more technical-analytical-infrastructure roles on the one hand, and the business stakeholders on the other. BSc Data Science will help build a new generation of data translators who are able to bring data skills, organisational experience and social, ethical and ecological awareness to the context of big business, start-ups, social enterprises, sustainable development and the third sector. Responsible Data Science Most UK undergraduate data science-related degrees emphasise technical skills such as statistics, predictive modelling and big data analysis. They teach what they view as day-to-day technical aspects, such as developing and implementing the supporting infrastructures and performing regular data analysis activities. Organisational and societal contexts of data applications are taught tendentially. Consequently, data science graduates are often coding algorithms without an understanding of their real-world implications. Responsible data science is a key USP of BSc Data Science, with emphasis on: Data for good and the embedding of FATES principles in Data Science and Artificial Intelligence; Understanding the contexts in which data science techniques are applied; The development of data science techniques that promote a sustainable future; The potential of Artificial Intelligence applications to contribute to biases that perpetuate social inequalities. 16. Programme aims BSc Data Science aims to:A1Equip students with the capabilities to work at the boundary between data science roles and managerial or policy-making roles.A2Equip students with the attributes and understandings needed to develop and use data-informed solutions which address social inequalities and promote sustainable futures and inclusivity.A3Equip students with the data, information and analytical literacies to enable them to become critical data professionals, evidence-based practitioners and successful lifelong learners.A4Equip students with the competencies to apply data science and Artificial Intelligence (AI) principles in order to derive insight, communicate findings and support proactive decision-making for responsible outcomes.A5Provide students with the competencies needed to effectively use industry-standard processes and innovative techniques within the data lifecycle and Artificial Intelligence (AI) applications. 17. Programme learning outcomes Knowledge and understanding On successful completion of the programme, students will be able to:Links to Aim(s)K1Describe the conceptual underpinnings of data science, their development as fields of study, and the cultural, social, political and historical contexts within which they are embedded locally, nationally and globally.A4K2Explain how data science techniques are applied in Artificial Intelligence (AI) and demonstrate an understanding of the associated issues (e.g. bias and human autonomy).A2, A3, A4, A5K3Evaluate and synthesise issues concerning equity and inclusion in the context of data-related practices and those affected by them.A2, A3K4Understand how data governance (including data stewardship), effective leadership and strategic thinking can contribute to responsible data-related services and policies.A1, A4K5Explain how to identify the priorities of key stakeholders in organisations and other contexts and communicate persuasively the value that data science and Artificial Intelligence (AI) have for creating effective and responsible outputs and insights.A1, A4K6Demonstrate an understanding of the processes of public policy making, organisational strategy and commercial awareness to ensure data science insights and Artificial Intelligence (AI) are used effectively and responsibly.A1, A3Skills and other attributes On successful completion of the programme, students will be able to:S1Understand how data (including big data) are obtained and created, and how this shapes its appropriateness for future use.A5S2Critique and develop data science - including its experiments, related practices, processes, instruments, systems and infrastructures - and evaluate specific approaches empirically and ethically.A3, A5S3Create and implement appropriate data science methods to discover relations, make useful predictions and deliver insights, applying criticality to assure approaches are appropriate to specific contexts.A5S4Analyse and critically evaluate a wide range of real-life problem-contexts from a data science perspective that incorporates ethics, sustainability, impact and the dissemination of the benefits or knowledge to wider society.A2, A3S5Demonstrate digital and data fluency in the application of appropriate visualisation and statistical methods in order to describe, explore, analyse and present data for different audiences and stakeholders.A1, A5S6Confidently, appropriately, effectively and persuasively communicate findings, insights, ideas and issues in order to lead, inform or inspire different audiences/stakeholders, including by means of data visualisations.A1, A4S7Develop the interpersonal skills needed to effectively collaborate with others in order to create responsible data solutions.A1 18. Learning and teaching methods The programme is underpinned by an inquiry-based pedagogy: one which provides rich and varied opportunities for students to apply data science to real world problems (K1-K6, S1-S7) Supporting the programmes inquiry-based pedagogy are the more traditional learning teaching methods (e.g lectures, seminars, workshops and computer laboratories) and a strong emphasis on digital education including flipped classrooms and the use of online communication tools (K1-K6, S1-S7) The more technical aspects of the programme will use teaching and learning methods inspired by successful industry approaches including peer instruction, live coding and paired programming (K6, S5, S7) Students are supported to become self-directed learners by incorporating tasks which promote reflection, information literacy and team working (K1-K6, S1-S7) Alongside an annual industry day, Industry case studies and industry-related inquiries enhance students employability and contextualise learning (K1-K6, S1-S7) 19. Assessment and feedback methods Assessment A diverse and balanced range of formative and summative assessment types will be provided to cater for students different learning preferences, styles and circumstances (K1-K6, S1-S7). Formative assessment types include weekly quizzes, interim submissions, personal portfolios, team challenges and group presentations (K1-K6, S1-S7). Summative assessments types include individual reports, individual essays, individual e-portfolios, invigilated exams, team produced videos, group presentations and group posters (K1-K6, S1-S7). Feedback A diverse and balanced range of formative and summative feedback types will be provided to cater for students different learning preferences, styles and circumstances (K1-K6, S1-S7). Formative feedback types include immediate grades from weekly quizzes, written feedback from interim submissions, verbal feedback after team challenges and peer feedback during inquiries (K1-K6, S1-S7). Summative feedback types primarily include Turnitin text comments and rubric grades (K1-K6, S1-S7). The Information Schools Teaching Support Team has mechanisms to monitor and report the timeliness of module teams summative feedback to students. 20. Programme structure and student development Level 1 is foundational and all modules are core. Students will develop the data science, professional and academic capabilities, grounded in reflective, and applied approaches, to be successful in Level 2, Level 3 and beyond. The Data Science Foundations and Contexts 40 credit module spans Semester 1 and 2 providing an academic and pastoral anchor point to students studies. At Level 2 all modules are core. Students apply and develop many of the foundational capabilities and understandings gained at Level 1 to the context of a data lifecycle and a team-based project. The Responsible Data Science Lab (1) 40 credit module spans Semester 1 and 2, consolidating, developing and applying the competencies and understandings acquired in the other Level 2 modules. At Level 3 all modules are core. Modules primarily provide opportunities to apply the capabilities gained at Level 1 and Level 2 to support students career aspirations/specialisms. Responsible Data Science Lab (2) is an inquiry-based capstone 60 credit module for S1 to S7. It consolidates, develops and applies the skills acquired in the other Level 1, 2 and 3 modules. The Data Science Portfolio is a capstone 20 credit module for K1 to K6. It consolidates, develops and applies the knowledge acquired in the other Level 1, 2 and 3 modules.Detailed information about the structure of programmes, regulations concerning assessment and progression and descriptions of individual modules are published in the University Calendar available online at  HYPERLINK "http://www.sheffield.ac.uk/calendar" http://www.sheffield.ac.uk/calendar 21. Criteria for admission to the programme Detailed entry criteria are yet to be agreed with Admissions colleagues but we anticipate that we will expect ABB or equivalent as a standard entry point, with a reduction of one grade or two grades for WP candidates as assessed by Admissions. We will also welcome applications from those with non-traditional qualifications and/or with significant workplace experience and will consider these applications individually. We are working with colleagues in the Department for Lifelong Learning to introduce a Foundation Year pathway for mature students. 22. Reference points The learning outcomes have been developed to reflect the following points of reference: Subject Benchmark Statements  HYPERLINK "https://www.qaa.ac.uk/quality-code/subject-benchmark-statements" https://www.qaa.ac.uk/quality-code/subject-benchmark-statements Framework for Higher Education Qualifications (2014)  HYPERLINK "https://www.qaa.ac.uk/docs/qaa/quality-code/qualifications-frameworks.pdf" https://www.qaa.ac.uk/docs/qaa/quality-code/qualifications-frameworks.pdf University Vision  HYPERLINK "/vision" /vision Learning and Teaching Strategy (2016-21)  HYPERLINK "/polopoly_fs/1.661828!/file/FinalStrategy.pdf" /polopoly_fs/1.661828!/file/FinalStrategy.pdf Subject specific reference points (Details can be found  HYPERLINK "https://docs.google.com/spreadsheets/d/1GYT7HPkwvp63VVTAtcJJh2cSOen0KEeKuVNdtHUcFqg/edit?usp=sharing" \h here): QAA (computing) QAA (Business and management) CILIP Data Science Competencies ACM/IEEE National Academies Press Undergraduate Data Science 23. Additional information   This specification represents a concise statement about the main features of the programme and should be considered alongside other sources of information provided by the teaching department(s) and the University. 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