MSc Data Analytics

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Queen Mary University of London
Start - End
5 Sep
Study Options
GBP 11850 - 11850
Fee International
GBP 27250 - 27250

“Data analytics is about digging out the key nuggets of information in an overwhelming sea of statistics, to provide insight and value. This drives today’s economy; replacing anecdotes with quantitative evidence.” Dr Primoz Skraba, Programme Director

This Data Analytics MSc will teach you the core mathematical principles of data analysis and how to apply these to real world scenarios.

Building on the statistical foundations of machine learning you’ll then choose from module options which explore the financial, business and scientific applications; such as in trading and risk systems, optimisation of business processes, and relationships across complex systems.

Data science is the driving force behind today’s most successful businesses. In our data-driven economy, companies are seeking data experts who can use statistical techniques and the latest technologies to extract clear insights to inform every aspect of their strategy and operations.

Why study with us?

We’re looking for numerate students with an interest in problem solving and some understanding of probability or statistics. You don’t need to be a programming expert before you join us.

You will study in recently refurbished MSc student offices with state-of-the-art computers, where you will discover a variety of industry-standard tools and software such as R and Python.

You’ll be taught by our expert academics, including former industry practitioners, Fellows of the Alan Turing Institute and members of Queen Mary's Institute of Applied Data Science.

Over the summer you will work on a research project in an area of interest, developing applied research skills and putting your learning into practice.

Modules include:

· Advanced Machine Learning

· Bayesian Statistics

· Computational Statistics with R

· Data Analytics Dissertation

· Financial Data Analytics

· Graphs and Networks

· Machine Learning with Python

· Neural Networks and Deep Learning

· Optimisation for Business Processes

· Probability and Statistics for Data Analytics

· Programming in Python

· SAS for Business Intelligence

· Storing, Manipulating and Visualising Data

· Time Series Analysis for Business

· Time Series

· Topics in Scientific Computing

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