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Data Analytics - MSc

Course Description

Data is becoming an ever increasing part of modern life, yet the talent to extract information and value from complex data is scarce. This Masters will provide you with a thorough grounding in state-of-the art methods for learning from data, both in terms of statistical modelling and computation. You will also gain practical hands-on experience in carrying out various data-driven analytical projects. Previous study of Statistics or Computing Science is not required.

Why this programme

-The University of Glasgow’s School of Mathematics and Statistics is ranked third in Scotland and eleventh in the UK (Complete University Guide 2017).
-The Statistics Group at Glasgow is the largest Statistics group in Scotland and internationally renowned for its research excellence.

Programme structure

Core courses
-Preliminary Mathematics for Statisticians 1
-Probability 2
-Statistical Inference2
-Regression Models2
-Introduction to R Programming
-Data Management and Analytics using SAS
-Bayesian Statistics
-Generalised Linear Models
-Big Data Analytics

One Course is optional for students with sufficient background in Linear Algebra and Calculus.

Two students who have already completed an equivalent course can substitute this course by any other optional course, including optional courses offered as part of the MRes in Advanced Statistics (see the website for details).

In your project (60 credits) you will model data collected from research in environmental science, assessed by a dissertation.

Optional courses - choose two courses from group 1, one course from group 2 with the remaining courses coming from groups 1, 2 or 3.
Group 1
-Artificial Intelligence
-Information Retrieval
-Machine Learning

Group 2
-Professional Skills
-Data Analysis

Group 3
-Multivariate Methods
-Time Series
-Design of Experiments
-Stochastic Processes
-Environmental Statistics
-Financial Statistics
-Statistical Genetics
-Spatial Statistics
-Functional Data Analysis

In your project (60 credits) you will tackle a complex data analytical problem or develop novel approaches to solving data analytical challenges.

The Data Lab

We work closely with The Data Lab, an internationally leading research and innovation centre in data science. Established with an £11.3 million grant from the Scottish Funding Council, The Data Lab will enable industry, public sector and world-class university researchers to innovate and develop new data science capabilities in a collaborative environment. Its core mission is to generate significant economic, social and scientific value from data. Our students will benefit from a wide range of learning and networking events that connect leading organisations seeking business analytics skills with students looking for exciting opportunities in this field.

Career prospects

There is a massive shortage of data-analytical skills in the workforce. Statistician is projected to be one of the fastest-growing occupations. Statistical Analysis and Data Mining was listed by LinkedIn as the hottest skill in 2014 and came second in 2015.

Our graduates have an excellent track record of gaining employment in many sectors including medical research, the pharmaceutical industry, finance and government statistical services, while others have continued to a PhD. Our recent graduates have taken up positions as Statisticians with the Scottish Government, as Advanced Analytics Analyst at Deloitte Ireland, as Consultant at the World Bank and as Research Officer at Kenya Medical Research Institute (KEMRI).

Visit the Data Analytics - MSc page on the University of Glasgow website for more details!

Entry Requirements

A first degree equivalent to a UK Upper Second Class Honours degree, normally with a substantial Mathematics component (at least equivalent to Level-1 courses in Mathematics and Level-2 courses in Calculus and Linear Algebra at the University of Glasgow). Graduates who only have the equivalent of A-Level Mathematics can also be admitted to the programme. However such candidates are required to work through self-study material provided and attend a pre-sessional course on Elementary Mathematics (to be taught in the two weeks preceding the start of the teaching period of semester 1).

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Recipient: University of Glasgow

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