Data Science

TheData Science program atQueen Mary University of Londonis designed to address the growing demand for highly skilled data scientists and engineers capable of combining advanced computer science techniques with modern statistical methods to extract insights and create data-driven services.

The Data Science program at Queen Mary University of London offersworld-leading expertise and industry partnerships to equip students with essential skills in statistical data modeling, visualisation, machine learning, and domain-specific applications like computer vision and natural language processing.

Key Features

You will cover fundamental statistical and analytical concepts (such as machine learning) and technological tools (such as cloud platforms, Spark) for large-scale data analysis. Through your taught modules, you will examine:

• Statistical data modelling, data visualisation and prediction.
• Machine learning techniques for cluster detection, and automated classification.
• Techniques for processing massive amounts of data.
• Domain-specific techniques for applying data science, including: computer vision, social media analysis, intelligent sensing and internet of things.

• Case study-based projects that show the practical application of key skills in real industrial and research scenarios.

Career paths

The postgraduates go on to work in a wide variety of careers and sectors, including technology, healthcare, finance, consulting, marketing and academia. The broad range of skills gained through programs 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
• Business Analyst
• Technical Analyst

In organisations including:

• IBM
• Dataiku
• Accenture
• Blackrock
• Credit Suisse
• NHS

Courses include:

• Applied Statistics
• Data Mining
• 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 AI