
Year
01 / 03
1
Year 1
Compulsory first-year core; individual module names are not supplied in the verified Stage 5 reference data.
概要
Economics, Finance and Data Science at Imperial College London (UCAS code LG11) is a three-year BSc run by Imperial College Business School. The course combines core economics with quantitative finance, statistics, programming and data science, preparing graduates for analytical roles in finance, technology and policy. Imperial requires the TMUA for this course; there is no interview as part of standard selection.
なぜImperialでEconomics, Finance and Data Scienceを?
Imperial's verified page names the course BSc Economics, Finance and Data Science, and it as a 3-year full-time BSc starting in October 2027. The verified UCAS course code is L1N3, and Imperial's institution code is I50.

Section 01
下のマップで自国をクリックすると、出願に必要な情報(受け入れられる資格、要求スコア、英語要件、現地の文脈)が表示されます。
International Applicants
Pick a highlighted country to see the admissions-test, score, and English-language requirements that apply for applicants from that country.
Section 02
| Qualification | Typical Offer | Key Requirements |
|---|---|---|
| A-Level | A*AA | Mathematics required. Further Mathematics, Economics recommended. General Studies, Critical Thinking not accepted.A* in Mathematics |
| IB Diploma | 39 points | HL: Mathematics required.7 in Mathematics at HL and 6 in two further HL subjects |
| Advanced Placement (AP) | 5, 5, 5 | Calculus BC, two other subjects required. SAT/ACT: ACT/SAT not accepted for undergraduate entry.5 in Calculus BC and 5 in two other subjects |
Section 03
May 2026
Applications open
Applications open on 12 May 2026.
June–July 2026
TMUA account and October booking
TMUA account creation opens on 1 June 2026 at 15:00 BST; October booking opens on 20 July 2026 at 15:00 BST.
September 2026
UCAS submission opens; October TMUA closes
UCAS submission opens on 1 September 2026; October TMUA registration closes on 28 September 2026 at 18:00 BST.
October 2026
October TMUA window
TMUA runs from 12–16 October 2026; China, Hong Kong and Macau dates are 15–16 October 2026.
November 2026
October TMUA results released
October TMUA results are released on 16 November 2026.
December 2026
January TMUA registration closes
January booking opens on 26 October 2026 and registration closes on 21 December 2026 at 18:00 GMT.
January 2027
January TMUA and UCAS deadline
TMUA runs from 4–8 January 2027; the UCAS deadline is 13 January 2027 at 18:00 UK time.
February 2027
January TMUA results released
January TMUA results are released on 8 February 2027.
March–May 2027
Decision aim and reply dates
UCAS provider decision aim is 31 March 2027; reply deadline is 5 May 2027 if all decisions are received by 31 March and the applicant is not using Extra; provider reject-by-default date is 12 May 2027.
May 2026
Applications open
Applications open on 12 May 2026.
June–July 2026
TMUA account and October booking
TMUA account creation opens on 1 June 2026 at 15:00 BST; October booking opens on 20 July 2026 at 15:00 BST.
September 2026
UCAS submission opens; October TMUA closes
UCAS submission opens on 1 September 2026; October TMUA registration closes on 28 September 2026 at 18:00 BST.
October 2026
October TMUA window
TMUA runs from 12–16 October 2026; China, Hong Kong and Macau dates are 15–16 October 2026.
November 2026
October TMUA results released
October TMUA results are released on 16 November 2026.
December 2026
January TMUA registration closes
January booking opens on 26 October 2026 and registration closes on 21 December 2026 at 18:00 GMT.
January 2027
January TMUA and UCAS deadline
TMUA runs from 4–8 January 2027; the UCAS deadline is 13 January 2027 at 18:00 UK time.
February 2027
January TMUA results released
January TMUA results are released on 8 February 2027.
March–May 2027
Decision aim and reply dates
UCAS provider decision aim is 31 March 2027; reply deadline is 5 May 2027 if all decisions are received by 31 March and the applicant is not using Extra; provider reject-by-default date is 12 May 2027.
Section 04

Imperial verifies the Test of Mathematics for University Admission (TMUA) as mandatory for this course in 2027 entry.
TMUA is run by UAT-UK and delivered through Pearson VUE test centres. The required papers are Paper 1: Applications of Mathematical Knowledge and Paper 2: Mathematical Reasoning.
The test window is 12–16 October 2026 or 4–8 January 2027, with China, Hong Kong and Macau dates listed as 15–16 October 2026 and 8 January 2027. Account creation opens on 1 June 2026 at 15:00 BST, October booking opens on 20 July 2026 at 15:00 BST, and January booking opens on 26 October 2026 at 15:00 GMT.
October registration closes on 28 September 2026 at 18:00 BST, and January registration closes on 21 December 2026 at 18:00 GMT. Results are released on 16 November 2026 for the October sitting and 8 February 2027 for the January sitting.
For international applicants, TMUA gives Imperial a common mathematics measure across school systems. Treat it as a core part of preparation, not as an administrative extra.
TMUA完全対策ガイド | 試験形式・採点・戦略・練習リソース。
TMUAガイド →Section 05
Interview Invitation
Late Nov
Arrival to Interview
Early Dec
Technical Question
Mid Dec
Decision
Early Jan
Interview Invitation
Late Nov
Arrival to Interview
Early Dec
Technical Question
Mid Dec
Decision
Early Jan
The interview is verified as one online interview with an academic lasting 20–30 minutes.
Do not prepare for this as a personality test. Strong preparation means practising clear mathematical explanation, calm handling of unfamiliar prompts, and precise discussion of why economics, finance and data science belong together in your application.
Offer conditions are determined after interview. That means the interview sits inside the decision process, rather than being a decorative extra after the academic checks.
無料のEconomics, Finance and Data Science面接練習問題バンクで本番さながらの問題を練習しましょう。
無料練習問題 →
Section 06
The verified decision components are academic achievement, TMUA, personal statement or subject fit, online interview and contextual information.
The 2024 entry figures record 3,036 applicants, 226 offers and 117 acceptances.
Eligible Home-fee widening-participation applicants receive an automatic interview invite if predicted AAA including A in Mathematics. In reality, that does not lower the need for strong mathematical preparation; it changes how the application is read in context.
Our recommendation · weighting of admission factors
Oxbridge Mentors recommendation, drawn from observed offer patterns. Imperial College London does not publish official weightings — exact balance varies by college, course and year.
Section 07

Imperial does not identify a separate written-work submission or additional course-specific personal statement for this course; the UCAS personal statement carries the subject-fit argument. That statement therefore needs to explain economics, finance and data science as one coherent choice, not as three unrelated interests.
Avoid a generic “I like business and technology” structure. A stronger statement shows one economic question, one quantitative method and one financial or policy application, then explains what you learned from connecting them.
It helps to write about process rather than achievement alone. For example, explain how you tested an assumption, changed a model, handled messy data or found a limitation in a neat economic story.
A practical structure is to ask the same question in three ways: what is the economic mechanism, what data would test it, and what financial or policy decision would change if the answer were different?
専門家による一行一行の解説付き完全例文を見る。
Economics, Finance and Data Science PS例文 →Section 08
This draft should therefore avoid publishing named project prompts as if they came from the official record.
A strong supercurricular project for this course usually starts with a question that can be analysed with data. The useful evidence is not the topic itself; it is the way you define the question, choose a method and reflect on what the method cannot show.
For example, an applicant might explore whether interest-rate changes are associated with shifts in consumer spending, using public data and a simple model. The important admissions value would be explaining the assumptions, confounders, limits of the dataset and what would be needed for a stronger conclusion.

Section 08
It is better to publish a shorter honest section than to pad the page with unverified recommendations.
Useful activity can still include reading, data work, mathematical problem solving and careful reflection. These are support, not substitute.
Section 08
No competition names are therefore listed in this draft.
Competitions are not required. In reality, one or two well-explained attempts beat five half-attempted entries.
Section 09

Year
01 / 03
1
Compulsory first-year core; individual module names are not supplied in the verified Stage 5 reference data.

Year
02 / 03
2
Compulsory second-year core; individual module names are not supplied in the verified Stage 5 reference data.

Year
03 / 03
3
Third year includes a final group project, I-Explore and five electives.
Section 10
This section should therefore not publish named resources as if they were official admissions facts.
Build subject knowledge through a small number of well-documented activities. Keep a record of the question, method, source of data, limitation and conclusion for each piece of work.
For this course, the strongest preparation links economics, finance and mathematics. A useful habit is to keep asking how a real economic question would become a measurable data question, and how the answer would affect a financial, business or policy decision.
Misbehaving: The Making of Behavioural Economics by Richard Thaler is an accessible route into the behavioural critique of standard economics. For the quantitative side The Quant by Scott Patterson shows how mathematics transformed financial markets from the 1980s onwards.
For video, Marginal Revolution University delivers short, rigorous economics lectures across micro, macro and development. Planet Money and EconTalk build the habit of connecting economic models to real cases, which is essential preparation for data science and finance discussions.
For data and programming practice Kaggle offers datasets and structured learning tracks in machine learning and data analysis. Machine Learning for Trading on Coursera introduces the quantitative finance applications that link directly to the Data Science strand of this course.

Section 11
Graduate sectors including technology, finance, consulting, public sector, central banks, regulatory bodies, think tanks and international organisations. It also records a Discover Uni outcome of 95% work or study 15 months after the course, based on other Business studies graduates rather than course-specific EFDS data.
That caveat matters. Use the sector list as a direction-of-travel indicator, not as a promise of course-specific destinations.
Section 12
It also verifies that offer conditions are determined after interview.
Contextual information. Use it to explain school context, disruption or subject availability clearly, while keeping the academic case focused on Mathematics, TMUA and subject fit.
Watch & Learn
学生ブログ・模擬面接・講義体験・入試アドバイス。
Student reflections on the EFDS programme.
Application and interview advice from EFDS students.
Programme overview video from Imperial Business School.
All videos are the property of their respective creators.
Further Reading
専門講師が推薦するSupercurricular読書リスト・ウェブサイト・ツール。