BSc Data Science

Our BSc Data Science brings together the study of data science, machine learning, statistics and mathematics – with an emphasis on their real-world applications and impact on economics and society.

The programme has a strong mathematical focus. You’ll learn about the properties of data, how to extract insights from data, and how to report the results. As you progress, you’ll build your understanding of classical and modern data analytics techniques, modelling, statistical machine learning, and AI. You’ll learn how to carry out typical workflows in data analytics, make inferences based on statistical data and become proficient in computer programming for data science.

Data scientists are in high demand in the private and public sectors. By the time you graduate, you’ll have all the practical skills and theoretical knowledge to progress to a career in areas such as banking, financial services and healthcare.

• J.Zelle, Python Programming: An Introduction to Computer Science, 3rd Edition, Franklin, Beedle & Associates, 2016
• M. Lutz, Learning Python, 5th Edition, O’Reilly Media, 2013
• R. Ramakrishnan and J. Gerhke, Database Management Sytems, McGraw-Hill, 2002
• J. Hellerstein and M.Stonebraker, readings in Database Systems, 4th Edition, 2005
• W. Mckinney, Python for Data Analysis, 2nd Edition, O’Reilly 2017
• H. Wickham, Ggplot2: Elegant Graphics for Data Analysis, Springer, 2009
• Larsen R.J. and M.L. Marx (2013) Introduction to Mathematical Statistics and Its Applications (fifth edition), Pearson (earlier editions are also acceptable)
• A. C. Muller and S. Guido, Introduction to Machine Learning with Python, O’Reilly, 2016
• A. Geron, Hands-on Machine Learning with Scikit-Learn & TensorFlow, O’Reilly, 2017
• M. Wooldridge, An Introduction to MultiAgent Systems, 2nd Edition, Wiley, 2009
• L. Deng and D. Yu, Deep Learning: Methods and Applications, Now Publishers Inc, 2014
• F. Chollet, Deep Learning with Python, Manning, 2018
• A. Geron, Hands-on Machine Learning with Scikit-Learn and Tensorflow, O'Reilly, 2017

Applicants are encouraged to take the Test of Mathematics for University Admission (TMUA). The test is not mandatory, however a good performance on the test may make an application more competitive.

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 consider each application carefully, taking into account all the details you’ve included on your UCAS form, such as:

• academic achievement, including predicted and achieved grades (also see specific information about this programme in the "entry requirements" above)
• subjects and subject combinations (also see specific information about this programme in the "entry requirements" above)
• your personal statement
• your teacher’s reference
• educational circumstances

You may also have to provide evidence of your English proficiency, although this is not needed at the application stage. See our English language requirements page.

We’re looking for students who demonstrate:

• outstanding mathematical abilities
• interests in data science and using programming to analyse data
• involvement in related extra-curricular activities, such as maths competitions or Olympiads
• independent thinking
• intellectual curiosity and the ability to ask incisive questions
• creativity and flexibility in problem-solving
• self-motivation and a willingness to work hard

Formative coursework

All taught courses include formative coursework, which is not assessed. This helps prepare you for summative assessment. We use a wide range of formative assessment methods, such as essays, case studies, reports, quizzes and mock exams.

Feedback on coursework is an essential part of the learning experience. Class teachers mark formative coursework and feedback is normally given within two weeks – provided the work is submitted on time.

Summative assessment

This assessment counts towards your final course mark and degree award.

Most courses are assessed by a three-hour exam in June. A small number of courses are assessed by project work. Your degree award will be based on assessment over all three years, with particular emphasis on your work in the second and third years.

You’ll receive feedback on summative coursework as part of the assessment for individual courses (except on final submitted dissertations). Feedback is normally provided before the examination period.

Assessment on individual courses can change from year to year.

Find out more about LSE’s teaching and assessment methods.

Graduates from the programme will be prepared for further study, or for professional and managerial careers, particularly in areas requiring the application of quantitative skills.

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.