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The MSc Data Science at Queen Mary University of London provides advanced training in statistical data modelling, machine learning, big data processing, and domain-specific applications such as computer vision and social media analysis. The program is suited for graduates in computing, engineering, or related fields who want to develop expertise in large-scale data analysis and industry-relevant technologies.
Professional alignment (accreditation):
BCS accredited (partially meeting Chartered IT Professional and Chartered Engineer requirements)
Course overview:
Stream 1: Data Science
Applied Statistics
Data Mining
Principles of Machine Learning
Natural Language Processing
Big Data Processing
Neural Networks and Deep Learning
Digital Media and Social Networks
Risk and Decision Making for Data Science and Al
Project
Stream 2: Data Engineering stream
Applied Statistics
Data Mining
Principles of Machine Learning
Cloud Computing
Big Data Processing
Neural Networks and Deep Learning
Data Semantics
Distributed Systems
Project
Research project: The final semester is dedicated to a substantial research project where students apply their skills to solve a complex real-world problem.
Teaching methods:
Modules are taught via lectures, seminars, lab sessions, and virtual learning platforms. Students benefit from hands-on labs, directed study, and regular contact with lecturers, supported by an assigned Academic Advisor.
Assessments:
Modules are assessed through coursework and written examinations (67% of the degree). The research project is evaluated via a thesis, presentation, and viva (33%).
Big data computing cluster: Access to a dedicated computing cluster for big data processing.
Informatics teaching lab: Use of over 350 computers and GPUs designed for machine learning applications.
Specialized labs: Access to the Augmented Human Interaction Lab and Robotics Laboratory.
Industry-relevant platforms: Practical use of cloud-based tools and industry software including Spark and PyTorch.
Library resources: Full access to the University of London’s Senate House library.
Industry collaboration: Projects and case studies developed in partnership with industry.
Academic advising: Personalized support from an assigned Academic Advisor throughout the program.
Lab activities and competitions: Opportunities for extracurricular lab-based activities and student competitions.
Networking and seminars: Regular events with industry professionals and academic seminars.
Graduate Centre access: Use of the £39 million Graduate Centre with advanced teaching spaces
Graduate employment: Graduates work at organizations such as IBM, Dataiku, Accenture, Blackrock, Credit Suisse, and the NHS across sectors like technology, healthcare, finance, consulting, and academia.
Further study: Students can pursue a PhD in Data Science, Computer Science, or related fields.
Advanced MSc programs: Options include Artificial Intelligence, Big Data Analytics, or Distributed Systems, building on the technical and research strengths of the MSc Data Science.
Degree Requirement: A 2:1 or above at undergraduate level in Electronic Engineering, Computer Science, Software Engineering, Information Technology or a related discipline. Other education backgrounds can be considered subject to demonstrating satisfactory knowledge of programming. Applicants with a good 2:2 degree (55% or above) will be considered on an individual basis.IELTS (Academic): minimum score 6.5 overall with 6.0 in each of Writing, Listening, Reading and SpeakingTOEFL: minimum score 92 overall with 21 in Writing, 19 in Reading, 18 in Listening and 21 in SpeakingScholarships availableThis degree is accredited by BCS, The Chartered Institute for IT, for the purposes of partially meeting the academic requirement for registration as a Chartered IT Professional. Machine Learning ResearcherData ScientistHead of Data EngineeringBig Data AnalystBusiness AnalystTechnical AnalystData EngineerResearch ScientistAI SpecialistBusiness Intelligence Analyst
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Queen Mary University of London
