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We are currently reviewing modules which provide opportunities to work with industry to gain real experience. Modules will be updated in due course.

Designed in consultation with partners from industry, and the business sector, our highly practical Data Science Masters provides you with the relevant skills needed to analyse, synthesise and manage different types and sizes of data efficiently. 

Our Data Science MSc will provide you with the ability to explore data insights to ensure organisations are making the most out of their data. You will develop knowledge insight from a variety of structured and unstructured data, using a range of data analysis methods, processes, algorithms and systems. 

The course provides a greater understanding of Big Data and Cloud computing and will equip you with skills needed to tackle realistic Big Data problems. You will use principal machine learning methods, advanced database technologies, data visualisation techniques and statistical approaches to apply modern, analytical and statistical techniques to business data - combining both theoretical and practical approaches. You will also become equipped with programming skills in Python and R for effective, efficient, statistical data analysis.

This course is suited to those who have an undergraduate degree in computing/information sciences, or mathematical and statistical disciplines with applied computing components. It is ideal for graduates who wish to advance their career by becoming a data scientist. Alternatively, those with a considerable computing background in an industrial / business setting are suitable applicants.

Course Information

Level of Study
Postgraduate

Mode of Study
2 years full-time with Advanced Practice
3 other options available

Department
Computer and Information Sciences

Location
City Campus, Northumbria University

City
Newcastle

Start
September 2024

Fees
Fee Information

Modules
Module Information

Discover More / Data Science

Listen to Programme Leader Dr Akhtar Ali talk about the Data Science MSc at Northumbria, in our Masters in a Minute (or so) and discover what the course involves and why this course could be for you.

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Funding and Scholarships

Discover the funding options available to you.

Discover NU World / A virtual journey through everything Northumbria has to offer.

Explore our immersive 360 tours, informative subject videos, inspirational student profiles, ground-breaking research, and a range of life at university blogs videos and articles.

Entry Requirements 2024/25

Standard Entry

Applicants should normally have:

A minimum of a 2:2 honours degree (or equivalent) in a quantitative subject such as computer / information science, engineering, maths, statistics, or a related discipline (e.g. IT, software engineering). Other subject qualifications, equivalent professional qualifications and/or relevant work experience will be considered on an individual basis.

International qualifications:

If you have studied a non UK qualification, you can see how your qualifications compare to the standard entry criteria, by selecting the country that you received the qualification in, from our country pages. Visit www.northumbria.ac.uk/yourcountry 

English language requirements:

International applicants are required to have a minimum overall IELTS (Academic) score of 6.5 with 5.5 in each component (or approved equivalent*).

 *The university accepts a large number of UK and International Qualifications in place of IELTS.  You can find details of acceptable tests and the required grades you will need in our English Language section. Visit www.northumbria.ac.uk/englishqualifications

Fees and Funding 2024/25 Entry

Full UK Fee: £13,460

Full EU Fee: £22,750

Full International Fee: £22,750



Scholarships and Discounts

Discover More about Fees, Scholarships and other Funding options for UK, EU and International applicants.

ADDITIONAL COSTS

There are no Additional Costs

If you’d like to receive the latest updates from Northumbria about our courses, events, finance & funding then enter your details below.

* At Northumbria we are strongly committed to protecting the privacy of personal data. To view the University’s Privacy Notice please click here

Modules

We are currently reviewing modules which provide opportunities to work with industry to gain real experience. Modules will be updated in due course.

Module information is indicative and is reviewed annually therefore may be subject to change. Applicants will be informed if there are any changes.

KF7028 -

Research Methods and Project Management (Core,20 Credits)

In this module you will learn about research and the processes involved in carrying out research in the area of computer and information sciences. The module will encompass the full research cycle from development of the initial concept through to final evaluation and reflection on your research. You will learn how to search, find and evaluate the literature and how to use this to construct a critical literature review. You will also be able to gain an understanding of different research methods and techniques and how to apply them in practice. You will be given the opportunity to gain expertise in data and information analysis, from data collection through to the analysis, presentation and interpretation of results. You will be able to critically evaluate and reflect on the research process. You will also learn about ethics and professional conduct whilst undertaking research and consider and manage risk (including health and safety) and legal, societal and sustainability issues with respect to a research project.

Alongside this you will be provided with the skills and techniques to effectively manage a project from start to finish, including the planning and monitoring aspects.

More information

KF7032 -

Big Data and Cloud Computing (Core,20 Credits)

In this module you will develop knowledge and skills that will enable you to tackle a realistic big data problem, using some of the principal machine learning techniques and statistical approaches used in big data analysis. Furthermore, you will learn how to implement your solution using an industry leading Cloud computing provider together with appropriate distributed processing environments.

You will learn how to host multi-terabyte sized big datasets using a cloud service provider. This will includes provisioning a commercial cloud provider, and then mastering appropriate distributed operating systems, such as Hadoop. You will then learn approaches to processing and analysing big data, based on advanced statistical processing, supervised and unsupervised machine learning algorithms and other state of the art big data analytic methods. Such techniques include clustering algorithms, pattern based information extraction, linear and non-linear regression, and feature based models. Inevitably, much work on big data analysis is statistical, so you will therefore develop some relevant statistical understanding. As data visualization is frequently critical in helping to develop hypotheses about the data, you will also cover and apply problem relevant 2D and 3D visualization methods where appropriate to the particular datasets.

More information

KL7010 -

Principles of Data Science (Core,20 Credits)

In this module, you will learn data science lifecycle and foundations, principles, and fundamental statistical methods, techniques and applications in data science. You will explore key areas of data science including question and hypotheses formulation, data collection and cleaning, visualization, statistical inference, predictive modelling, and decision-making. You will learn fundamental aspects of probability and statistics to equip you to lead standard data analysis projects in industry and research. The module will cover broad topics such as:

• Foundations of Data Science
• Principles and techniques of Data Science
• Review and evaluation of Data Science methods, techniques and tools

More information

KL7011 -

Advanced Databases (Core,20 Credits)

In this module, you will learn about the entire data life cycle (from creation to disposal) and will gain a deep understanding of classical database development processes and approaches to modelling, design and management of databases. You will be able to learn and employ data warehousing techniques to integrate and consolidate data from different sources, which can then be used for business reporting, exploratory data analysis and advanced data analytics. In addition, you will realise the responsibilities of database designers with respect to professional, legal, security and ethical issues as well as undertaking risk management and evaluation of commercial risk in relation to data management. Moreover, you get an appreciation of non-traditional data types, systems and applications (e.g., NoSQL Databases), data standards and data quality. The module will covers topics such as:

• An overview of the entire data life cycle (e.g., creation, modelling, representation, usage, maintenance, disposal, etc)
• Classical data engineering processes and approaches (modelling, design, implementation and management and access of databases)
• Data warehousing
• Non-traditional data management technologies (e.g., NoSQL databases)
• Data analytics
• Data standards and data quality

More information

KL7012 -

Statistical Programming (Core,20 Credits)

The aim of this module is to provide you with the knowledge and practical skills for understanding the statistical methods and programming for data science. The module combines both theoretical and practical approaches so that you will have the skills to tackle problems in various realistic business settings. It also equip you with programming skills in R for effective and efficient statistical data analysis.

This module is primarily concerned with examining and analysing data using R (statistical programming) arising from real world environments (e.g., businesses, industries) and to relate the extracted information to strategic, tactical and operational decision-making. You will covers topics such as:
• Introduction to statistical modelling, data processing and big data and to challenges in practical data analyses
• Fundamental statistics: variable types: nominal, ordinal, categorical. Formulae: functions, powers, summation
• Introduction to R programming language environment
• Introduction to practical data analysis with the statistical software environment R for data manipulation, sampling, importing and exporting data and for drawing Scatterplot, Histogram, etc.
• Application and implementation of core statistical analysis using R (e.g., probability and distributions, Statistical models (e.g., Linear models, Generalized linear models, Nonlinear least squares and maximum likelihood models)
• How to subsume and to present results of statistical data analyses (for statisticians and non-statisticians)
• Practical statistical analysis of real data sets, reporting and presentation of the obtained results

More information

KV7001 -

Academic Language Skills for Computer and Information Sciences (Core – for International and EU students only,0 Credits)

Academic skills when studying away from your home institution can differ due to cultural and language differences in teaching and assessment practices. This module is designed to support your transition in the use and practice of technical language and subject specific skills around assessments and teaching provision in your chosen subject area in the Department of Architecture and Built Environment. The overall aim of this module is to develop your abilities to read and study effectively for academic purposes; to develop your skills in analysing and using source material in seminars and academic writing and to develop your use and application of language and communications skills to a higher level.

The topics you will cover on the module include:

• Understanding assignment briefs and exam questions.
• Developing academic writing skills, including citation, paraphrasing, and summarising.
• Practising ‘critical reading’ and ‘critical writing’.
• Planning and structuring academic assignments (e.g. essays, reports and presentations).
• Avoiding academic misconduct and gaining credit by using academic sources and referencing effectively.
• Listening skills for lectures.
• Speaking in seminar presentations.
• Giving discipline-related academic presentations, experiencing peer observation, and receiving formative feedback.
• Speed reading techniques.
• Discussing ethical issues in research, and analysing results.
• Describing bias and limitations of research.
• Developing self-reflection skills.

More information

KV7006 -

Machine Learning (Core,20 Credits)

In this module you will develop knowledge and skills that will enable you to tackle a realistic machine learning problem, using some of the principal advanced machine learning techniques. You will also learn about recent applications of machine learning. Furthermore, you will learn how to implement machine learning based solutions and evaluate their performance using real world examples. The main topics covered in this module include:

• Mathematical foundations of machine learning
• Supervised, Unsupervised and reinforcement learning
• Feature extraction, feature selection and dimensionality reduction
• Classification and clustering techniques
• Optimisation techniques
• Ensemble techniques
• Autoencoders
• Deep generative models
• Deep Learning
• Data visualisation

More information

KF7029 -

MSc Computer Science & Digital Technologies Project (Core,60 Credits)

The aim of this module is to enable you to undertake a substantial academic research project at Masters level, record your progress though this, and present the results from your research in both written and oral forms. Your research project will be a major piece of independent and original research centred at the forefront of your programme discipline within the wider sphere of computer and information sciences.

You will experience the full life cycle of a research project from initial conception and development of a research proposal, through a critical review of the literature, planning, design, implementation and analysis of your main research project, to final evaluation, reflection and dissemination. You will be expected to conduct your research in an ethical and professional manner, and manage risk and consideration of the legal, societal and sustainability issues applicable, to this academic research project. You will also be expected to apply your expertise, project management and practical skills within your particular domain of computer and information sciences and demonstrate critical and innovative thinking and problem solving within a research environment.

More information

KV7007 -

Advanced Practice Semester (Core,60 Credits)

This 60 credit module is designed for all full-time postgraduate programmes within the Faculty of Engineering and Environment and provides you with the opportunity to undertake a Live Project (including the possibility of live research project work with staff). for one semester as part of your programme. This experience gives you the opportunity to apply skills and knowledge acquired during the taught part of your programme and to acquire new skills and knowledge in an alternative learning environment. Specific learning will be defined in a personal learning contract. You will be expected to conduct a risk assessment of the project with a project plan.
You will be expected to do a critical literature review research on existing state-of-the-art techniques and provide an optimal solution to the problem based on the research as well minimise any weaknesses or adverse effects of your solution.
Your Advanced Practice semester will be assessed on a pass/fail basis and as such, it does not contribute to the classification of your degree. However when taken and passed it is recognised both in your transcript as a 60 credit Advanced Practice Module and in your degree title.

More information

Modules

We are currently reviewing modules which provide opportunities to work with industry to gain real experience. Modules will be updated in due course.

Module information is indicative and is reviewed annually therefore may be subject to change. Applicants will be informed if there are any changes.

KF7028 -

Research Methods and Project Management (Core,20 Credits)

In this module you will learn about research and the processes involved in carrying out research in the area of computer and information sciences. The module will encompass the full research cycle from development of the initial concept through to final evaluation and reflection on your research. You will learn how to search, find and evaluate the literature and how to use this to construct a critical literature review. You will also be able to gain an understanding of different research methods and techniques and how to apply them in practice. You will be given the opportunity to gain expertise in data and information analysis, from data collection through to the analysis, presentation and interpretation of results. You will be able to critically evaluate and reflect on the research process. You will also learn about ethics and professional conduct whilst undertaking research and consider and manage risk (including health and safety) and legal, societal and sustainability issues with respect to a research project.

Alongside this you will be provided with the skills and techniques to effectively manage a project from start to finish, including the planning and monitoring aspects.

More information

KF7032 -

Big Data and Cloud Computing (Core,20 Credits)

In this module you will develop knowledge and skills that will enable you to tackle a realistic big data problem, using some of the principal machine learning techniques and statistical approaches used in big data analysis. Furthermore, you will learn how to implement your solution using an industry leading Cloud computing provider together with appropriate distributed processing environments.

You will learn how to host multi-terabyte sized big datasets using a cloud service provider. This will includes provisioning a commercial cloud provider, and then mastering appropriate distributed operating systems, such as Hadoop. You will then learn approaches to processing and analysing big data, based on advanced statistical processing, supervised and unsupervised machine learning algorithms and other state of the art big data analytic methods. Such techniques include clustering algorithms, pattern based information extraction, linear and non-linear regression, and feature based models. Inevitably, much work on big data analysis is statistical, so you will therefore develop some relevant statistical understanding. As data visualization is frequently critical in helping to develop hypotheses about the data, you will also cover and apply problem relevant 2D and 3D visualization methods where appropriate to the particular datasets.

More information

KL7010 -

Principles of Data Science (Core,20 Credits)

In this module, you will learn data science lifecycle and foundations, principles, and fundamental statistical methods, techniques and applications in data science. You will explore key areas of data science including question and hypotheses formulation, data collection and cleaning, visualization, statistical inference, predictive modelling, and decision-making. You will learn fundamental aspects of probability and statistics to equip you to lead standard data analysis projects in industry and research. The module will cover broad topics such as:

• Foundations of Data Science
• Principles and techniques of Data Science
• Review and evaluation of Data Science methods, techniques and tools

More information

KL7011 -

Advanced Databases (Core,20 Credits)

In this module, you will learn about the entire data life cycle (from creation to disposal) and will gain a deep understanding of classical database development processes and approaches to modelling, design and management of databases. You will be able to learn and employ data warehousing techniques to integrate and consolidate data from different sources, which can then be used for business reporting, exploratory data analysis and advanced data analytics. In addition, you will realise the responsibilities of database designers with respect to professional, legal, security and ethical issues as well as undertaking risk management and evaluation of commercial risk in relation to data management. Moreover, you get an appreciation of non-traditional data types, systems and applications (e.g., NoSQL Databases), data standards and data quality. The module will covers topics such as:

• An overview of the entire data life cycle (e.g., creation, modelling, representation, usage, maintenance, disposal, etc)
• Classical data engineering processes and approaches (modelling, design, implementation and management and access of databases)
• Data warehousing
• Non-traditional data management technologies (e.g., NoSQL databases)
• Data analytics
• Data standards and data quality

More information

KL7012 -

Statistical Programming (Core,20 Credits)

The aim of this module is to provide you with the knowledge and practical skills for understanding the statistical methods and programming for data science. The module combines both theoretical and practical approaches so that you will have the skills to tackle problems in various realistic business settings. It also equip you with programming skills in R for effective and efficient statistical data analysis.

This module is primarily concerned with examining and analysing data using R (statistical programming) arising from real world environments (e.g., businesses, industries) and to relate the extracted information to strategic, tactical and operational decision-making. You will covers topics such as:
• Introduction to statistical modelling, data processing and big data and to challenges in practical data analyses
• Fundamental statistics: variable types: nominal, ordinal, categorical. Formulae: functions, powers, summation
• Introduction to R programming language environment
• Introduction to practical data analysis with the statistical software environment R for data manipulation, sampling, importing and exporting data and for drawing Scatterplot, Histogram, etc.
• Application and implementation of core statistical analysis using R (e.g., probability and distributions, Statistical models (e.g., Linear models, Generalized linear models, Nonlinear least squares and maximum likelihood models)
• How to subsume and to present results of statistical data analyses (for statisticians and non-statisticians)
• Practical statistical analysis of real data sets, reporting and presentation of the obtained results

More information

KV7001 -

Academic Language Skills for Computer and Information Sciences (Core – for International and EU students only,0 Credits)

Academic skills when studying away from your home institution can differ due to cultural and language differences in teaching and assessment practices. This module is designed to support your transition in the use and practice of technical language and subject specific skills around assessments and teaching provision in your chosen subject area in the Department of Architecture and Built Environment. The overall aim of this module is to develop your abilities to read and study effectively for academic purposes; to develop your skills in analysing and using source material in seminars and academic writing and to develop your use and application of language and communications skills to a higher level.

The topics you will cover on the module include:

• Understanding assignment briefs and exam questions.
• Developing academic writing skills, including citation, paraphrasing, and summarising.
• Practising ‘critical reading’ and ‘critical writing’.
• Planning and structuring academic assignments (e.g. essays, reports and presentations).
• Avoiding academic misconduct and gaining credit by using academic sources and referencing effectively.
• Listening skills for lectures.
• Speaking in seminar presentations.
• Giving discipline-related academic presentations, experiencing peer observation, and receiving formative feedback.
• Speed reading techniques.
• Discussing ethical issues in research, and analysing results.
• Describing bias and limitations of research.
• Developing self-reflection skills.

More information

KV7006 -

Machine Learning (Core,20 Credits)

In this module you will develop knowledge and skills that will enable you to tackle a realistic machine learning problem, using some of the principal advanced machine learning techniques. You will also learn about recent applications of machine learning. Furthermore, you will learn how to implement machine learning based solutions and evaluate their performance using real world examples. The main topics covered in this module include:

• Mathematical foundations of machine learning
• Supervised, Unsupervised and reinforcement learning
• Feature extraction, feature selection and dimensionality reduction
• Classification and clustering techniques
• Optimisation techniques
• Ensemble techniques
• Autoencoders
• Deep generative models
• Deep Learning
• Data visualisation

More information

KF7029 -

MSc Computer Science & Digital Technologies Project (Core,60 Credits)

The aim of this module is to enable you to undertake a substantial academic research project at Masters level, record your progress though this, and present the results from your research in both written and oral forms. Your research project will be a major piece of independent and original research centred at the forefront of your programme discipline within the wider sphere of computer and information sciences.

You will experience the full life cycle of a research project from initial conception and development of a research proposal, through a critical review of the literature, planning, design, implementation and analysis of your main research project, to final evaluation, reflection and dissemination. You will be expected to conduct your research in an ethical and professional manner, and manage risk and consideration of the legal, societal and sustainability issues applicable, to this academic research project. You will also be expected to apply your expertise, project management and practical skills within your particular domain of computer and information sciences and demonstrate critical and innovative thinking and problem solving within a research environment.

More information

KV7007 -

Advanced Practice Semester (Core,60 Credits)

This 60 credit module is designed for all full-time postgraduate programmes within the Faculty of Engineering and Environment and provides you with the opportunity to undertake a Live Project (including the possibility of live research project work with staff). for one semester as part of your programme. This experience gives you the opportunity to apply skills and knowledge acquired during the taught part of your programme and to acquire new skills and knowledge in an alternative learning environment. Specific learning will be defined in a personal learning contract. You will be expected to conduct a risk assessment of the project with a project plan.
You will be expected to do a critical literature review research on existing state-of-the-art techniques and provide an optimal solution to the problem based on the research as well minimise any weaknesses or adverse effects of your solution.
Your Advanced Practice semester will be assessed on a pass/fail basis and as such, it does not contribute to the classification of your degree. However when taken and passed it is recognised both in your transcript as a 60 credit Advanced Practice Module and in your degree title.

More information

Study Options

The following alternative study options are available for this course:

Any Questions?

Our Applicant Services team will be happy to help.  They can be contacted on 0191 406 0901 or by using our Contact Form.



Accessibility and Student Inclusion

Northumbria University is committed to developing an inclusive, diverse and accessible campus and wider University community and are determined to ensure that opportunities we provide are open to all.

We are proud to work in partnership with AccessAble to provide Detailed Access Guides to our buildings and facilities across our City, Coach Lane and London Campuses. A Detailed Access Guide lets you know what access will be like when you visit somewhere. It looks at the route you will use getting in and what is available inside. All guides have Accessibility Symbols that give you a quick overview of what is available, and photographs to show you what to expect. The guides are produced by trained surveyors who visit our campuses annually to ensure you have trusted and accurate information.

You can use Northumbria’s AccessAble Guides anytime to check the accessibility of a building or facility and to plan your routes and journeys. Search by location, building or accessibility feature to find the information you need. 

We are dedicated to helping students who may require additional support during their student journey and offer 1-1 advice and guidance appropriate to individual requirements. If you feel you may need additional support you can find out more about what we offer here where you can also contact us with any questions you may have:

Accessibility support

Student Inclusion support




All information is accurate at the time of sharing. 

Full time Courses are primarily delivered via on-campus face to face learning but could include elements of online learning. Most courses run as planned and as promoted on our website and via our marketing materials, but if there are any substantial changes (as determined by the Competition and Markets Authority) to a course or there is the potential that course may be withdrawn, we will notify all affected applicants as soon as possible with advice and guidance regarding their options. It is also important to be aware that optional modules listed on course pages may be subject to change depending on uptake numbers each year.  

Contact time is subject to increase or decrease in line with possible restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors if this is deemed necessary in future.

 

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Find out about our distinctive approach at 
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northumbria.ac.uk/terms

Fees and Funding
northumbria.ac.uk/fees

Admissions Policy
northumbria.ac.uk/adpolicy

Admissions Complaints Policy
northumbria.ac.uk/complaints



If you’d like to receive the latest updates from Northumbria about our courses, events, finance & funding then enter your details below.

* At Northumbria we are strongly committed to protecting the privacy of personal data. To view the University’s Privacy Notice please click here

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