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Data Science Pathways for International Students

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Master of Science in Connected Intelligent Embedded Systems - MGCER, India and ESIGELEC Pathways Programmes

ESIGELEC, Graduate School of Engineering France

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Looking for your first degree in Data Science? See All Bachelors degrees in Data Science

Alternatively if you are looking for graduate level options? See All Masters degrees in Data Science

Study a Data Science Pathways or Foundation Programme in 2025

Data science as a subject is multi-disciplinary, and covers more than one area. As a data scientist, you will use scientific and mathematical methods, algorithms, processes and systems to extract, organise and analyse data. Data sets come in all sizes, and can be both structured and unstructured, and you will be responsible for understanding how to work with that data. Data scientists can help to solve problems, make predictions, and aid growth in business.

Undergraduate

An undergraduate degree in data science will give you a good foundation of knowledge in the main areas of the subject. You will cover the basic theoretical and practical aspects of data science. Modules you study might include an introduction to programming, maths statistics, data analysis and statistical methods.

Your degree will be delivered in a mixture of modes. These will include lectures and seminars, as well as practical and computer laboratory based sessions. You may be presented with the opportunity to take part in a placement module, which would allow you to gain real-world experience as a data scientist.

Postgraduate

A postgraduate degree in data science will allow you to build on the knowledge you gained at undergraduate level. The modules you study will cover more specialist and complex areas of data science. These modules could include advanced programming, databases, data management, web application development and software development.

Your degree will be delivered through lectures and seminars, as well as incorporating practical sessions. Your course will likely be focused on preparing you for your final dissertation or data science project, as well as equipping you with skills to help develop your career.

Depending on where you choose to study, you may be able to specialise throughout your degree. The specialisations you choose could influence the area in which you wish to work after you have graduated. It can also help you to decide which area you wish to focus your final project on. Common specialisations include:

  • Machine Learning
  • Big Data
  • Statistical and Computational Data Science
  • Artificial Intelligence
  • Stochastic Processes

The accreditation of your degree will depend on where you choose to study. It will also be influenced by your level of study. At undergraduate level, you can expect to be awarded a Bachelor of Science (BSc), or a Bachelor of Computing (BComp). At postgraduate level, you can expect to be awarded a Master of Science (MSc), or a Master of Computing (MComp). If you choose to study for a postgraduate research degree, you can expect to be awarded a Master of Research (MRes), a Master of Philosophy (MPhil), or a Doctor of Philosophy (PhD).

Generally, an undergraduate degree will take three to four years to complete. Foundation degrees, diplomas and certificates can last up to two years, when studied full-time. A postgraduate degree will normally take one to two years. If your course includes a placement, this will normally add another year onto your studies.

Some institutions might offer part-time study options. Studying for a degree part-time normally means that your course will last anywhere between three and eight years, with some universities offering more flexible study options.

Graduates of a data science degree will find that there are a wide variety of career opportunities available to them. If you choose to work in a role directly related to your degree, jobs could include a data scientist, data analyst, or data engineer. If you choose to work in a role not directly related to your degree, jobs could include business analyst, applied mathematician, or statistician.

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