Applications

Applications for Autumn 2024 entry are now open, the final application deadline is May 31. 

The course

In this programme you will develop an in-depth understanding of the mathematical and statistical foundations underlying modern machine learning methods, alongside invaluable practical skills and guided experience in applying them to real-world problems. The curriculum is designed to propel your engineering or data science career forward, allowing you to choose the path that’s right for you, be that a role as a data scientist, a machine learning engineer, or a computational statistician.

With hands-on projects, you’ll build a portfolio to showcase your new skills in everything from probabilistic modelling, deep learning, unstructured data processing and anomaly detection. You will not only build a strong foundation in Mathematics and Statistics, giving you confidence in your analytical skills, but you will also acquire expertise in implementing scalable machine learning solutions using industry-standard tools such as PySpark, ensuring that no data is too big or too complex for you.

You will also have the opportunity to broaden your horizons through one of the first of its kind study of ethical issues posed by machine learning. You will graduate with an ability to go beyond the algorithms and turn data into actionable insights, contribute to strategic decision making in your organisation and become a responsible member of this rapidly growing profession.

Course Overview

The MLDS programme will be delivered as a fully online degree. Teaching and learning on the programme will be delivered by the departmental faculty through a range of methods including recorded lectures, online tests, scheduled live tutorials, and coding exercises. You will also learn as a cohort through discussion boards (which can be used as assessment through graded discussion prompts) and peer assessed exercises.

Each academic year is divided into three terms. Complete 12 modules over two years (90 ECTS), including a research portfolio. On average, you will dedicate 21 hours per week to study.

The College’s flexible approach to learning afforded by an online degree allows us to meet the demands of our growing student base, allowing students for whom study in London or full-time study is not feasible. This will enable a broader base of the best students to access and participate in an Imperial education.