The Data Science Graduate Programme curriculum provides a robust and exciting learning pathway designed to consolidate existing analytical skills. Our experienced lecturers work closely with graduate data scientists and stakeholders to continuously review and improve the content of our curriculum, ensuring our training materials are engaging, relevant and effective.
The aim is to provide you with a range of tools and experience in applying them, ensuring that you can make impartial, pragmatic analysis, using the most appropriate software or technique in working
towards given success criteria.
Please note that the exact content and sequence of training delivery is regularly reviewed and improved.
During the first year of the programme, the modules cover the following topics:
|Intro to Python / R
|Command line basics
|Introduction to Git
|Data viz in Python / R
|Statistics in Python / R
|Introduction to RAP
|Reproducible reporting using RMarkdown
|Machine learning in Python / R
|NLP in Python / R
|Quality assurance of predictive modelling
|Introduction to Geospatial for data science
|Foundations of SQL
|Introduction to Pyspark / SparklyR
|Group and individual projects
During the second year, you will attend organised events, workshops and seminars to stimulate and encourage further learning. Monthly sessions focus directly on developing experience and knowledge in relation to the Data Science Competency Framework.
The framework includes the following competencies:
- Programming and build
- Data science innovation
- Applied maths, statistics and scientific practices
- Ethics and privacy
- Developing data science capability
- Delivering business impact
- Data engineering and manipulation
- Understanding product delivery
You will work on specific technical projects that use your learning from the first year and also build vital professional skills through team work and collaboration.