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  • DeadlineStudy Details: 1 Year Full-time

Masters Degree Description

Our Advanced Data Science Masters is an advanced course that offers you the opportunity to develop theoretical and practical skills in statistics and data science.

You'll be supported by members of staff, who work directly with industry and the public sector, to develop analytical skills which are relevant to the varied industries which make use of data science.

You'll gain skills in:

  • processing and analysis of complex data
  • the use of statistical programming for data analysis, machine learning and prediction modelling
  • understanding the key mathematical methods that underpin the analytical and predictive modelling techniques
  • problem-solving
  • effective communication
  • conducting research in the data science field

Entry Requirements

Minimum second-class (2:2) Honours degree or overseas equivalent* in a mathematical discipline.

Prospective students with relevant experience or appropriate professional qualifications are also welcome to apply.

*For Australia and Canada, normal degrees are accepted.

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Fees

See our website for fees

Student Destinations

Our MSc in Advanced Data Science will provide graduates with skills in the statistical analysis of big data. Many employers require these skills in sectors such as:

  • investment companies
  • financial institutions
  • pharmaceutical industry
  • medical research
  • government organisations
  • retailers
  • internet information providers

Module Details

Compulsory modules

  • Data Analytics in R
  • Big Data Fundamentals
  • Big Data Tools & Techniques
  • Multivariate Analysis
  • Mathematics of Machine Learning
  • Data dashboards with RShiny
  • Statistical Machine Learning
  • Deep Learning
  • Research project

Optional modules

Choose 20 credits from the list below:

  • Mathematical Introduction to Networks
  • Game Theory & Multiagent Systems
  • AI for Finance
  • Medical Statistics
  • Effective Statistical Consultancy
  • Survey Design & Analysis
  • Bayesian Spatial Statistics
  • Quantitative Risk Analysis
  • Database Fundamentals

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