Research course

Data Science

Institution
University of Essex · Department of Mathematical Sciences
Qualifications
Integrated PhD

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Entry requirements

We will consider all applicants with 2:2 or above, or equivalent international qualifications. For some courses, there may be additional requirements which can be found on our website.

Months of entry

October

Course content

An Integrated PhD provides a route into research study if you do not have a Masters degree, or have very little research training. It enables you to spend your first year completing a Masters-level qualification, followed by a full-time PhD studied over 3-4 years. We also offer a ‘standard’ PhD in this subject which can be studied either full-time (3-4 years) or part-time (6-7 years).

The techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society and are the basis of growth of the economy and success of businesses. Technology is growing and evolving at an incredible speed, and both the rate of growth of data we generate and the devices we use to process it can only increase. Data science is a growing and important field of study with a fast-growing number of jobs and opportunities within the private and public sector. The application of theory and methods to real-world problems and applications is at the core of data science, which aims especially to use and to exploit big data. If you are interested in solving real-world problems, you like to develop skills to use smart devices efficiently, you want to use and to foster your understanding of mathematics, and you are interested and keen to use statistical techniques and methods to interpret data, then the first year of our Integrated PhD Data Science may be for you. In your second year you move onto the PhD element of the course. We have staff members available to act as supervisors across a number of areas within data science. Possible areas of research include: artificial intelligence, classification/supervised learning, clustering/unsupervised learning, data science education, deep learning, industry 4.0, information retrieval, mathematical foundations of data science, multidimensional scaling, optimisation, and statistical learning. The University of Essex is a leading institution worldwide on Data Science Education. We have a strong track record on Knowledge Transfer Partnerships (KTP) with data-driven industries, for example: Profusion, Mondaq, MSXI and Ocado. We have two research groups: Data Science and Mathematics. All University of Essex research students have access to our innovative and unique scheme, Proficio. Postgraduate research students are automatically enrolled on Proficio, which provides a variety of training courses, and a fund of up to £2,500 per student for conference attendance and relevant external training courses.

Information for international students

Entry requirements - You can find international entry requirements for most countries on our webpage www.essex.ac.uk/international. If your country is not listed, please contact Postgraduate Admissions at pgadmit@essex.ac.uk.

Pre-sessional English Language courses - If you need to improve your English language skills before joining your main degree course, we offer pre-sessional English courses on our campuses in partnership with Kaplan International Pathways. To learn more, please visit www.essex.ac.uk/international/pre-sessional

Funding - You can explore scholarship opportunities for your country at www.essex.ac.uk/international or view our university-wide scholarships at www.essex.ac.uk/scholarships

Fees and funding

There are a wide range of funding options for postgraduate study, including scholarships, bursaries and alumni discounts. You can check your eligibility at https://www.essex.ac.uk/postgraduate/research/fees-and-funding.

Qualification, course duration and attendance options

  • Integrated PhD
    full time
    36 months
    • Campus-based learningis available for this qualification

Course contact details

Name
PG Admissions
Email
pgadmit@essex.ac.uk