FTS UK public procurement

MSc Data Science & Machine Learning

Department for Work & Pensions

CompleteOCDS ID ocds-h6vhtk-05a657
Tender valueNot supplied1 awards · 1 contracts

01 Procurement

Tender overview

Updated 2 October 2025
Buyer
Department for Work & Pensions
Status
Complete
Procurement method
Below threshold - without competition
Category
Not supplied
CPV classification
Not supplied
Tender period
Not supplied — Not supplied

Description

The objective of the course is to accelerate a career in engineering by building a portfolio in everything from probabilistic modelling to anomaly detection. Through a variety of comprehensive modules, the aim is to gain fluency in both R and Python and combine this with a strong mathematical course in order to fully understand the techniques behind deep learning and computation using Bayesian methods. The course is delivered fully online through a combination of live sessions, recorded lectures and ongoing assessment feedback. The learning is required from Sep 2025-Sep 2026. Processed by SSCL

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This page represents one OCDS contracting process. Notices can be revised over time. Values may be estimates, award values or contract values and should be read with their displayed stage and date.

02 Awards and contracts

Published award records

1 shown
Active

MSc Data Science & Machine Learning

Awarded
Not supplied
Award value
Not supplied
Suppliers
1
Signed contract
1 October 2025

03 Organisations

Buyers, suppliers and other parties

2 parties
Buyer

Department for Work & Pensions

Caxton House 7th Floor 6-12 Tothill Street, London, UKI32, SW1H 9NA, United Kingdom

GB-PPON PJCP-7274-TLRQ
Supplier

IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE

The faculty Building, Imperial College London, Exhibition Road, London, UKI32, SW7 2AZ, United Kingdom

Companies House RC000231 →

Companies House links appear only for explicit GB-COH identifiers. BritDB does not create company links from a similar name or address.

04 Provenance

Source for this process

Contains public sector information licensed under the Open Government Licence v3.0. Official external links are available only on the source transition page.