BSc (Hons) Data Science Programme Specification

Aims

The BSc in Data Science aims to combine rigorous study of data science with a personalised elective pathway that complements (deepens, broadens, contextualises) data science in line with the student’s values, interests, or goals. Data Science has emerged as a discipline due to the confluence of two major events: the emergence of big data; and the convergence of programming, machine learning, and visualisation as complementary tools for the analysis and understanding of data. The programme studies the collection, manipulation, storage, retrieval, and computational analysis of data in various forms (e.g., numeric, textual, image, and video data) and scales (small to big data). Coursework covers topics such as predictive analytics, machine learning, data mining, and information visualisation.

The curriculum prepares full-time students for careers or graduate studies in data analytics and machine learning and advance the discipline; and to provide the necessary knowledge and practical skills to apply data science to advance other areas (e.g., politics, business, or history).

The programme is designed with the aim that, in pursuing their studies, students also gain core competencies (e.g., writing across audiences and genres, employing ethical reasoning, analysing and using data, integrating knowledge and skills through experience) that empower them for sustained impact, success, and self-actualisation.

Structure and Requirements

The degree regulations require that students take courses to the value of 360 credits across three years, with at most 120 credits at L4, and at least 90 credits at L6.

Optional and Elective courses

By definition: an ‘option’ is a course that a student on the programme is not required to take but that they may take in order to fulfil a discipline-specific requirement; whereas ‘electives’ are any other (non-required) courses that the student may take to fulfil the wider, overall, programme requirements.

Choosing Options or Electives

Students will be asked to select options and/or electives in advance of each new academic year, and in this process will receive dedicated support and guidance from Academic Advisors, who will also connect them with faculty as appropriate.

*University Courses List Condition

The optional/elective courses that run in each academic year are subject to change in line with faculty availability and student demand, and may be capped or be unavailable in the timetable, so there is no guarantee every optional/elective course will be available every year. Where a course is set to run, students for whom it is an option will typically be given priority over students for whom it is only an elective.

For the most up-to-date list of courses, please visit the University Courses webpage.

First Year 

Required course list:

• LCSCI4212 Discrete Structures (L4, 15 credits)
• LDSCI4211 Programming with Data (L4, 15 credits)
• LDSCI4210 Intermediate Programming with Data (L4, 15 credits)
• LDSCI4209 Probability and Statistics (L4, 15 credits)

In addition, take available L4 courses from the University Courses list* to add up to 120 credits, selected with an Academic Advisor so as to support progress towards meeting all (including core) programme requirements by the end of the third year.

Second Year

Required course list:

• LDSCI5206 Advanced Programming with Data (L5, 15 credits)
• LDSCI5247 Foundations of Data Science (L5, 15 credits)
• LCSCI5208 Database Design (L5, 15 credits)
• LDSCI5209 Information Presentation and Visualisation (L5, 15 credits)

Elective courses include:

• LDSCI5207 Experimental Data Science Project (L5, 15 credits)

In addition, take available L5 courses from the University Courses list* to add up to 120 credits, selected with an Academic Advisor so as to support progress towards meeting all (including core) programme requirements by the end of the third year.

This programme is designed to enable eligible students the option to progress through their degree by studying abroad, at another global location, in the second semester of their second year. Advice and support on specific opportunities will be provided by the Academic Advisors.

Third Year 

Required course list:

• LDSCI6210 Machine Learning and Data Mining I (L6, 15 credits)
• LDSCI6211 Machine Learning and Data Mining II (L6, 15 credits)
• LDSCI6209 Large-Scale Information Storage and Retrieval (L6, 15 credits)
• LDSCI6208 Final Project (Computer/Data Science) (L6, 30 credits)

In addition, take available courses (typically all at L6) from the University Courses list* to add up to 120 credits, selected with an Academic Advisor so as to support progress towards meeting all (including core) programme requirements by the end of the year.

Core Requirements

By completion of their degree, each student is required to have passed courses with the following attributes. (Please note: codes indicated on the course descriptors and in the University Courses list.)

All of the following:

• Writing across Audiences and Genres
  • Writing Intensive (WI) x 2
  • Communicating in Public and Professional Contexts (CPPC)
• Integrating Knowledge and Skills through Experience (EX)
• Demonstrating Thought and Action in a Final Project (FP)

At least FOUR of the following EIGHT:

• Engaging with the Natural and Designed World (ND)
• Exploring Creative Expression and Innovation (EI)
• Interpreting Culture (IC)
• Conducting Formal and Quantitative Reasoning (FQ)
• Understanding Societies and Institutions (SI)
• Analysing and Using Data (AD)
• Engaging Differences and Diversity (DD)
• Employing Ethical Reasoning (ER)

Elective Pathways

Students who take the equivalent of at least THREE courses across at least TWO levels in a defined area outside of their main degree discipline requirements, may apply to receive recognition for this (e.g., in addition to BSc (Hons) Data Science on their degree certificate, a letter and transcript will include Sustainability as a pathway). Courses may not be double counted across pathways.

Entrance Requirements

Age

The University requires applicants to be at least 18 years old on 1 September in the year of entry.

General Entrance Requirements

The University’s typical offer for undergraduate study is AAB at A Level, 35 points or 6,6,5 in Higher Level (HL) subjects in the International Baccalaureate (IB) Diploma, or the equivalent. A Level General Studies, Critical Thinking, Thinking Skills and Global Perspectives are not accepted by the University. Students studying the Extended Project Qualification (EPQ) alongside three A Levels may be eligible for an alternative offer. For the IB, the overall score of 35 points includes Theory of Knowledge and the Extended Essay, and students achieving the University’s alternative offer of 6,6,5 in HL subjects must also achieve an overall pass in the IB Diploma for entry to our programmes.

If English is not an applicant’s native language, they will need to demonstrate proficiency in English in order to study at the University. A minimum IELTS score of 6.5 overall with 6.0 in each sub-test, or equivalent is required. For a list of equivalencies, please check here.

Specific Entrance Requirements

Students who do not have C/4 GCSE mathematics or equivalent qualification are required to demonstrate equivalent level skills before entry. Admissions advisors can support students who wish to take this route.

Recognition of Prior Learning

Where a student wishes to apply for the recognition of prior learning on the basis of certificated or experiential learning, they should follow the University’s Recognition of Prior Learning and Credit Transfer Policy.

Learning Outcomes

By completion of the programme:

Knowledge and Understanding

A student will be able to: 

Subject Specific Skills

A student will be able to: 

Transferable and Employment Skills

A student will be able to:

All of the above learning outcomes are mapped to the relevant QAA Subject Benchmark threshold statements, see Appendix A.

For a mapping of courses to learning outcomes, see Appendix B.

For the Exit Awards, see Appendix D.

Teaching and Learning

Overview

The University aims to provide a lively, open, active, and authentic teaching and learning environment, in which students have the opportunity to connect their studies with wider interests and applications, and in which research and teaching are complementary.

An inclusive and interactive approach enables focus on the individual student, prompts and encourages independent reading and research, and hones their ability to apply their knowledge and skills in new contexts. This provides students with opportunities to develop and demonstrate their discipline expertise in a variety of contexts, enabling them to enhance their subject specific and transferable skills. Teaching is flexible and adaptive to respond to student needs and classroom dynamics.

Approaches to increase inclusivity and experiential learning in the classroom might include:

• Flipped classroom: study materials and formal lecture content is delivered to students outside of the contact hours. This enables the classroom time to focus on a discussion of key concepts and themes, for students to ask targeted questions to enhance their understanding, and for interactive group activities to share and widen knowledge and understanding. This might include small groups giving mini-presentations, or proposing a solution to a problem.
• Role play/simulations: students are given scenarios/briefs in advance of the session, and possibly a specific role to play in the activity. The tutor guides the process by establishing context, releasing new material to students in the course of the activity, and providing space for reflection on the outcomes of the activity, and on the theories and concepts discussed and tested during the activity.
• Tutor – Student co-creation: this is similar to the flipped classroom model, but relies on a stronger degree of student input at the design stage of the learning activity. The course leader still has control of overall content and direction of the course. However, weekly focus and case studies to apply and evaluate theories and concepts can be agreed collectively.

The portfolio of teaching, learning, and assessment elements is designed to embrace the University’s Teaching and Learning and Assessment Strategies and provide a diverse range of teaching and assessment methods, tasks, and tools.

Since the programme supports each student (in conversation with an Academic Advisor) to take a personal elective pathway through their studies, the range of teaching and learning activities and assessment types will vary student by student. A student who wants to go into postgraduate study in their main degree discipline, for example, may elect to take more Directed Study (1:1, 2:1, or small group) courses in their main discipline than a student with other interests or goals.

The teaching and learning for the programme is designed to progress steadily over three years and develop students’ conceptual sophistication and powers of application, through cumulative knowledge and experience.

The third-year culminating project or dissertation enables the student to refine their independent research and communication skills and to synthesise and develop their studies with a supervisor.

Teaching and Learning Activities

The teaching and learning activities include:

• Lectures, seminars, labs, and workshops
• Directed study (1:1, 2:1, or small group teaching on specific topics)
• Informal discussion (including on online discussion boards and in regular faculty ‘office hours’; the latter are opportunities for students to drop in or sign up to explore ideas, raise questions, or seek targeted guidance or feedback, individually or in small groups)
• Formative and summative assessment tasks
• Independent study and research

Assessment

A dedicated Assessment Strategy supports authentic, inclusive, and experiential assessment. This includes offering students a broad range of assessment types, which support active learning. The assessment types available to students are listed in the University Assessment Strategy. Whilst it is not expected that all programmes should offer all of these different assessment types, and choice of assessment should be based on the most effective and appropriate way to test student learning, there will be a range available which empowers students to demonstrate their discipline knowledge via diverse means.

Courses at the University are assessed formatively and summatively in a variety of ways, including:

• Written Assignment (e.g., long-form coursework essay; study report; literature review; reflective essay; dissertation)
• Examination (e.g., open book scheduled exam; closed book; 24-/48-hour exam)
• Presentation (e.g., oral presentation with accompanying slides; Viva voce)
• Role Play (e.g., Moot; consultancy simulation; “code walks”)
• Practical ( e.g., lab skills assessments)
• Artefact (e.g., software artefact with an accompanying report)
• Portfolio (i.e., students only have one final, formal deadline, but what they submit includes a series of shorter pieces created and reflected upon and revised across the course)
• Set Exercises (e.g., a series of short set exercises distributed across the course)

Feedback on formative and summative assessment tasks is provided in verbal or written forms.

Note about set exercises. In computer and data science courses, we advocate continuous summative assessments. Set exercises consist of a series of short exercise sheets, distributed across the duration of the course. The way they are distributed depends on the course, and is set by the Course Leader by the start of the course (for example, weekly, or bi-weekly). Each exercise sheet has a firm summative deadline. Students must submit their solutions for a given exercise sheet by a given deadline. The overall grade of the set exercises is the aggregate mark of its constituent exercise sheets. If students submit any exercise sheet late, without approved extenuating circumstances, penalties will be applied to the mark for that sheet

Appendix C contains the programme structure and assessment summary.

The University’s Assessment Strategy can be found here.

The University’s Assessment Regulations for Taught Awards can be found here.

Teaching and Learning Environment

The teaching and learning environment includes:

• The University’s campus has state-of-the-art teaching rooms and independent and group study spaces.
• A Virtual Learning Environment (VLE) for each course with a syllabus and range of additional resources (e.g. readings, question prompts, tasks, assessment briefs, slides or handouts, discussion boards, and sample examination papers and examiners’ reports) to orientate and engage students in their studies.
• Northeastern University’s online library digital resources, and other online academic resources, such as JSTOR and the OED. Students are inducted on their use at the start of the programme, and wider digital literacy is reinforced and developed across their studies.
• Students can also apply for a reader’s card to use the British Library membership and apply for membership of any of the City of London libraries. Students at the University can apply for Senate House Library membership.
• As part of the wider teaching and learning environment, the University hosts a range of academic and social events in which students, faculty, alumni and interlocutors from outside the academy are brought together.

Research

The University is an active research and knowledge exchange community. Its programmes are designed by faculty with relevant research expertise and teaching is allocated to faculty as far as possible to align with their research expertise and interests.

All students have the opportunity to develop their research skills as they progress through the programme, culminating with the written assignment in their final year, when their supervisor will be on hand to provide bespoke support.

Students are invited to a range of faculty research events and, where possible and from time to time, research assistance opportunities may be made available.

Student Support and Development

Disabilities and/or Specific Learning Difficulties (SpLDs)

Students are strongly encouraged to inform the University of any medical conditions, disabilities, specific learning difficulties (SpLD) or neurological differences as soon as is practical. Students will be asked to submit supporting documentation from a doctor, clinical or educational psychologist detailing the nature of their disability and the impact it is likely to have on their studies in order to help us put in place appropriate support and accommodations. More information can be found in the Student Disability Policy. This data is managed and securely stored by Student Support and Development (SSD). At the start of the academic year, a number of talks and events are held which are designed to support and inform students with regard to mental health, disabilities, safety and learning support.

SSD meets with students as soon as possible, and preferably before the start of the academic year, to discuss their needs and draft a Learning Support Plan (LSP) which outlines the support to be provided both within the University (if appropriate) and externally. If requested by the student, the SDD will then arrange to inform relevant faculty of the student’s needs and any reasonable adjustments required. 

If a student is undiagnosed but believes they may have a SpLDS (e.g., Dyslexia), SDD will help them to access diagnostic services. If the assessment confirms a SpLDS, SDD will work with the student to prepare an LSP and will provide advice about accessing additional funding and support through the Disabled Students Allowance, where a student may be eligible.

For more information, please click here.

Employability and Careers Guidance

The core competencies embedded within this credit-bearing degree programme are designed to prepare students for public citizenship, professional success, and personal flourishing.

The University’s employability and career opportunities have been designed in collaboration with a large number of experts from inside and outside academia, to develop the attitudes, behaviours and capabilities that will prepare students for the world of work.

University Careers Advisors help students to identify their career goals and create individual career plans. Students are actively encouraged to seek internships, with guidance and support given throughout the application process.

Quality Assurance and Enhancement

Award Standards

Every programme of study is developed by the faculty, utilising their subject specialists and approved by the University’s Academic Board.

Review and Evaluation Mechanisms

The University has robust procedures, as described in AQF4 Programme and Course Approval and Modifications and AQF5 Annual Monitoring and Reporting, in place to assure the quality of the programme development, delivery, and management, alongside systematic monitoring, ongoing review and enhancement of all University programmes. Enhancements are made as necessary to ensure that systems remain effective and rigorous.

The University utilises constructive feedback from a variety of sources, internal and external, to inform its decision-making process to enhance the programme and the student experience. These feedback sources are:

• Annual Course Reviews, written by the Course Leader, are prepared to enable the Course Leader to reflect on the course, using a variety of data and student/faculty feedback to enhance the course and support the in writing of the Annual Programme Review.
• Annual Programme Reviews, written at the end of each academic year, are prepared in order to enhance individual programmes and to plan ahead.
• Annual External Examiner Reports are prepared by the External Examiners, as appointed by the University, to confirm that a programme has been assessed in accordance with the approved documentation and that the student performance meets the appropriate academic standards.
• Formal student feedback mechanisms consist of course questionnaires and Student-Staff Liaison Committee meetings at least once each semester, as well as annual student satisfaction surveys, including external independent surveys, such as the National Student Survey.

Informal student feedback is also valued by the University and this can take the form of students talking or corresponding with faculty or professional staff.

About This Document

Disclaimer

The University has checked the information provided in this Programme Specification and will aim to deliver this programme in keeping with this Programme Specification. However, changes to the programme may sometimes be required arising from annual monitoring, student feedback, and the review and update of courses and programmes. Where this activity leads to significant changes to courses and programmes there will be prior consultation with students and others, wherever possible, and the University will take all reasonable steps to minimise disruption to students. It is also possible that the University may not be able to offer a course or programme for reasons outside of its control, for example, due to the absence of a member of staff or low student registration numbers. Where this is the case, the University will aim to inform applicants and students as soon as possible, and where appropriate, will facilitate the transfer of affected students to another suitable programme.

Copyright

The contents of this Programme Specification are the copyright of the University and all rights are reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form or by any means, such as electronic, mechanical, photocopied, recorded or otherwise, without the prior consent of the University.

Appendix A: Map Learning Outcomes to QAA subject benchmark statement

** This is intended to mean that all students (taken over all years) graduating with an honours degree in this discipline will have achieved this.

Appendix B – Map of Courses to Learning Outcomes

Knowledge and Understanding

Subject Specific Skills

Transferable and Professional Skills

NB: Electives are typically mapped to the programme learning outcomes through the Transferable Skills.

Appendix C – Required Course Summative Assessment Summary

Course Type:  

R = Required or O = Optional

Assessment Weighting:

AE1 = Assessment Element 1; AE2 = Assessment Element 2; AE3 = Assessment Element 3

Appendix D – Exit Awards

Certificate in Higher Education:

In order for a student to be awarded a Certificate in Higher Education (Cert HE), they are required to have achieved 120 Level 4 Credits, in accordance with the University’s Academic Regulations for Taught Awards.

Knowledge and Understanding

A student will be able to: 

Subject Specific Skills

A student will be able to: 

Transferable and Employability Skills

A student will be able to: