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King’s Unveils Maths Course Designed for Career Readiness

Tom Page, CC BY-SA 2.0 <https://creativecommons.org/licenses/by-sa/2.0>, via Wikimedia Commons (https://commons.wikimedia.org/wiki/File:King%27s_College_London_flag.jpg)

King’s College London (KCL) announced the launch of a new undergraduate Mathematics degree designed to better prepare students for employment.

The University has said that Mathematics for Quantitative Problem-Solving BSc will be “designed to equip students with the skills and experience needed to thrive in an increasingly complex world and competitive job market.”

The Department of Mathematics is noted for its partnerships with major employers, including NASA, Microsoft and Santander.

The degree will not require students to have taken A-Level Further Mathematics, differing from the university’s Mathematics BSc course. The University stressed this marks a part of their wider attempts to attract students from under-represented backgrounds.

A 2023 report from Imperial College London on A-Level Further Maths noted that “it is clear that students’ school/college attended, gender, ethnicity and social class impacts upon participation.”

The course will commence in the 2027/28 academic year, with applications opening in September the year before.

The launch of the new degree follows KCL’s strong performance in recently published major university rankings, reaching 31st in the 2026 QS World University Rankings, rising from 40th in the 2025 league tables.

In the rankings, King’s performed strongly in its Employer Reputation score of 91.8, with the new Mathematics degree set to solidify the university’s global recognition among recruiters.

Professor Anne Taormina, Head of the Department of Mathematics and lead for the project, said:

“Mathematicians are among the world’s great problem-solvers. Their ability to combine creative and deductive thinking in data-rich environments makes them highly employable across a diverse range of sectors. The demand for such skills is rising, particularly in areas like big data, machine learning, and AI.”

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