Name of Programme
MSc Applied Data Science
Final Award
Awarding Institution/Body
University Of Buckingham
Teaching Institution
University Of Buckingham
School of Study
School of Computing
Programme Code(s)
PMSF1PAD / Full Time / 1 Year
Professional Body Accreditation
Relevant Subject Benchmark Statement (SBS)
Computing (2022)
Admission Criteria
2:1 BSc (Hons) Computing, Engineering, Physics or Mathematics
Fundamental programming skills
Applicable Cohort(s)
January 2023
FHEQ Level
Summary of Programme
This programme is a specialist master’s programme for first degree holders in computing related majors such as computer science, computer engineering or software engineering. The programme consists of 3 common core modules, 3 specialised core modules, a leadership and innovation module, a work placement module, and an individual project. The programme strikes a balance between theory and practical skills, emphasising on technical know-how, innovation and application.
Educational Aims of the Programme
As the consequence of computer automation and extensive use of the internet, modern information age has produced huge amount of data known as big data. Such data implicitly contain a rich collection of useful knowledge patterns that describe, summarise and interpret human behaviours of various kinds. As the processing power of modern computers increases, there is an urgent need to process and digest the mountains of data in order to discover useful hidden information patterns that can benefit the society as a whole. Data Science has become a new and important discipline of science that has a wide range of applications. As data science being practised more extensively, the market demands for qualified graduates with specialised knowledge and skills in the field are fast increasing. However, bachelor degree qualifications can only prepare graduates to the entry level requirement for data science.
This programme aims to teach qualified first degree holders with advanced knowledge and understanding in data science, data mining and machine learning down-streamed from the strong and continuing research by the Department in this field. Through studying this programme, students will understand the concepts and issues faced by data science in various applications, study related theories, rigorous principles and methodologies, advanced techniques and algorithms, appreciate issues regarding big data platforms and systems, as well as the application of the technology. The students will also gain a wide range of practical skills in data science as well as transferrable skills relevant to Computing and IT.
The graduates of the programme are expected to play a leading role in data science projects and be able to compete in the specialised data science job market. The programme also builds a strong foundation for those students who want to pursue higher degrees by research in data science related areas.
Programme Outcomes

Knowledge and Understanding

At the end of the programme students should be able to gain knowledge and understanding in:
1. The roles that data science plays in the modern society and in a business strategy context
2. Theories, principles and methodologies for data science
3. A range of specialised modern computing techniques in data analysis, data mining and machine learning with relevant skills to apply the techniques effectively in practice
4. Awareness of the state-of-art technological development for data science in big data, data mining, and machine learning
5. Critical evaluation of existing and new solutions as well as own work in data science applications
6. Independently and collaboratively solving problems of complex nature from various areas of application.

Teaching/Learning Strategy

The ILOs are achieved through a mixture of lectures, workshops, seminars, tutorial classes and practical classes. The academic maturity in self-reliant individual learning in terms of extensive reading and practising outside the classes is expected. The following strategies are used to meet each itemised ILO:
1. Professional Development seminars, work placement, individual project
2. Lectures, tutorials and coursework
3. Lectures, tutorials and practical exercises
4. Lectures, individual project, dissertation, coursework, and work placement, research seminars
5. Individual project, coursework and group work
6. Individual project, work placement, group work

Assessment Strategy

Assessment of the ILOs is through the following means where numbers in the brackets refer to the ILO items:
● Written exams (1, 2, 3, 4)
● Coursework (1, 2, 3, 4, 5, 6)
● Project reports (1, 2, 3, 4, 5, 6)
● Project presentation (1, 2, 3, 4, 5, 6)
● Project software (1, 2, 4, 5, 6)
● Project viva (1, 2, 3, 4, 5, 6)
Programme Outcomes

Cognitive Skills

At the end of the programme students should be able to gain:
1. An understanding and appreciation of scientific approach to data science and its relevance to society and everyday life
2. Data comprehension and analytics through knowledge and understanding gained from the programme
3. Independent and collaborative problem solving by applying the knowledge and understanding of concepts, theories, methodologies and techniques gained from the programme
4. Critical analysis and evaluation of solutions and software tools through an understanding of their strengths and limitations, their suitability in problem solving, and any trade-off issues
5. Model and solution testing through use of recognised and appropriate criteria and rigorous procedures and draw objective conclusions
6. Developing understanding and appreciation of professional issues in relation to proper use of data science technology and related GDPR guidelines in the UK

Teaching/Learning Strategy

All the skills listed are obtained through a mixture of practical exercises, tutorial discussions, coursework attempts, individual project work and work placement experience. In particular, the following are directly useful:
● Research Methods
● Coursework/module projects
● Individual project
● Work placements

Assessment Strategy

All the cognitive skills listed are assessed by the following means and shown through the work submitted:
▪ Coursework
▪ Practical examinations & tests
▪ Project reports
▪ Project viva
▪ Professional development portfolios
Programme Outcomes

Practical/Transferable Skills

Subject Related Practical Skills:
At the end of the programme students should be able to:
1. Technical skills in specifying, constructing, testing and evaluating data science solutions in terms of quality attributes and possible trade-offs inside the problem domain;
2. Mathematical and statistical skills in data understanding and analysis
3. Software practical use and selection skills
4. Programming and fast prototyping as well as test scripting skills
5. Project and time management skills
6. The ability to critically evaluate and analyse complex problems, including those with incomplete information, and devise appropriate solutions, within certain constraints.

Transferable Skills:
At the end of the programme students should be able to enhance the following general skills gained from previous experience, develop and transfer any new ones to their future employment:
1. Intellectual skills in critical thinking, information literacy, putting forward a sound argument
2. Research skills such as collecting, selecting, analysing and documenting literature regarding relevance and recency
3. Autonomy and independence in self-guided learning, self-management, reflection and dealing with deadlines
4. Communication skills in conversing ideas to people of various backgrounds effectively, and being able to convince others
5. Teamwork in tackling problems of complex natures, being able to compromise and negotiating acceptable conclusions
6. Contextual awareness of the needs of individual and community, the working environments of business organisations, opportunities and challenges created by computer based solutions.

Teaching/Learning Strategy

The skills are obtained through practice in
▪ Tutorial classes (1, 4)
▪ Coursework (1, 2, 3, 4, 5)
▪ Lectures, tutorials, and practical classes (3)
▪ Individual project (1, 2, 3, 4, 6)
▪ Group module projects (2, 3, 4, 5, 6)
▪ Work placement (1, 2, 3, 4, 6)

Assessment Strategy

The key skills are assessed by the following means where numbers in the brackets refer to the corresponding skills:
▪ Coursework demonstrations (all)
▪ Examinations (all)
▪ Individual project (1, 2,,3, 4, 6)
▪ Written essays and reports (all)
▪ Oral presentations (4)
▪ Demonstration performance (3, 4, 5)
▪ Group project demonstrations (5)
External Reference Points
● QAA Framework for Higher Education Qualifications of UK Degrees

● Relevant QAA Subject Benchmark Statements:
Please note: This specification provides a concise summary of the main features of the programme and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if he/she takes full advantage of the learning opportunities that are provided. More detailed information on the learning outcomes, content and teaching, learning and assessment methods of each course unit/module can be found in the departmental or programme handbook. The accuracy of the information contained in this document is reviewed annually by the University of Buckingham and may be checked by the Quality Assurance Agency.
Date of Production
Summer 2020
Date approved by School Learning and Teaching Committee
Latest Revision Date: November 2023
Date approved by School Board of Study
Latest Revision Date: November 2023
Date approved by University Learning and Teaching Committee
Latest Revision Date: November 2023
Date of Annual Review
In line with the University annual monitoring review process



MSc Applied Data Science

PMSF1PAD / Full Time / January Entry
Term 1
Research Methods [L7/15U] (SPFRMET)
Mathematics and Statistics for Data Analysis [L7/15U] (SPFMSDA)
Scripting for Data Analysis [L7/15U] (SPFSCDA)
Term 2
Data Exploration and Visualisation [L7/15U] (SPFDEAV)
Applied Techniques of Data Mining and Machine Learning [L7/15U] (SPFDMML)
Individual Project Applied Data Science [L7/60U] (SPFINPR) **
June Examination
Term 3
One of:
Work Placement [L7/15U]
Work-based Dissertation [L7/15U] (APCXXXXX31) ***
Systems and Tools for Data Science [L7/15U] (SPFSTDS)
Individual Project Applied Data Science [L7/60U] (SPFINPR) **
Term 4
Leadership and Innovation in Data Science [L7/15U] (SPFLIDS) *
December Examination

** Please note there are Special Regulations governing this programme, which can be reviewed in the University of Buckingham’s regulations Handbook:
*** Work Placement is offered on the basis of available opportunity. Students are required to look for work placement opportunities themselves. In the extreme rare cases where work placement opportunities cannot be located, the students are required to complete a 15-unit dissertation on a certain aspect of data science (See the module spec for Dissertation for more details). Except Work Placement and Dissertation as selective modules, all other modules are compulsory for the programme of study.
* Leadership and Innovation in Data Science is a reading module that is closely associated with the Leadership and Innovations in Data Science Seminar Series that runs throughout the programme (See syllabus for details).