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Data Science

Course Description

This interdisciplinary MSc is aimed at students who wish to develop skills in the emerging discipline of Data Science. Building upon data science fundamentals, a variety of pathways through the MSc are available and allow students to choose from a range of elective modules according to their skills, interests and career aspirations. Students then undertake a 12-week summer placement either within industry (in a business setting), or as part of an academic research project to consolidate their learning.

Optional pathways span fundamentals and also application areas including:
• Business Analytics: how to gain business insight from large and complex industrial data
• Data Mining: how data mining can be performed at scale, and in a range of application areas (eg marketing and finance, social computing)
• Health Informatics: how to build models and gain insight to improve public health and aid clinical decision making
• Systems and Technologies: how to build large-scale systems for answering data science questions
• Statistical Inference: how to specify models and build a statistical framework to gain insights from data

Core modules:
• Data Mining
• Data Science Fundamentals
• Generalised Linear Models
• Likelihood Inference
• Programming for Data Scientists

Optional (elective) Modules:
• Applied Data Mining
• Clinical Trials
• Data Mining for Marketing, Sales and Finance
• Elements of Distributed Systems
• Environmental Epidemiology
• Extreme Value Theory
• Forecasting
• Longitudinal Data Analysis
• Methods for Missing Data
• Multi-level Modelling
• Optimisation and Heuristics
• Principles of Epidemiology
• Statistical Genetics and Genomics
• Survival Analysis
• Systems Architecture and Integration

Visit the Data Science page on the Lancaster University website for more details!

Entry Requirements

An upper-second class honours degree, or its equivalent, in a subject relevant to Computer Science, Mathematics or Statistics

Course Fees

£7,390 per year (UK/EU), £18,100 per year (Overseas)

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