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Different course options

Full time | Mile End | 1 year | 18-SEP-23

Study mode

Full time


1 year

Start date


Key information

Qualification type

MSc - Master of Science

Subject areas

Database Management Data Science

Course type


Course Summary

Companies, users and devices generate enormous volumes of unstructured data. Train to become a data scientist – a highly skilled professional who is able to combine state-of-the-art computer science techniques with modern methods of statistical analysis.

  • Extract understanding from data and create new services that are based on mining the knowledge within this information
  • Learn about how to create automated prediction, recommendation and classification services
  • Gain skills for this highly-sought-after career path, where demand is expected to increase significantly over the coming years
  • Benefit from the world-leading expertise in research at Queen Mary as well as our strategic partnership with IBM and other leading IT sector companies
  • Learn from academics in our research groups, including: Networks, Centre for Intelligent Sensing, Risk and Information Management, Computer Vision and Cognitive Science


The School has excellent bespoke facilities, including:

  • Augmented human interaction (AHI) laboratory
  • Dedicated computing cluster for Big Data processing jobs
  • Informatics teaching laboratory with 350 state-of-the-art computers, and GPUs for machine learning
  • Antenna measurement laboratory
  • Listening room
  • Media and arts technology studios
  • Performance lab
  • Robotics laboratory (ARQspace)

Career paths

Our postgraduates go on to work in a wide variety of careers, mostly within IT and information services. The broad range of skills gained through programmes in this School, coupled with multiple opportunities for extra-curricular activities and work experience, has enabled postgraduates to move into careers such as:

  • Machine learning researcher
  • Data scientist
  • Head of data engineering
  • Big data analyst
  • Analyst
  • Technical analyst.


The module covers the theoretical underpinnings and practical applications of Neural Networks and automatic differentiation as a tool for modern AI. Neural Networks & Deep Learning are now the method of choice for solving various Machine Learning problems. They are applied to several real-world problems not only within Academia but most importantly within Industry. Knowledge of Neural Networks and how to apply them to solve practical problems is now considered one of the most essential skills in the job market for a CS graduate. The module will include a detailed exposition for Neural Networks and their implementation using a Deep Learning framework. Topics covered include but not limited to: Automatic Differentiation, Stochastic Gradient Descent, Regression, Softmax Regression, Multi-Layer Perceptrons, Training of Neural Networks and hyper-parameter optimization, Convolutional Neural Networks, Recurrent Neural Networks. Applications of Neural Networks to Vision and NLP.

Tuition fees

UK fees
Course fees for UK students

For this course (per year)


International fees
Course fees for EU and international students

For this course (per year)


Entry requirements

Students need to have a 2:1 or above at undergraduate level in Electronic Engineering, Computer Science, Mathematics or a related discipline.

University information

Queen Mary University of London (QMUL) is an internationally regarded public research institution based in London. It has a long history, dating back over 230 years, and is a member of the prestigious Russell Group of universities. QMUL has five campuses in the city of London and an international network of satellite campuses in China, Malta and Paris. There is a population of around 16,000 students at the London campuses and more than 32,000...more

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