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Please note: Applications for our Advanced Practice routes are now closed for September 2021,we are still accepting applications for our one year route, applications are also open for our Advanced Practice courses for January 2022 and will be opening soon for September 2022.

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

Fee Information

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 more / Explore Northumbria University

Take a look at what Northumbria has to offer and discover what studying with us can do for you.

Entry Requirements 2021/22

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 2021/22 Entry

Full UK Fee: £11,400

Full EU Fee: £19,000

Full International Fee: £19,000



Scholarships and Discounts

Click here for UK, EU and International Scholarships scholarship, fees, and funding information.


ADDITIONAL COSTS

There are no Additional Costs

If you'd like to receive news and information from us in the future about the course or finance then please complete the below form

* 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 and project management, and you will apply them to develop a master’s project proposal. This will include research approaches and methods of research, including literature searching, evaluation and review and risk management within project management tools and techniques. You will also consider relevant legal, ethical and social issues and good professional practice.

By the end of this module you will have constructed a project proposal which can be executed in a master’s project. This will contain a brief literature review justifying a research question, establish aims and objectives, carry out a risk evaluation and provide a plan of execution, using tools and techniques in project management, including an outline of deliverables (both artefacts and products).

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 covers 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 (Optional,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
• Restricted Boltzmann machines
• 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 and present the results from this work in both written and oral forms. Your project itself will be a major piece of independent and original research centred at the forefront of your programme discipline within the wider sphere of the computer science and digital technologies field.

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 consider and address the professional, ethical, legal and social issues related 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 science and digital technologies and demonstrate critical and innovative thinking and problem solving within a research environment.

Your research proposal will normally have been produced as part of an earlier module on research and project planning but should be reviewed again at the start of the project phase to ensure it is still valid and appropriate.

More information

KV7007 -

Advance 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.

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 and project management, and you will apply them to develop a master’s project proposal. This will include research approaches and methods of research, including literature searching, evaluation and review and risk management within project management tools and techniques. You will also consider relevant legal, ethical and social issues and good professional practice.

By the end of this module you will have constructed a project proposal which can be executed in a master’s project. This will contain a brief literature review justifying a research question, establish aims and objectives, carry out a risk evaluation and provide a plan of execution, using tools and techniques in project management, including an outline of deliverables (both artefacts and products).

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 covers 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 (Optional,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
• Restricted Boltzmann machines
• 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 and present the results from this work in both written and oral forms. Your project itself will be a major piece of independent and original research centred at the forefront of your programme discipline within the wider sphere of the computer science and digital technologies field.

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 consider and address the professional, ethical, legal and social issues related 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 science and digital technologies and demonstrate critical and innovative thinking and problem solving within a research environment.

Your research proposal will normally have been produced as part of an earlier module on research and project planning but should be reviewed again at the start of the project phase to ensure it is still valid and appropriate.

More information

KV7007 -

Advance 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.

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 admissions team will be happy to help. They can be contacted on 0191 406 0901.

Contact Details for Applicants:

bc.applicantservices@northumbria.ac.uk

All information on this course page is accurate at the time of viewing.

Courses starting in 2021 are offered as a mix of face to face and online learning. We continue to monitor government and local authority guidance in relation to Covid-19 and we are ready and able to flex accordingly to ensure the health and safety of our students and staff.

Contact time is subject to increase or decrease in line with additional restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors, potentially to a full online offer, should further restrictions be deemed necessary in future.

Our online activity will be delivered through Blackboard Ultra, enabling collaboration, connection and engagement with materials and people.

 

Current, Relevant and Inspiring

We continuously review and improve course content in consultation with our students and employers. To make sure we can inform you of any changes to your course register for updates on the course page.


Your Learning Experience find out about our distinctive approach at 
www.northumbria.ac.uk/exp

Admissions Terms and Conditions - northumbria.ac.uk/terms
Fees and Funding - northumbria.ac.uk/fees
Admissions Policy - northumbria.ac.uk/adpolicy
Admissions Complaints Policy - northumbria.ac.uk/complaints




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