Data Analytics (Taught)

*We also offer a part-time online MSc in Data Analytics

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

• Our expertise spans topics including: biostatistics and statistical genetics; environmental statistics; statistical methodology; statistical modelling and the scholarship of learning and teaching in statistics.

• The Statistics Group at Glasgow is a large group, internationally renowned for its research excellence.

• 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.

• The Masters in Data Analytics is accredited by the Royal Statistical Society.

• This programme qualifies for the prestigious Data Lab Masters Scholarships, available for Scottish students.

PROGRAMME STRUCTURE

Students are required to take 10 compulsory taught courses and select 1 taught course from the optional courses in semester 2.

Semester 1
Core courses
Probability (Level M)
Regression Models (Level M)
Statistical Inference (Level M)
Database and Data Analytics (M)
Introduction to statistical programming in R and Python

Semester 2
Core courses
Advanced Predictive Models
Bayesian Statistics (Level M)
Big Data Analytics (Level M)
Data Analysis Skills (Level M)
Data Mining and Machine Learning

Optional courses (choose 1)
Information Visualisation (M)
Environmental and Ecological Statistics (Level M)
Spatial Statistics (Level M)
Statistical Genetics (Level M)
Functional Data Analysis (Level M)
Design of Experiments (Level M)

Project (summer)
One of:
Statistics Project and Dissertation
Statistics Project and Dissertation (with Placement)

CAREER PROSPECTS

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.