Data Science (with specialisation in Statistics) MSc, PGDip, PGCert

Overview

This course blends modern statistical methods with the computational skills to handle large quantities of unstructured data.

Data science is revolutionising every area of science, engineering and commerce. It has the potential for huge societal and economic benefits.

Statistical analysis, with data analytics, is in demand. There is an industry need for analytical skills that can interpret and extract value from complex data.

During this course, you'll develop a comprehensive understanding of data science theory, and how to use it in real-world scenarios. You'll also develop a combination of skills in statistics and computer science.

You’ll put your new skills into action immediately, and work with real data sets in your applied data science projects. All module assessments are based on these projects.

Important information

We've highlighted important information about your course. Please take note of any deadlines.

Your course and study experience - disclaimers and terms and conditions

Please rest assured we make all reasonable efforts to provide you with the programmes, services and facilities described. However, it may be necessary to make changes due to significant disruption, for example in response to Covid-19.

View our Academic experience page, which gives information about your Newcastle University study experience for the academic year 2024-25.

See our terms and conditions and student complaints information, which gives details of circumstances that may lead to changes to programmes, modules or University services.

 

Qualifications explained

Find out about the different qualification options for this course.

MSc - Master of Science

An MSc is a taught Master’s degree. It usually involves the study of a science-related subject. It typically includes:

• subject-specific taught modules
• a dissertation or research project of approximately 15,000 – 18,000 words

You'll usually study an MSc full-time over 12 months.

A Master of Science is typically awarded for the successful completion of 120 credits of taught modules and a 60-credit dissertation or research project.

Find out about different types of postgraduate qualifications.

PGDip

A PGDip is awarded if you successfully complete all of the taught modules (120 credits).

Find out about different types of postgraduate qualifications

PGCert

A PGCert is awarded if you successfully complete half of the taught modules (60 credits).

Find out about different types of postgraduate qualifications.

What you'll learn

This course has three phases.

There will be no written exams during this Master's.

Phase one

We’ll introduce you to the core knowledge and skills in statistics and computer science.

You’ll also receive introductory training in Python, alongside data science modules in:

• data visualisation
• machine learning
• time series analysis
• Bayesian inference

These modules are taught as an intensive block. This means that full-time students are taught two modules simultaneously. Teaching for part-time students is timetabled to accommodate everyone.

Phase two

Phase two consists of more advanced technical modules, as well as a group project.

We'll introduce the aspects that govern all areas of data science practice, including:

• professionalism
• legislation
• ethics

During the group project, you'll develop and evaluate a data science solution to a complex, real-world problem. You'll work in an industry organisation. They can be a regional, national or charitable organisation. You'll propose a data science project in that company, institute, or area of research.

Phase three

In your final phase, you'll work on an individual research project. It'll give you an opportunity to:

• develop your knowledge and skills
• work in a research or development team

You can develop your project:

• at the University under an academic supervisor
• by securing an industrial placement
• working with your current employer

You'll have one-to-one supervision from an experienced staff member. If needed, you'll also get supervision from industry partners.

Modules

You will study modules on this course. A module is a unit of a course with its own approved aims and outcomes and assessment methods.

Course content changes

Module information is intended to provide an example of what you will study.

Our teaching is informed by research. Course content changes periodically to reflect developments in the discipline, the requirements of external bodies and partners, and student feedback.

Full details of the modules on offer will be published through the Programme Regulations and Specifications ahead of each academic year. This usually happens in May.

To find out more please see our terms and conditions.

Optional modules availability

Some courses have optional modules. Student demand for optional modules may affect availability.

 

Data Science (Statistics) MSc modules

Compulsory Modules	Credits
Engineering for AI	10
Computing Foundations of Data Science	10
Data Visualization	10
Data Management and Exploratory Data Analysis	10
Data Science in the Wild (Group Project)	10
Machine Learning with Project	10
Time Series Data	10
Bayesian Methodology	10
Statistical Learning for Data Science	10
Project and Dissertation in Data Science	80

Optional Modules	Credits
Complex Data Visualization	10
Deep Learning	10

Data Science (Statistics) PGDip modules

Compulsory Modules	Credits
Engineering for AI	10
Computing Foundations of Data Science	10
Data Visualization	10
Data Management and Exploratory Data Analysis	10
Data Science in the Wild (Group Project)	10
Machine Learning with Project	10
Time Series Data	10
Bayesian Methodology	10
Statistical Learning for Data Science	10
Project and Dissertation in Data Science	20

Optional Modules	Credits
Complex Data Visualization	10
Deep Learning	10

Data Science (Statistics) PGCert modules

Optional Modules	Credits
Engineering for AI	10
Data Visualization	10
Image Informatics	10
Data Management and Exploratory Data Analysis	10
Data Science in the Wild (Group Project)	10
Machine Learning with Project	10
Complex Data Visualization	10
Deep Learning	10
Statistical Foundations of Data Science	10
Statistical Learning for Data Science	10

How you'll learn

Delivery and teaching methods

The School of Computing and School of Mathematics, Statistics and Physics deliver this course.

Assessment methods

Depending on your modules, you'll be assessed through a combination of:

• Computer assessment
• Dissertation
• Professional skills assessments
• Oral examination
• Oral presentation
• Practical lab report
• Poster
• Problem-solving exercises
• Report

Wellbeing advisor support

The School of Computing has a dedicated Wellbeing Advisor who understands the needs of our students.

They can be a confidential listening ear and provide guidance on a range of wellbeing issues.

Student Support

Your teaching and learning is also supported by Canvas. Canvas is a Virtual Learning Environment. You'll use Canvas to submit your assignments and access your:

• module handbooks
• course materials
• groups
• course announcements and notifications
• written feedback

Throughout your studies, you’ll have access to support from:

• peers
• academics
• personal tutors
• our University Student Services Team
• student representatives

You'll also be assigned an academic member of staff. They will be your personal tutor throughout your time with us. They can help with academic and personal issues.

Our staff

We're home to the UK’s National Innovation Centre for Data (NICD). We're also a partner of the Alan Turing Institute, the national institute for data science and artificial intelligence. All our academic staff involved in teaching data science modules have international reputations for their contributions to the field. Many of them have extensive experience as practitioners in industry as well as work in academia.

Your development

During your studies, you will quickly develop your technical, communication and soft skills. You’ll learn how to apply them to real-world problems.

You’ll also create a portfolio of work using your course projects. Your portfolio can demonstrate your skills to future employers and can be shared on various platforms like GitHub.

Technical skills

Learn to program using Python and R and use them for:

• data manipulation
• data analysis
• data visualisation

You’ll also develop your skills in data modelling and data forecasting using various statistical and machine learning techniques.

Communication skills

You’ll learn how to write technical reports and present your work. You’ll demonstrate your presentation skills through oral presentations and academic posters.

You’ll also demonstrate your skills during your individual and group projects, while working on academic and industry-based topics.

Your future

Working with data is arguably the most desirable skill in today's workplace. As a graduate of this course, you will be qualified to work in a wide range of areas and sectors.

Becoming a data scientist or data analyst is the most common career path. Our graduates have found jobs in Newcastle, the UK, and around the world.

You could also continue studying for your PhD. PhD areas include applied statistics and artificial intelligence.

No matter which path you choose, you'll graduate with in-demand skills. These skills can open many doors to well-paying and engaging careers.

What our graduates do now

Our graduates have an excellent record of finding employment. Recent examples include:

• Data Scientist at the Bank of England
• Business Analyst at Prognosys Solutions in Cyprus
• Data Scientist at DWP Digital

Our Careers Service

Our award-winning Careers Service is one of the largest and best in the country, and we have strong links with employers. We provide an extensive range of opportunities to all students through our ncl+ initiative.

Visit our Careers Service website

Quality and ranking

All professional accreditations are reviewed regularly by their professional body

Recognition of professional qualifications outside of the UK

From 1 January 2021 there is an update to the way professional qualifications are recognised by countries outside of the UK

Check the government’s website for more information.

Facilities

Urban Sciences Building

The School of Computing is based in the £58 million Urban Sciences Building (USB), a flagship development located on the £350 million Newcastle Helix regeneration site in the heart of Newcastle. It brings together:

• academia
• the public sector
• communities
• business and industry

Postgraduate student facilities

As a Master's student, you'll have access to specialist teaching spaces and facilities in the USB. These are only available to postgraduate students.

Wellbeing and inclusivity are at the heart of our School. The USB has several wellbeing spaces for students, including:

• The Retreat: A sensory space with relaxing stimuli to distract from busy student life.
• Wellbeing room: Designed for relaxation and quiet time. Here you can take a moment to breathe and unwind. It can also be used by students with special medical requirements.
• Prayer room: For all faiths and none, this space can be used for prayer or quiet reflection.

Fees and funding

Tuition fees for 2025 entry (per year)

Tuition fee increases

As a general principle, you should expect the tuition fee to increase in each subsequent academic year of your course, subject to government regulations on fee increases and in line with inflation.

EU, other EEA and Swiss nationals - tuition fee status

Depending on your residency history, if you’re a student from the EU, other EEA or a Swiss national, with settled or pre-settled status under the EU Settlement Scheme, you’ll normally pay the ‘Home’ tuition fee rate and may be eligible for Student Finance England support.

EU students without settled or pre-settled status will normally be charged fees at the ‘International’ rate and will not be eligible for Student Finance England support.

If you are unsure of your fee status, check out the latest guidance here.

Scholarships

We support our EU and international students by providing a generous range of Vice-Chancellor's automatic and merit-based scholarships. See our searchable postgraduate funding page for more information. 

What you're paying for

Tuition fees include the costs of:

• matriculation
• registration
• tuition (or supervision)
• library access
• examination
• re-examination
• graduation

Find out more about:

• living costs
• tuition fees

Deposit

If you are an international student or a student from the EU, EEA or Switzerland and you need a visa to study in the UK, you may have to pay a deposit.

You can check this in the How to apply section.

Funding deadlines

If you're applying for funding, always check the funding application deadline. This deadline may be earlier than the application deadline for your course.

For some funding schemes, you need to have received an offer of a place on a course before you can apply for the funding.

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How to apply

Using the application portal

The application portal has instructions to guide you through your application. It will tell you what documents you need and how to upload them.

You can choose to start your application, save your details and come back to complete it later.

If you’re ready, you can select Apply Online and you’ll be taken directly to the application portal.

Alternatively you can find out more about applying on our applications and offers pages.

Apply Online  

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