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Data Science (with Specialisation in Statistics), MSc, PG Cert, PG Dip

Newcastle University, the United Kingdom

Newcastle University

Study options for this course

The award How you will study Study duration Course start Domestic course fees International course fees
MSc, PG Cert, PG DipFull-time, Part-time8 - 18 monthsfind outfind outfind out

About Data Science (with Specialisation in Statistics), MSc, PG Cert, PG Dip - at Newcastle University

MSc - full time: 12 months, part time: 24 months
PGDip - full time: 8 months,  part time: 24 months
PGCert - full time: 8 months, part time: up to 18 months

Our Data Science (with Specialisation in Statistics) MSc gives you the knowledge, experience, and expertise to solve real-world problems and realise data-driven insights for organisations.

About this course

Our MSc blends modern statistical methods together with the associated computational skills that are essential for handling large quantities of unstructured data. 

Data Science is revolutionising every area of science, engineering and commerce. It offers the potential for huge societal and economic benefits. The Data Science (with Specialisation in Statistics) MSc, was created to address an industry need for statistical analysis alongside data analytics in order to interpret and extract information and value from complex data.

What you'll learn

Through this course you'll receive a comprehensive grounding in theory and application of data science. You'll also develop the multi-disciplinary combination of skills in statistics and computer science. You'll also gain the ability to apply these skills to real problems in a given application area.

Topics covered in the course include:

  • data visualization
  • cloud computing
  • Bayesian statistics
  • machine learning.

Your development

We have substantial expertise in data science, focusing on a wide range of application areas. This includes:

  • healthcare
  • transport
  • cybersecurity
  • smart cities
  • manufacturing.

We are home to the UK’s National Innovation Centre for Data (NICD). We are 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.

You will be encouraged to play a full part in the life of the School of Computing, including:

  • taking advantage of dedicated computing and study facilities
  • participating in seminars delivered by researchers and distinguished external speakers.

Project work

You will undertake individual and group-based projects. You will work in collaboration with regional and national industry and charitable organisations.

Your five-month individual project gives you an opportunity to:

  • develop and deepen 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 will have one-to-one supervision from an experienced member of staff, supported with supervision from industry partners as required.

Delivery

The School of Computing and School of Mathematics, Statistics and Physics deliver the course. The course starts in mid-September. You will be taught in modern facilities in the newly-opened Urban Sciences Building. The course has three phases.

In phase one you’ll be introduced to core knowledge and skills in statistics and computer science. These modules are taught as an intensive block. Pairs of modules will be taught concurrently over four weeks of lectures and lab classes. Teaching is timetabled to accommodate participants from industry, working alongside full-time employment.

Phase two will present further advanced technical modules. You will be introduced to the aspects that underlie all areas of data science practice including:

  • professionalism
  • legislation
  • ethics.

This phase also includes a group project in collaboration with industry. You'll develop and evaluate a data science solution to a complex, real-world problem.

Phase three is an individual research and development project. You'll receive personal supervision in one of the School’s research labs in collaboration with industry or with your current employer.

You'll be assessed by a portfolio of practical work, accompanied by an oral interview. There will be no written examinations as part of the Data Science MSc.

If you’re a part time student, you have the flexibility to study over two years. The part time version of the course encourages participation of practitioners from industry. As a part time student you can:

  • align your assessed work with the priorities of your job role
  • carry out your individual project in your place of work (as long as the supervisory processes in place meet University standards).

Facilities

You'll be taught on the Newcastle Helix campus which brings together:

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

You will benefit from modern teaching facilities within the £58m Urban Sciences Building, including a purpose-built Decision Theatre and 3D visualisation facility.

For full How to Apply details, see Programme information online.

Notes about fees for this course

See Fees and Funding tab in the Programme information in our online Prospectus.

Entry requirements

A 2:1 BSc honours degree, or international equivalent, in:

  • mathematics
  • statistics
  • operational research
  • other mathematical Sciences.

We have a strong track record of admitting applicants from a non-standard background and individuals with strong relevant work experience are encouraged to apply and will be considered on an individual basis.

English Language Requirements    

Direct Entry: IELTS 6.5 overall (with a minimum of 5.5 in all sub-skills). If you have lower English Language scores, you may be accepted onto a Pre-sessional English course.

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