- Start date: September
- Study mode and duration: 12 months full-time
Funded places: 8 fully funded places for Scottish/EU students
Study with us
- gain skills to meet the challenges posed by the advent of the big data revolution
- understand how classical statistical techniques are applied in modern data analysis
- work on a research project with our industrial partners
- partial accreditation by the British Computer Society
Why this course?
Our MSc Advanced Computer Science with Big Data gives you the skill set needed in a range of industries. You'll gain an understanding of the new challenges posed by the big data revolution, particularly in relation to its modelling, storage and access.
Big data is of growing importance in businesses and society in general. A skills shortage in this area means companies are willing to pay high salaries for the right skill set (The Guardian Career Choices).
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 Big Data gives you the skill set needed.
You'll gain an understanding of the new challenges posed by the big data revolution, particularly in relation to its modelling, storage and access.
Interested in studying a postgraduate degree within Science?
The Faculty of Science Admissions team are holding a virtual drop-in session for the MSc programmes on Wednesday 15 July between 10am and 4pm.
If you wish to discuss anything relating to the application process, potential funding opportunities, etc please register using the link below.Register for event
What you’ll study
You'll choose leading classes that span the breadth of both computer and information sciences, including:
- theoretical computer science
- human-computer interaction
- information sciences
- software engineering
- machine learning
- big data technologies
You'll understand key algorithms and techniques embodied within data analytics solutions and be exposed to a number of different big 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 big data analytics such as:
- NoSQL databases
- Map-Reduce programming paradigm
You'll also be equipped 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 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.
This takes the form of an individual project on an approved topic, which allows you to pursue an area of specific interest, providing scope for original thought, research and presentation.
Learning & teaching
Teaching methods include lectures, tutorials and practical laboratories. Dissertation is by supervision.
You’ll also have the opportunity to meet industry employers and participate in recruitment events.
Legal, Ethical and Professional Issues for the Information Society (10)
This class aims to ensure that the student is aware of the legal, social, ethical and professional issues commensurate with the practice of Information Systems Engineering.
Research Methods (10)
This module aims to provide students 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.
Big Data Technologies (20)
The aim of the class is to:
- understand the fundamentals of Python to enable the use of various big data technologies
- understand how classical statistical techniques are applied in modern data analysis
- understand the potential application of data analysis tools for various problems and appreciate their limitations;
- 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, to enable students to write programs which can execute in massively parallel cloud-based infrastructures
Distributed Information Systems (20)
This class 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)
The aim of this class is to equip students 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 class 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).
Select 40 credits from the following:
Advanced Topics in Software Engineering (20)
This class aims to:
- make students aware of key aspects of current software engineering research
- familiarise students 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 students to allow them to contribute to the software engineering research community
- equip students 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
Mobile Software Applications (20)
You'll develop an understanding of the theories, paradigms, algorithms and architectures for building software applications to function in mobile computing environments.
Evolutionary Computing for Finance (20)
On completion of this class students 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
Minimum second-class honours degree or international equivalent in computer science or another numerate discipline (eg mathematics, physics, engineering).
|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 ELTD 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.
Pre-Masters preparation course
The Pre-Masters Programme is a preparation course held at the University of Strathclyde International Study Centre, for international students (non EU/UK) who do not meet the academic entry requirements for a Masters degree at University of Strathclyde. The Pre-Masters programme provides progression to a number of degree options.
Upon successful completion, you will be able to progress to this degree course at the University of Strathclyde.
We've a thriving international community with students coming here to study from over 100 countries across the world. Find out all you need to know about studying in Glasgow at Strathclyde and hear from students about their experiences.Visit our international students' section
Fees & funding
All fees quoted are for full-time courses and per academic year unless stated otherwise.
|Rest of UK|
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 and non-UK EU postgraduate students
Scottish and non-UK EU 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.
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.
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.
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.
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.
Data Lab Scholarships
13 fully-funded scholarships are available to new Scottish/ EU students joining the MSc Artificial Intelligence and Applications and MSc Advanced Computer Science with Big Data for the 2020/21 academic year.
These scholarships have been made possible with funding from the Data Lab, Scottish Funding Council and the European Social Fund.
The deadline for applications is 30th June 2020.Apply for scholarship
Opportunities for graduates of the MSc Advanced Computer Science with Big Data exist in various industries:
- films and games
- consumer products
- public services
Future career options include:
- big data analyst
- software engineer
- data scientist
- data consultant
Glasgow is Scotland's biggest & most cosmopolitan city
Our campus is based in the very heart of Glasgow, Scotland's largest city. National Geographic named Glasgow as one of its 'Best of the World' destinations, while Rough Guide readers have voted Glasgow the world’s friendliest city! And Time Out named Glasgow in the top ten best cities in the world - we couldn't agree more!
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.
Find out what some of our students think about studying in Glasgow!Find out all about life in Glasgow
Advanced Computer Science with Big Data
Start Date: Sep 2020
Mode of Delivery: full-time
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