About this course
Study an emerging discipline that aims to extract unique insights by extending the field of statistics to incorporate advances in computing, particularly in relation to large quantities of data.
- Study a ground-breaking curriculum linked to industry needs
- Use industry standard tools and methodologies
- Undertake an extended project using real world data science problems
- Learn from academics who are world-leading researchers
- Use the programme as a basis for PhD study
It is estimated that 90% of the world's data was created in the last two years and that each day, over 2.5 billion gigabytes of data is created. Between 2012 and 2017 the take up of big data analytics amongst large enterprises in the UK more than doubled from 14% to 29%. The number of big data specialist staff working in large firms is predicted to increase by 243% to approximately 69,000 employees.
The challenges in data science are multi-faceted and very complex in their nature, including handling the huge amount of data in incompatible legacy databases. By exploiting cloud computing services, data scientists are now relatively free from the constraints of hardware and can concentrate on applying their domain knowledge and experience to make sense of the data. They can apply advanced statistical techniques and machine learning algorithms to make informed decisions which can deliver a major impact on their organisations.
The programme comprises six core modules covering statistical and computing techniques plus an extended research project. It is designed to give you the knowledge to move into the world of work as a qualified Data Scientist or to carry out further research through a PhD or equivalent. It is delivered via a combination of lectures, tutorials and and hands-on computer laboratory sessions. A major component of the MSc programme is the research project module, which will give you the opportunity to work on a high-level original research topic, with guidance from an experienced researcher and supervisor.
MSc Data Science is available for study as either a 180 credit or 240 credit routeway.
The 180 credit version of the course is usually run over one full year with the taught modules in Semesters 1 and 2, and the independent research project in the summer. The 240 credit routeway is studied over two years: Semesters 1 and 2 in both years.
The new 240 credit route includes a Group Design Project which aims to apply data science to the solution of a real-world problem. This will:
- consolidate knowledge and skills learned during the first year, as well as develop new ones
- provide an opportunity to further use the tools and systems that data scientists and data engineers use, increasing your readiness for the job market
- equip you with the transferrable project management skills needed to excel in your chosen career
Fees and funding
There are many ways to fund postgraduate study for home and international students
Fees
The fees quoted at the top of this page cover registration, tuition, supervision, assessment and examinations as well as:
- library membership with access to printed, multimedia and digital resources
- access to programme-appropriate software
- library and student IT support
- free on-campus wifi via eduroam
Additional costs
Although not all of the following are compulsory/relevant, you should keep in mind the costs of:
- accommodation and living expenditure
- books (should you wish to have your own copies)
- printing, photocopying and stationery
- PC/laptop (should you prefer to purchase your own for independent study and online learning activities)
- mobile phone/tablet (to access online services)
- field trips (travel and activity costs)
- placements (travel expenses and living costs)
- student visas (international students only)
- study abroad opportunities (travel costs, accommodation, visas and immunisations)
- academic conferences (travel costs)
- professional-body membership
- graduation (gown hire etc)
Funding
There are many ways to fund postgraduate study for home and international students. From loans to International Scholarships and subject-specific funding, you’ll find all of the information you need on our specialist postgraduate funding pages.
Please be aware that the UK’s departure from the EU may affect your tuition fees. Learn more about your fee status and which tuition fees are relevant to you.
Employability
Further your career prospects
LJMU has an excellent employability record with 96% (HESA 2018) of our postgraduates in work or further study six months after graduation. Our applied learning techniques and strong industry connections ensure our students are fully prepared for the workplace on graduation and understand how to apply their knowledge in a real world context.
This programme is particularly focused on preparing you for a career in industry or for PhD level study.
Data Science is a growing area of demand with a predicted scarcity of expertise forecast over the next decade. LJMU has strong links with many graduate employers of data scientists who value the skills developed in the course such as accessing, manipulating and drawing insight from large volumes of complex data; assimilating information from the research literature and scientific and technical writing.
The programme enables you to plan and complete a substantial programme of original research at the highest level. We expect that many projects should have the potential to result in papers in refereed journals and serve as the starting point for PhD level work.
The student experience
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Course modules
Discover the building blocks of your programme
Your programme is made up of a number of core modules which are part of the course framework. Some programmes also have optional modules that can be selected to enhance your learning in certain areas and many feature a dissertation, extended report or research project to demonstrate your advanced learning.
Core modules
Introduction to Data Analytics
20 credits
This module aims to provide an introduction to the key concepts of data compilation, management, querying, cleaning and visualisation.
Statistical Methods in R
20 credits
This module provides an introduction to key concepts in statistics and statistical computing using the R programming language, with an emphasis on the informed interpretation of the results of statistical testing.
Big Data Computing
20 credits
This module aims to develop skills in modern computing techniques for high performance analysis of large data sets. It also provides an understanding of how to translate an analysis problem to best exploit such techniques.
Research Methods in Data Science
20 credits
This module aims to equip you with the research skills necessary to undertake your dissertation project. It covers the preparation of a research proposal or a business case (depending on the academic/industrial context), consideration of ethical and legal issues, literature review, oral and written presentation skills and project management.
Machine Learning and Data Mining
20 credits
This module aims to develop skills in data mining, using methods from computational learning theory and artificial intelligence to extract previously unknown relationships from large data sets.
Efficient algorithms for complex data sets
20 credits
This module aims to develop skills in big data analysis, including techniques of dimensionality reduction and the application of statistical and machine learning models. In addition it aims to develop the key skill of working with experts from other domains.
Data Science Project
60 credits
This module allows you to display all of the skills learnt on the programme and give you the opportunity to demonstrate creativity, initiative, ingenuity and communication in an academic context.
Optional Modules
Work Based Project
60 credits
The Work Based Project aims to apply data science to the solution of a real-world problem, which will help to consolidate knowledge and skills learnt using the tools and systems that data scientists and data engineers use.
Teaching
An insight into teaching on your course
The methods used to enable outcomes to be achieved and demonstrated are as follows:
Acquisition of knowledge through lectures, seminars, practicals, directed reading, focused literature review exercises, coursework assignments, student discussion groups and tutorials.
Testing of the knowledge base is through a combination of continuous assessment methods, short and extended computer based assignments, formal written examinations, project report or dissertation, and oral presentations.
Analytic, mathematical and problem solving skills are developed through self-learning assessments, focussed literature review exercises, written assignments and tutorials.
The Research Project will involve both experimental and literature research, further developing skills of research methodology, critical analysis and discussion.
Analysis and problem solving skills are assessed through computer based assignments and written examinations. Experimental and analysis research skills are assessed in the presentation of the Project together with an oral defence.
Practical experimental skills, preparing a high-level technical report and giving a technical presentation are developed through the Research Project and Research Methods modules.
Computational and programming skills are embedded in every module and will be developed throughout the programme.
Project proposal and report, computer based coursework assignments and examinations, oral presentation.
Transferable skills are embedded on the programme. Skills are learned through completion of written assignments, participating in tutorials and collecting and transferring data via computer networks and from on-line databases, and through the Data Science Project.
As part of the research methods and project modules, the students will be encouraged and assisted to develop a professional on-line presence.
Skills are developed through self-assessment exercises, coursework and the oral and written "pitches" of research proposals and projection management plan. Tutorials and feedback assist and improve the level of skill achieved.
Assessment
How learning is monitored on your programme
To cater for the wide-ranging content of our courses and the varied learning preferences of our students, we offer a range of assessment methods on each programme.
You will be assessed via a combination of exams, coursework and an extended report based on your dissertation project. Typically 50% of your mark comes from coursework and 50% from an exam, although some modules may be assessed entirely by coursework done in the computer lab.
Course tutors
Our staff are committed to the highest standards of teaching and learning
Prof Ivan Baldry
Programme Leader
Ivan joined Liverpool John Moores University in October 2005. His research is in the area of galaxy properties, population statistics and evolution. This involves analysis and visualization of large multi-dimensional data sets.
On graduation you may become a qualified Data Scientist or carry out further research at doctorate level.
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School facilities
What you can expect from your School
The Astrophysics Research Institute in the Knowledge Quarter combines world-leading research in astrophysics with excellence in teaching at all levels. The Department of Applied Mathematics, based in the City Campus, is dedicated to producing research with real-world applications and offers a student-centered teaching portfolio. Teaching on this programme takes place in the city centre.
Entry requirements
You will need:
Qualification requirements
Undergraduate degree
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a minimum 2:2 in a numerate, scientific or computer-based subject
A levels
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an A level, or equivalent, in Mathematics at Grade C or more
International requirements
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IELTS
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IELTS 6.5 (minimum 6.0 in each component)
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Further information
- Extra Requirements
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RPL
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RPL is not accepted on this programme
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Application and selection
Securing your place at LJMU
To apply for this programme, you are required to complete an LJMU online application form. You will need to provide details of previous qualifications and a personal statement outlining why you wish to study this programme.
The University reserves the right to withdraw or make alterations to a course and facilities if necessary; this may be because such changes are deemed to be beneficial to students, are minor in nature and unlikely to impact negatively upon students or become necessary due to circumstances beyond the control of the University. Where this does happen, the University operates a policy of consultation, advice and support to all enrolled students affected by the proposed change to their course or module.