MSc Data Science

The MSc Data Science programme offers in-depth training at the forefront of machine learning and data science. We seek applicants with a solid quantitative background and a strong interest in statistics, mathematics, and programming.

The programme offers a unique opportunity for prospective students: the theoretical foundations and computational skills you'll acquire will enable you to deploy state-of-the-art data science methods and gain a thorough understanding to tackle various real-world challenges across different application domains at scale. The programme's integration within LSE will also guide your attention towards socially relevant and impactful problems.

The curriculum requires four mandatory courses, including a Capstone project, and three elective courses. The compulsory courses cover fundamental aspects of modern data analysis from both computational and statistical perspectives. The optional courses will enrich you with additional in-depth knowledge in areas such as Artificial Intelligence, Deep Learning, Reinforcement Learning, Bayesian Machine Learning, Distributed Computing for Big Data, Graph Data Analytics and Representation Learning, Time Series Analysis, and Financial Statistics. During the computer seminars accompanying the lectures, you'll gain hands-on experience in numerous applications and develop skills in using modern computing systems and software frameworks.

Within the Capstone Project, you'll have the opportunity to apply your acquired skills to real-world data science problems in a team and to interact directly with industry partners. You'll be jointly supported by one of our industrial partners and an academic with leading expertise from the department. In recent years, our Capstone Project partners have included companies such as Google, Microsoft, Facebook/Meta, Adobe, Samsung, Koa Health, AstraZeneca, Capgemini, Siemens Advanta Consulting, ADIA, Wise, Deutsche Bank, Houghton Street Ventures, Experian DataLabs, KPMG, Tesco, Plymouth Marine Laboratory, Alpha Telefónica, Westminster City Council, and the Thames Valley Violence Reduction Unit. Our capstone projects covered a wide range of application domains, including healthcare, marine and climate research, sustainability, agriculture, transportation, logistics, student well-being, entrepreneurship, and finance.

You'll take four compulsory courses, including a Capstone Project. You'll also select optional courses to the value of one and a half units from a list of options.

The Capstone Project will provide you with the opportunity to study in depth a topic of specific interest. The topic may be identified from a list supplied by the department or may be proposed by you. The topic will normally relate to a specific data source or sources and will require the use of data science skills learnt on the programme. The topic for a Capstone Project will be similar to that for the kinds of data-based issues faced in practice by private or public sector organisations. The focus is likely to be practical and there may be the opportunity to liaise with such an organisation during the project to ensure the project has practical relevance. A Capstone Project will be more academic; it'll refer to a research literature and address a research question, building on that literature and using the data source(s).

The Department of Statistics at LSE is one of the oldest and most distinguished in the UK.

The department has an international reputation for the development of statistical methodology and a long history of pioneering contributions to research and teaching. Many of the world’s most famous and innovative statisticians have been associated with LSE.

Our research spans four main areas – data science, probability in finance and insurance, social statistics, and time series and statistical learning. The department has close links with the Data Science Institute at LSE – an interdisciplinary institute that fosters the study of data science, with a particular emphasis on the social, economic and political contexts.

All aspects of our research were ranked world-leading or internationally excellent in the most recent Research Excellence Framework (2021), submitted jointly with the Department of Mathematics.

The department’s research strengths are reflected in our teaching curriculum. We have a comprehensive range of undergraduate and postgraduate degrees and doctoral research opportunities – hosted in a vibrant department with a supportive and friendly community of staff and students.

Our alumni enjoy successful careers in diverse areas, such as banking, accounting, finance, statistics, government and business consulting while others pursue postgraduate study or research. Our alumni frequently return to LSE to share their career experiences with current students.

Learn more about our programmes and research.

Department of Statistics

We welcome applications from all suitably qualified prospective students. At LSE, we want to recruit students with the best academic merit, potential and motivation, irrespective of background.

We carefully consider each application and take into account all the information included on your application form, such as your:

• academic achievement (including predicted and achieved grades)
• statement of academic purpose
• two academic references
• CV.

See further information on supporting documents.

You may need to provide evidence of your English language proficiency. See our English language requirements.

This programme is available as part of an ESRC-funded pathway onto a PhD programme. The 1+3 scheme provides funding for a one year research training master's linked to a PhD programme and is designed for students who have not already completed an ESRC recognised programme of research training. An application must be submitted for the relevant master’s programme, including a research proposal for the PhD aspect of the pathway. Applicants must also indicate their wish to be considered for the 1+3 pathway within their personal statement.

Personal statement requirements

Your statement of academic purpose should state why you want to do the programme applied for and why you have chosen LSE. Brief details of your academic background and aspirations are also useful. If your background is outside of mathematics or statistics then you should provide further explanation of how your experience is relevant to the programme applied for; as well as further details of your current studies.

If you're applying to study on a part-time basis, please ensure that you address the following in your statement of academic purpose:

• your motivations to study part-time
• how you will balance the demands of studying with part-time work (if applicable)
• confirmation that you have the support of your employer (if applicable).

Your statement of academic purpose should be concise and should not exceed 500 words.

If you're applying for more than one choice in the Department of Statistics, it's recommended that you submit two separate statements of academic purpose. If the two programmes for which you're applying are very similar and you'd prefer to combine the information in one statement then you may do so; however, please ensure that your statement clearly addresses your motivations for applying for each separate programme.

Applications for this programme are considered on a rolling basis. This means that applications will close once the programme is full.

There is no fixed deadline. However, if you’d like to be considered for any funding opportunities, you must submit your application (and all supporting documents) by the funding deadline. See the fees and funding section below for more details.

For this programme, all students pay the same tuition fees, regardless of their fee status.

For this programme, all students pay the same tuition fees, regardless of their fee status.

All taught courses are required to include formative coursework which is unassessed. It's designed to help prepare you for summative assessment which counts towards the course mark and to the degree award.

The programme will incorporate diverse forms of summative assessment, including some conventional assessment by written examination in Spring Term, but also a range of other kinds of assessment of varying size, reflecting the fundamentally computational nature of the subject matter.

There'll be shorter take-home exams for which an invigilated exam would be unrealistic given the computer applications involved. There'll be smaller projects, both individual-based and group-based, which enable practical problem-based learning to take place.

Finally, the capstone project/dissertation will assess your ability to take on large-scale data-based problem solving.

An indication of the formative coursework and summative assessment for each course can be found in the relevant course guide.

Data scientists are much in demand across industry, including a variety of Internet online service companies, marketers, banks, investment management, and other financial companies.

Data scientist positions involve a wide range of responsibilities; such as conducting exploratory data analysis, applying statistical methodologies, deriving business insights from data, partnering with company executives, product and engineering teams to solve problems, identify trends and opportunities, inform, influence, support, and execute product decisions and launches.

Further information on graduate destinations for this programme

From CV workshops through to careers fairs, LSE offers lots of information and support to help you make that all-important step from education into work.

Many of the UK’s top employers give careers presentations at the School during the year and there are numerous workshops covering topics such as job hunting, managing interviews, writing a cover letter and using LinkedIn.

See LSE Careers for further details.