MSc Advanced Computer Science with Data Science (January start)
ApplyKey facts
- Start date: January
- Study mode and duration: 12 months full-time
Study with us
- gain skills to meet the challenges posed by the advent of the data revolution
- understand how classical statistical techniques are applied in modern data analysis
- work on a research project with our industrial partners
Prefer to start your course in September? We also offer our MSc Advanced Computer Science with Data Science with a September start date.
Why this course?
Our MSc Advanced Computer Science with Data Science (January start) gives you the skill set needed in a range of industries. You'll gain an understanding of the new challenges posed by the data revolution, particularly in relation to its modelling, storage and access.
Data scientists are in high demand across a number of sectors, as businesses require people with the right combination of technical, analytical and communication skills. (Prospects, 2022).
Opportunities for graduates exist in a range of industries such as finance, films and games, pharmaceuticals, health care, consumer products and public services. Our MSc Advanced Computer Science with Data Science (January start) gives you the skill set needed.
You’ll also have the opportunity to participate in recruitment events and meet industry employers such as JP Morgan, Adimo, JWF and more.
What you’ll study
You'll choose leading classes that span the breadth of both computer and information sciences, including:
- machine learning
- deep learning
- data wrangling
- legal and ethical aspects of data science
- software engineering
- use of contemporary tools and packages
You'll understand key algorithms and techniques embodied within data analytics solutions and be exposed to a number of different data technologies and techniques. You’ll see how they can achieve efficiency and scalability, while also addressing design trade-offs and their impacts.
You'll learn key technologies that are at the heart of data analytics such as Keras and Tensorflow. You'll also be equipped with a sound understanding of the principles of machine learning and deep learning and a range of popular approaches, along with the knowledge of how and when to apply these.
On this Masters course you’ll also have the opportunity to implement and experiment with these machine learning algorithms using the most popular languages such as R and Python, explore their applications to areas as diverse as analysing activity-related data captured using a smartphone to financial time-series prediction.
Throughout the course we offer opportunities to engage with industry through IT careers fairs, guest lectures from local industry, industrially sponsored projects, specialist sessions from our University Careers Service and student demo days to industry.
Research project
Our project supervision programme ensures a named academic is attached to every MSc student during their project study between May and August each year. Regular meetings are scheduled during the period of the project work, and where an external organisation is involved, meetings between supervisor, student and external body are standard. We have extensive experience of supporting students in placement and thus are well equipped and experienced to manage both university and placement provider expectations.
Facilities
The course will involve some extensive laboratory-based instruction and student work. The Department of Computer and Information Science utilise their own specialist laboratories to provide practical student tuition on large scale advanced applications.
The University library also has a sufficient body of resources to support this course.
Course content
In addition to the compulsory 80 credits, you're required to select 40 credits from the elective classes. Please note, elective classes are subject to change.
Compulsory
Research Methods (10 credits)
This module aims to provide you with an understanding of both quantitative and qualitative research processes and associated techniques, including the effective presentation of findings in accordance with the best principles of scholarship.
Distributed Information Systems (20 credits)
This module will give you an extended understanding of the deep, technical issues underlying information systems, in the particular context of distributing content over the world-wide web.
Machine Learning for Data Analytics (20 credits)
The aim of this module is to equip you with a sound understanding of the principles of machine learning and a range of popular approaches, along with the knowledge of how and when to apply the techniques. The module balances a solid theoretical knowledge of the techniques with practical application via Python (and associated libraries) and students are expected to be familiar with the language. Aspects of the course will be highly mathematical and technical requiring strong math and programming ability (Python and Tensorflow).
Electives
Mobile Software & Applications (20 credits)
By the end of the module you should be able to:
- appreciate and explain the problems associated with mobile software environments
- identify and explain the models and techniques typically employed in the design and development of a range of software for mobile environments, and appreciate the limitations of these
- appreciate the role and impact of context-awareness and persuasion in modern mobile applications
- demonstrate the ability to implement selections from a range of the software typically used in mobile environments
Evolutionary Computing for Finance (20 credits)
On completion of this module you will:
- gain an understanding of a range of evolutionary computational and machine learning techniques
- gain an understanding of the relative advantages and disadvantages of each technique for different financial applications
- be able to evaluate the results of a financial problem investigated using evolutionary computation and machine learning techniques
Business Analysis (10 credits)
This module aims to provide tools and techniques for the effective analysis and design of business information systems and enable you to develop an understanding of their respective advantages, disadvantages and applicability.
Research Project (60 credits)
Over the summer semester, from May until August, you'll undertake a significant piece of work that will be your MSc project. This is typically a very practical activity, and can include:
- analysing a problem and designing, implementing and evaluating a solution
- conducting an in-depth experimental analysis of technique or technology
- performing an analysis of a large data set, and building and evaluating models of the data
We'll also have a range of projects available that are sponsored by our staff based on their latest research, and many that come direct from the employers we work with, representing real-world problems they are trying to solve.
Compulsory
Legal, Ethical and Professional Issues for the Information Society (10 credits)
This module aims to ensure that you're aware of the legal, social, ethical and professional issues commensurate with the practice of Information Systems Engineering.
Deep Learning Theory & Practices (20 credits)
By the end of the module you should be able to:
- understand the challenges in the training of deep neural network and how to overcome the challenges with few shot learning techniques
- understand how to process sequential data with deep neural network and the applications in natural language processing
- develop an understanding of the potentials and generalisation of deep neural networks through theoretical analysis
- understand deep generative models and their main approaches
- understand how to build deep neural networks in Python using packages such as Keras/PyTorch and implement such networks
Electives
Advanced Topics in Software Engineering (20 credits)
This class aims to:
- make you aware of key aspects of current software engineering research
- familiarise you with the state-of-the-art in terms of what problems can be solved and what are the current exciting challenges
- develop the necessary skills in you to allow them to contribute to the software engineering research community
- equip you with the skills and background to appreciate the contributions to software engineering research across the full range of material presented at the key international conferences in the field
Information Retrieval (10 credits)
You'll learn to:
- critically examine a number of influential information-seeking models
- provide an understanding of research methodologies for studying human information behaviour
- examine important concepts, such as relevance, in the context of information seeking and retrieval
- examine how findings from information seeking theory and practise can inform the design of information access systems
- outline the theory and technology used to construct modern Information Retrieval and Access systems
- critically evaluate the assumptions behind the evaluation of Information Retrieval systems
Big Data Tools and Techniques (10 credits)
The objectives of the module are to:
- understand challenges posed by big data, as they refer to its modelling, storage, and access
- be familiar with a number of different big data technologies and techniques, in terms of efficiency and scalability while also addressing design trade-offs and their impacts
- be familiar with a number of different cloud NoSQL systems and their design and implementation, showing how they can achieve efficiency and scalability while also addressing design trade-offs and their impacts
- be familiar with the Map-Reduce programming paradigm
Learning & teaching
Our teaching and learning methods include lectures, tutorials, laboratory practicals and combinations of individual and group work. These will not only develop your expertise in computer science and data science, but also in communication, team-working and analytical skills which are all essential skills for your future career.
Class material will be placed on Myplace for students to gain access to in their own time. A personal computer (microcomputer, desktop computer, laptop computer, tablet) is required to access teaching materials and attending online lectures/meeting. The computer lab is also available for students to use.
You’ll have regular contact with our expert staff, many of whom have been nominated for, and won, teaching awards based on nominations by our students.
Assessment
Methods of assessment and learning: written examinations, formative and summative approaches are taken in different aspects of the course. Written group/individual reports, oral presentations and moderated peer assessment are all included in the course.
Entry requirements
| Academic requirements | Minimum second-class (2:2) Honours degree or international equivalent in computer science or another numerate discipline (for example, mathematics, physics or engineering). Some programming or database experience is normally required. Other disciplines who have significant programming experience should contact us to discuss applying for this course. |
|---|---|
| English language requirements | You must have an English language minimum score of IELTS 6.0 (with no component below 5.5). We offer comprehensive English language courses for students whose IELTS scores are below 6.0. Please see English Language Teaching for full details. As a university, we now accept many more English language tests other than IELTS for overseas applicants, for example, TOEFL and PTE Cambridge. View the full list of accepted English language tests here. |
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Fees & funding
All fees quoted are for full-time courses and per academic year unless stated otherwise.
Fees may be subject to updates to maintain accuracy. Tuition fees will be notified in your offer letter.
All fees are in £ sterling, unless otherwise stated, and may be subject to revision.
Annual revision of fees
Students on programmes of study of more than one year (or studying standalone modules) should be aware that tuition fees are revised annually and may increase in subsequent years of study. Annual increases will generally reflect UK inflation rates and increases to programme delivery costs.
| Scotland | £9,600 |
|---|---|
| England, Wales & Northern Ireland | £9,600 |
| International | £23,200 |
| Available scholarships | Take a look at our scholarships search for funding opportunities. |
| Additional costs | International students may have associated visa and immigration costs. Please see student visa guidance for more information. |
Please note: the fees shown are annual and may be subject to an increase each year. Find out more about fees.
How can I fund my course?
Scottish postgraduate students
Scottish postgraduate students may be able to apply for support from the Student Awards Agency Scotland (SAAS). The support is in the form of a tuition fee loan and for eligible students, a living cost loan. Find out more about the support and how to apply.
Don’t forget to check our scholarship search for more help with fees and funding.
Students coming from England
Students ordinarily resident in England may be to apply for postgraduate support from Student Finance England. The support is a loan of up to £10,280 which can be used for both tuition fees and living costs. Find out more about the support and how to apply.
Don’t forget to check our scholarship search for more help with fees and funding.
Students coming from Wales
Students ordinarily resident in Wales may be to apply for postgraduate support from Student Finance Wales. The support is a loan of up to £10,280 which can be used for both tuition fees and living costs. Find out more about the support and how to apply.
Don’t forget to check our scholarship search for more help with fees and funding.
Students coming from Northern Ireland
Postgraduate students who are ordinarily resident in Northern Ireland may be able to apply for support from Student Finance Northern Ireland. The support is a tuition fee loan of up to £5,500. Find out more about the support and how to apply.
Don’t forget to check our scholarship search for more help with fees and funding.
International students
We've a large range of scholarships available to help you fund your studies. Check our scholarship search for more help with fees and funding.
Glasgow is Scotland's biggest & most cosmopolitan city
Our campus is based right in the very heart of Glasgow. We're in the city centre, next to the Merchant City, both of which are great locations for sightseeing, shopping and socialising alongside your studies.
Life in Glasgow
Careers
There will be opportunities for you to meet industry employers and take part in recruitment events, in addition to taking advantage of a wealth of support offered by our award-winning careers service.
Opportunities for graduates of the MSc Advanced Computer Science with Data Science exist in various industries:
- finance
- films and games
- pharmaceuticals
- healthcare
- consumer products
- public services
Salaries*
| Career | Salary |
|---|---|
| Data scientist | Salaries for junior data scientists tend to start at around £25,000 to £30,000, rising to £40,000 depending on your experience. With a few years' experience you can expect to earn between £40,000 and £60,000. Lead and chief data scientists can earn upwards of £60,000, in some cases reaching more than £100,000. |
| Data analyst | Entry-level salaries for Data Analyst’s range between £23,000 and £25,000. Graduate schemes in data analysis and business intelligence at larger companies tend to offer a higher starting salary of £25,000 to £30,000. With a few years' experience, salaries can rise to between £30,000 and £35,000. Experienced, high-level and consulting jobs can command £60,000 or more. |
| Software engineer | Typical graduate software engineer salaries start from £18,000 a year. The average annual salary for a software engineer is between £25,000 and £50,000. At senior or management level, software engineers can earn £45,000 to £70,000 or more per annum. Bonus schemes may be available. |
*Information taken from Prospects (last accessed June 2022)
Apply
Applications for January 2025 entry will open soon. To apply for September 2024 entry, please visit MSc Advanced Computer Science with Data Science.
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