Program Overview

Introduction to the BSc Data Science Programme

The BSc Data Science programme at the London School of Economics and Political Science (LSE) is designed to equip students with the skills and knowledge required to succeed in the field of data science. This programme 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.

Programme Overview

The BSc Data Science programme is a three-year, full-time undergraduate degree that provides students with a strong mathematical focus. Students will learn about the properties of data, how to extract insights from data, and how to report the results. As they progress, they will build their understanding of classical and modern data analytics techniques, modelling, statistical machine learning, and AI.

Programme Structure

The programme consists of 12 units over three years, plus LSE100. In the first year, students will take four compulsory courses and LSE100. In the second and third years, students will take a mix of core and optional courses.

Year 1

• ST102: Elementary Statistical Theory
• MA100: Mathematical Methods
• ST101A: Programming for Data Science
• ST115: Managing Visualising Data
• LSE100: The LSE Course
• Courses to the value of one unit from the following options:
  • AC102: Elements of Financial Accounting
  • AC103: Elements of Management Accounting, Financial Management and Financial Institutions
  • EC1A3: Microeconomics I
  • EC1B3: Macroeconomics I
  • FM101: Finance
  • MA102: Mathematical Proof and Analysis
  • MA103: Introduction to Abstract Mathematics

Year 2

• ST206: Probability and Distribution Theory
• ST211: Applied Regression
• ST207: Databases
• MA214: Algorithms and Data Structures
• MA222: Further Mathematical Methods (Linear Algebra)
• If MA102 or MA103 has not been taken in Year 1:
  • MA102: Mathematical Proof and Analysis
• If MA102 or MA103 has been taken in Year 1, you take one of the following courses:
  • MA203: Real Analysis
  • MA221: Further Mathematical Methods (Calculus)
• Courses to the value of one unit from the following options:
  • EC2A3: Microeconomics II
  • EC2B3: Macroeconomics II
  • EC2C3: Econometrics I
  • EC2C4: Econometrics II
  • FM214: Principles of Finance I
  • FM215: Principles of Finance II
  • LL210: Information Technology and the Law
  • LL220: Technology Law and Regulation
  • MA103: Introduction to Abstract Mathematics
  • MA203: Real Analysis
  • MA208: Optimisation Theory
  • MA210: Discrete Mathematics
  • MA213: Operations Research Techniques
  • MA221: Further Mathematical Methods (Calculus)
  • ST205: Sample Surveys and Experiments
  • ST216: Statistical Inference
  • ST226: Mathematics for Finance and Investment
  • ST227: Survival Models

Year 3

• ST310: Machine Learning
• ST311: Artificial Intelligence
• ST312: Applied Statistics Project
• Courses to the value of one unit from the following options:
  • ST300: Regression and Generalised Linear Models
  • ST301: Actuarial Mathematics (Life)
  • ST302: Stochastic Processes
  • ST304: Time Series and Forecasting
  • ST308: Bayesian Inference
  • ST314: Multilevel and Longitudinal Models
  • ST326: Financial Statistics
  • ST330: Stochastic and Actuarial Methods in Finance
• Courses to the value of half a unit from the following options:
  • ST300: Regression and Generalised Linear Models
  • ST301: Actuarial Mathematics (Life)
  • ST302: Stochastic Processes
  • ST303: Stochastic Simulation
  • ST304: Time Series and Forecasting
  • ST307: Aspects of Market Research
  • ST308: Bayesian Inference
  • ST313: Ethics for Data Science
  • ST314: Multilevel and Longitudinal Models
  • ST326: Financial Statistics
  • ST327: Market Research: An Integrated Approach
  • ST330: Stochastic and Actuarial Methods in Finance
• Courses to the value of one unit from the following options:
  • ST300: Regression and Generalised Linear Models
  • ST301: Actuarial Mathematics (Life)
  • ST302: Stochastic Processes
  • ST303: Stochastic Simulation
  • ST304: Time Series and Forecasting
  • ST307: Aspects of Market Research
  • ST308: Bayesian Inference
  • ST313: Ethics for Data Science
  • ST314: Multilevel and Longitudinal Models
  • ST326: Financial Statistics
  • ST327: Market Research: An Integrated Approach
  • ST330: Stochastic and Actuarial Methods in Finance
  • MA301: Mathematical Game Theory
  • MA316: Graph Theory
  • MA320: Mathematics of Networks
  • MA324: Mathematical Modelling and Simulation
  • MA330: Game Theory for Collective Decisions
  • MA333: Optimisation for Machine Learning
  • EC2A3: Microeconomics II
  • EC2B3: Macroeconomics II
  • EC2C3: Econometrics I
  • EC2C4: Econometrics II
  • FM214: Principles of Finance I
  • FM215: Principles of Finance II
  • FM310: Corporate Finance, Investments and Financial Markets I
  • FM311: Corporate Finance, Investments and Financial Markets II
  • FM321: Risk Management and Modelling
  • FM322: Derivatives

Entry Requirements

To be considered for the BSc Data Science programme, applicants must meet the following entry requirements:

Home

• GCSEs: A strong pre-16 academic profile such as several GCSE grades of A (or 7) and A* (or 8-9).
• A good set of GCSE grades or equivalent across a broad range of subjects, with a minimum of grade B (or 6) in GCSE English and Mathematics.
• A-levels: AAA, with an A in Mathematics.
• Where it's offered by your school or college, AS- or A-level Further Mathematics is expected to be taken and a grade A achieved.
• Contextual admissions A-level grades: AAB with an A in Mathematics.
• A-level subject combinations: Mathematics at A-level or equivalent is required. Further Mathematics is highly desirable.
• The programme requires excellent quantitative skills. So, quantitatively oriented A-level courses such as Physics or Chemistry provide excellent preparation, although these subjects are not mandatory.
• Good marks in any quantitative courses at GCSE level are also desirable.

Overseas

• Select a country to find the equivalency to A-levels of your qualification.

Additional Tests

• 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.

Fees and Funding

The tuition fees for the BSc Data Science programme are as follows:

Home

• £9,535 per year (provisional 2026/27 tuition fee for Home students).

Overseas

• £32,100 per year (2026/27 fee for each year of your programme).

Learning and Assessment

The programme is taught through a mix of lectures, classes, seminars, and workshops. Students will also have the opportunity to work on projects and participate in group work.

How You Learn

• Teaching: You’ll usually attend a mix of lectures and related classes, seminars or workshops totalling 10 to 15 hours per week.
• Independent study: You’ll need to complete independent study outside your classes.
• LSE teaching: LSE is internationally recognised for teaching and research and our academics have wide-ranging expertise.

How You're Assessed

• Formative coursework: All taught courses include formative coursework, which is not assessed.
• 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.

Graduate Destinations

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.

Top 4 Sectors Our Students Work In:

• Financial and Professional Services
• Insurance
• Information, Digital Technology and Data
• Accounting and Auditing

Why Study with Us

The Department of Statistics at LSE is one of the oldest and most distinguished in the UK. Our research spans four main areas – data science, probability in finance and insurance, social statistics, and time series and statistical learning.

Student Stories

• LSE has taught me the value of pushing boundaries and seizing every opportunity, shaping my journey toward excellence.
• Liya Don, Kazakhstan, BSc Data Science

Meet the Department

• 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.

Why LSE

• University of the Year 2025 and 1st in the UK (Times and The Sunday Times - Good University Guide 2025)
• 1st in London for the 14th year running (The Complete University Guide - University League Tables 2026)
• 6th In the world (QS World University Rankings by Subject 2025)
• Carbon Neutral In 2021, LSE became the first Carbon Neutral verified university in the UK

Your Application

We consider each application carefully, taking into account all the details you’ve included on your UCAS form.

Overview

• 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
  • Subjects and subject combinations
  • Your personal statement
  • Your teacher’s reference
  • Educational circumstances

Who Attends

• 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

Discover Uni

Every undergraduate programme of more than one year duration will have Discover Uni data. The data allows you to compare information about individual programmes at different higher education institutions.

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