📖Program Curriculum
This course will equip students with some basic mathematical knowledge and problem-solving skills.
Statistics for Data Science
The course is intended to give students:
a basis for the analysis and interpretation of quantitative information
an understanding of the basic ideas underlying statistical methods at an introductory level
an understanding of how to overcome problems when analysing big data sets
Relational and Non-Relational Databases
After covering relational databases and SQL this course takes you through the various NoSQL databases including document stores like MongoDB column stores like Cassandra and graph databases such as Neo4j. You'll learn to pick the right database for your application and how to build search and distribute the data in them.
Machine Learning
You'll learn the practicalities of big data analytics with techniques from data mining machine learning statistics and data visualisation. You’ll explore how we’re training computers to understand the present and predict the future with data from finance marketing and social media. You’ll learn how to apply machine learning techniques such as neural networks and decision trees to practical problems.
Cluster Computing
This course covers distributed data processing with Hadoop and MapReduce in addition to the use of Condor for distributed computation.
Scientific and Commercial Applications
With guest lectures from science and industry this course presents a set of case studies of Big Data in action. You'll learn first-hand how companies are using big data in fields such as banking travel telecoms genetics and neuroscience.
For students interested in a January start the duration of the course will be 21 months. For example students starting in January 2023 will graduate in November 2024. This decision was made to allow students to learn flexibly and enhance other skills during the summer months when teaching is not available.
Modules
The module information below provides an example of the types of course module you may study. The details listed are for the current academic year (September 2022). Modules and start dates are regularly reviewed and may be subject to change in future years.
There are three options for this course:
Starting September full time (Stirling Campus)
There are two alternative paths in year one. Please review all options carefully.
Year 1 Autumn semester (Option 1)
Compulsory module
Module Credits
Representing and Manipulating Data (ITNPBD2) 20 credits
Compulsory module
Module Credits
Commercial and Scientific Applications (ITNPBD4) 20 credits
Compulsory module
Module Credits
Mathematical Foundations (ITNPBD1) 10 credits
Compulsory module
Module Credits
Introductory Statistics for Data Science (MATPMD0) 10 credits
