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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.

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
1 Year Full-Time

Department
Computer and Information Sciences

Location
City Campus, Northumbria University

City
Newcastle

Start
September 2020

Fee Information

Module Information

Discover more / Data Science MSc

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.

Book an Open Day / Experience Data Science

Visit an Open Day to really get an inside view of what it's like to study Data Science at Northumbria. Speak to staff from the course and discover your funding options.

Our Data Science MSc has been designed in consultation with partners from industry, and has been strongly influenced by industry practice and employer's needs, as well as our research strengths.  

You will take six taught modules covering principles of data science, computational and statistical methods and techniques, programming in Python and R, advanced databases, Big Data in a cloud computing setting, machine learning, and transferable skills in project management and research.

Finally, you will undertake a major data science project, allowing you to specialise in a subject of interest to you (e.g Business Analytics, Health Analytics and Game Analytics). You will complete practical tasks based on real-life situations, accompanied by critical reflection, and will follow standard professional practice.

Book an Open Day / Experience Data Science

Visit an Open Day to really get an inside view of what it's like to study Data Science at Northumbria. Speak to staff from the course and discover your funding options.

Our teaching staff include cutting-edge researchers, whose areas of specialisms include topics covered on this course, helping ensure that teaching is right up-to-date. Specialisms include big data, data mining, machine learning, digital literacy, information behaviour, information retrieval systems, recommender systems, and the link between information science and cognitive psychology.

Our eminent academics have written books that regularly appear on reading lists for information science courses at universities all over the world. They also work as external examiners and reviewers of courses and research at other UK and non-UK universities.

Book an Open Day / Experience Data Science

Visit an Open Day to really get an inside view of what it's like to study Data Science at Northumbria. Speak to staff from the course and discover your funding options.

Northumbria University uses a range of technologies to enhance your learning, with tools including web-based self-guided exercises, online tests with feedback, videos and tutorials. These tools support and extend the material that is delivered during lectures, and are available anywhere anytime. Group work and peer interaction feature prominently in our learning and teaching, this reflects the practices you are likely to encounter within the working environment.

You will have 24/7 term-time access to Northumbria’s library, which has over half a million print books as well as half a million electronic books available online. 

The University Library has advanced search software and database tools that allow you to use a single search box to get fast results from across a wide and reliable range of academic resources.

Throughout the duration of your course you will have access to our state-of-the-art facilities including our new Computer and Information Sciences building, this gives you access to dedicated IT systems and is available evenings and weekends.

Facilities

Explore our brand new Computer and Information Sciences building.

Book an Open Day / Experience Data Science

Visit an Open Day to really get an inside view of what it's like to study Data Science at Northumbria. Speak to staff from the course and discover your funding options.

Through the dissertation module, you will undertake a major individual project. This provides a platform to conduct a substantial piece of research and development into a topic at the forefront of the Data Science discipline. This will include the need to utilise appropriate research techniques, critical evaluation and synthesis. 

You are also invited to attend department research seminars and workshops organised by IoC (Institute of Coding). These provide another opportunity for exposure to research methods and contemporary research issues in computer and data sciences.

Research / Department of Computing and Information Sciences

Click through to discover our current research areas.

Book an Open Day / Experience Data Science

Visit an Open Day to really get an inside view of what it's like to study Data Science at Northumbria. Speak to staff from the course and discover your funding options.

Demand for those with specialist data science qualifications is high and this course can prepare you for a range of careers in the modern digital world. 

You will be well placed to take up a range of roles available in the IT based business world including, but not limited to, Information and Data Manager, Business Systems Analyst, Enterprise Data Analyst, Data Scientist, Data Engineer, and Data Specialist. These roles are in a variety of industries including investment management, healthcare and banking, amongst many others. There is currently high demand for data science specialists in the UK IT sector.

Book an Open Day / Experience Data Science

Visit an Open Day to really get an inside view of what it's like to study Data Science at Northumbria. Speak to staff from the course and discover your funding options.

Entry Requirements 2020/21

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 2020/21 Entry

Full UK Fee: £7,170

Full EU Fee: £7,170

Full International Fee: £15,500

ADDITIONAL COSTS

There are no Additional Costs

Scholarships and discounts

Click here for Home/EU scholarships and discounts information

Click here for International scholarships and discounts information

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* By submitting your information you are consenting to your data being processed by Northumbria University (as Data Controller) and Campus Management Corp. (acting as Data Processor). To see the University's privacy policy please click here

Modules

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 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, and provide a plan of execution, using tools and techniques in project management, including an outline of deliverables (both artefacts and products).

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

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. 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

Modules

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 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, and provide a plan of execution, using tools and techniques in project management, including an outline of deliverables (both artefacts and products).

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

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. 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

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

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 
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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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* By submitting your information you are consenting to your data being processed by Northumbria University (as Data Controller) and Campus Management Corp. (acting as Data Processor). To see the University's privacy policy please click here

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