Course specification and structure
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UDDATSCI - BSc (Hons) Data Science

Course Specification

Validation status Validated
Highest award Bachelor of Science Level Honours
Possible interim awards Bachelor of Science, Diploma of Higher Education, Certificate of Higher Education, Bachelor of Science
Total credits for course 360
Awarding institution London Metropolitan University
Teaching institutions London Metropolitan University
School School of Computing and Digital Media
Subject Area Computer Science and Applied Computing
Attendance options
Option Minimum duration Maximum duration
Part-time 4 YEARS 6 YEARS
Full-time 3 YEARS  
Course leader  

About the course and its strategy towards teaching and learning and towards blended learning/e-learning

The demand for Data and Information has tripled over the last five years. There is an increasing demand for Data Science skills in the job market, and employers do not find enough graduates to fill their positions. The BSc Data Science is specifically designed for those who wish to study Big Data Engineering, Analytics Engineering, and Data Visualisation to enable completely new generations of technology solutions that have become the key resources in a wide range of industries.

The course fosters pedagogical practice by using a wide range of tools and development platforms that engage students in the latest products and solutions. Students are encouraged to critically evaluate different data model solutions and statistical techniques in a variety of contexts. These activities provide students’ understanding and learning the required skills and knowledge for sector demand.

During the early stages, students will be supported with a focus on an introduction to support facilities, including Academic Mentor, Success coaches, Course Leader, and Academic tutor and Course Weblearn sites. This support mechanism will facilitate transition and progression through the levels. The students will also receive early scheduled sessions with the Subject Librarian, which will continue as they progress through the degree.

The teaching strategy is coupled with a theoretical approach with practical based learning providing a varied simulating experience. Assessments at different levels are aimed to introduce and develop key skills integral to academic success at higher levels, including writing, presentation, research, analytical, mathematical and technical skills.

Students are given one-on-one training for assessment submission in early levels by providing opportunities to develop good academic practice. Weekly tutorials, workshop exercises, and formative assessments are designed to enhance their learning with applied differentiation techniques.

Module assessment typically consists of a combination of assessment types, including coursework, in-class tests, and unseen exams. Coursework can include solution modelling such as Data models, a Data Program code in addition to a written report/essay. The volume, timing, and nature of assessment enable students to demonstrate the extent to which they have achieved the intended learning outcomes.

Formative feedback is provided during workshops and tutorial sessions. The support is personalised, stretches students' skills and understanding of subject matters, and promotes student engagement.

The Summative feedback often recorded electronically or on paper, is provided for coursework and other summative pieces of work, giving students opportunities to improve. This process enables students to develop independent or group/peer learning strategies identifying by their strengths and weaknesses.

Learning technologies such as the university’s virtual learning platform Weblearn, online delivery tools such as Collaborate Ultra and Microsoft Teams, Library e-books and Online Databases are used to facilitate student learning. These tools are specifically used to deliver content, active learning, online engagement, to provide formative, summative assessment, and feedback.

The BSc Data Science course meets the needs of the student demographic in North London and enables students to integrate studying with work and life commitments by enabling job opportunities such as junior Data Engineers, junior Applied Data Scientist, Decision Scientist, and Data Analysts. The BSc Data Science course and subject cluster demonstrate an improvement of students' outcomes by promoting the Data Science skills and practice

Course aims

  1. The main aim of this course is to equip students with core academic and technical skills in problem solving to proceed through higher education in an appropriate framework onto the relevant employment in the Data Science industry.
  2. To provide education in the designing, building, analysing of data science solution to improve intellectual skills and use of software that will equip students’ practical skills.
  3. To provide a range of tools and up-to-date big data platforms to apply maths, statistics and science practice, recognise and exploit business opportunities, find a solution to domain-specific problems using data science capability.
  4. To provide graduates with data science-related, cognitive, practical and transferable skills by preparing them for advanced postgraduate courses of study and for employment as a Data Scientist.
  5. To further develop intellectual skills of reasoning, problem solving, decision-making, self-expression, and independent study, thereby enabling students to build scalable data products for strategic or operational business.

Course learning outcomes

ULO Demonstrate confidence, resilience, ambition and creativity and will act as inclusive, collaborative and socially responsible practitioners/professional in their discipline.
LO1. Demonstrate solid understanding and fundamental knowledge of data preparation, designing, building, analysis, and visualisation techniques.

LO2. Demonstrate understanding of mathematical and statistical methods and techniques associated with data science.

LO3. Apply analytical and design techniques to the problems by selection and application of analytical software tools to meet the challenges of small and large data sets.

LO4. Find solutions to domain-specific problems using data science capability, use a range of coding practice, build scalable data products for strategic or operational business, and contribute through the product life cycle.

LO5. Demonstrate the ability to apply, test, and evaluate artificial intelligence and machine learning techniques.

LO6. Demonstrate an awareness of the importance of legal, social, ethical and professional issues underpinning the IT Data discipline.

LO7. Research, plan, structure and deliver an academic report and presentation with self-evaluation individually or as a member of a team.

LO8. Demonstrate an understanding of the personal qualities, skills, and qualifications needed for employment in a range of roles and organisations.

Principle QAA benchmark statements

Assessment strategy

Students are provided with opportunities to develop an understanding of, and the necessary skills to demonstrate, good academic practice. Particularly, students will be encouraged to complete weekly tutorial and workshop exercises as well as periodic formative diagnostic tests to enhance their learning. During tutorial and workshop sessions students will receive ongoing support and feedback on their work to promote engagement and provide the basis for tackling the summative assessments.
A range of assessment methods is employed throughout the course. Module assessment typically consists of a combination of assessment instruments including.

  • courseworks
  • in-class tests
  • exams

Coursework can include an artifact such as an output of dataset analysis, application of algorithms, data trends or program code in addition to a written report/essay. The volume, timing and nature of assessment enable students to demonstrate the extent to which they have achieved the intended learning outcomes.
Formative and summative feedback will be provided using a variety of methods and approaches, such as learning technologies, one to one and group presentation of the submitted work, at various points throughout the teaching period and will adhere to University policy regarding the timing of feedback.

Organised work experience, work based learning, sandwich year or year abroad

A Work-Related Learning single-semester module is available at Level 6. Work experience and work placements are facilitated via the London Met employability team. Medium and small size companies provide placement opportunities and real-client briefs for students to work onsite or remotely as part of a team. This opportunity gives students, real work experience during their degree, apply theory in to practise, industry links, work continuation opportunities, and job market readiness.

London Met’s Accelerator is a business incubator for small and new business where students get opportunities to work alongside technical experts to foster existing business or new ideas.

Course specific regulations

British Computer Society (BCS) accreditation is awarded according to the following additional course regulation:

The project must be passed in order for a student to obtain BSc (Hons) Data Science.

The project must be passed without compensation.

Modules required for interim awards

Level 4 - Certificate level

Introduction to information Systems
Fundamentals of Computing
Logic and Mathematical Techniques
Data Analysis and Financial Maths

Level 5 - Diploma Level

Certificate level modules and
Professional Issues Ethics and Computer Law
Smart Data Discovery
Data Engineering
Programming with Data
Data Analytics
Statistical Methods and Modelling Markets

Level 6 - Degree Level

Diploma Level modules and
Artificial Intelligence and Machine Learning
Big Data and Visualisation
Work-Related Learning
Advanced Database Management Systems - optional
Financial Modelling and Forecasting - optional
Formal Specification and software Implementation - optional
Cryptography and Number Theory - optional
Academic independent Study - optional
Ethical Hacking - optional
Artificial Intelligence - optional

Arrangements for promoting reflective learning and personal development

Learning and assessment activities are designed to promote self-reflection of the learning outcomes in many undergraduate modules including Projects, Databases, and Programming. Students gain opportunities to write about the journey of their assessment process, difficulties, milestone achievements, future development, and actions that the links to personal goal setting.

Group collaboration and reflective writing are highly promoted in the Work-Related Learning module where students are expected to write weekly journal entries of their technical and non-technical work, personal development plans including communication skills, problem-solving skills, and reflective skills.

The module Professional Issues, Ethics, and Computer Law enables the reflection and group discussion of ethical behaviour in the IT profession on real-world case-based scenarios.

Other external links providing expertise and experience


Career, employability and opportunities for continuing professional development

The university employability service offers support and services related to employability skills such as CV writing, interview practice, teamwork, communication, etc. The enrichment week is specially designed to give students opportunities to gain knowledge from industry experts and speakers.

Job fairs are organised throughout the academic year, giving students opportunities to interact with potential employers, paying the way for work opportunities while students are studying the degree course. The graduate pack includes career guidance and personalised support services to attain employment even three years after graduation.

The Cyber Security Centre works with many national and international companies (Lloyds Bank, Oracle) to design security solutions that give students opportunities to link with industry professionals and get involved in the latest projects by applying theory into practice.

Graduates gain employment in the financial and technology industries, public and private service sectors. A number of examples are shown below:

Data Scientist - HSBC
Applied Data Scientist - Airbus
Data Analyst - Amazon Web Services
Data Engineer - Mastercard
Data Science Operations - Spotify
Decision Scientist - Facebook
Junior/associate Data Scientist - BBC

Graduates can also pursue careers in research and development in scientific areas of Computer and/or Data Science.

Career opportunities

This course will prepare you to work in the field of data analytics, data programming, data visualisation, IT data consultation, big data solution designing or data solution development.

This degree award can put you in a position to apply to companies such as Facebook, Mastercard, Amazon, Microsoft or the BBC for roles such as Junior Data Scientist, Data Science Operational Officer or Associate Data Analyst.

This course is also excellent preparation for further study or research.

Entry requirements

In addition to the University's standard entry requirements, you should have:

  • a minimum grade C in three A levels (or a minimum of 96 UCAS points from an equivalent Level 3 qualification, eg BTEC Level 3 Extended Diploma, Advanced Diploma, Progression Diploma or Access to Higher Education Diploma of 60 Credits)
  • English language and Mathematics GCSEs at grade C/4 or above (or equivalent)

Applicants with relevant professional qualifications or extensive professional experience will also be considered.

If you don’t have traditional qualifications or can’t meet the entry requirements for this undergraduate degree, you may still be able to gain entry to the four-year Data Science (including foundation year) BSc programme.

Official use and codes

Approved to run from 2021/22 Specification version 1 Specification status Validated
Original validation date 30 Oct 2020 Last validation date 30 Oct 2020  
JACS codes 100366 (computer science): 50% , 100358 (applied computing): 50%
Route code DATSCI

Course Structure

Stage 1 Level 04 September start Offered

Code Module title Info Type Credits Location Period Day Time
CC4057 Introduction to Information Systems Core 15 NORTH AUT TUE PM
CS4001 Programming Core 30 NORTH AUT+SPR TUE AM
CS4051 Fundamentals of Computing Core 15 NORTH SPR TUE PM
MA4005 Logic and Mathematical Techniques Core 30 NORTH AUT+SPR THU AM
MA4041 Data Analysis and Financial Mathematics Core 30 NORTH AUT+SPR THU PM

Stage 2 Level 05 September start Offered

Code Module title Info Type Credits Location Period Day Time
CC5051 Databases Core 15 NORTH AUT WED AM
CC5062 Data Engineering Core 15 NORTH SPR TUE AM
CC5063 Data Analytics Core 15 NORTH AUT TUE AM
CC5064 Programming with Data Core 15 NORTH AUT THU AM
CC5067 Smart Data Discovery Core 15 NORTH SPR FRI AM
CS5052 Professional Issues, Ethics and Computer Law Core 15 NORTH SPR THU PM
MA5041 Statistical Methods and Modelling Markets Core 30 NORTH AUT+SPR FRI PM

Stage 3 Level 06 Not currently offered

Code Module title Info Type Credits Location Period Day Time
CC6058 Big Data and Visualisation Core 15        
CS6053 Artificial Intelligence and Machine Learning Core 15        
CS6P05 Project Core 30        
CC6001 Advanced Database Systems Development Option 30        
CC6051 Ethical Hacking Option 15        
CS6001 Formal Specification & Software Implementation Option 30        
CU6051 Artificial Intelligence Option 15        
FC6W51 Work Related Learning II Option 15        
MA6041 Financial Modelling and Forecasting Option 30        
MA6054 Cryptography and Number Theory Option 15        
MA6P52 Academic Independent Study Option 15