Data Science Foundations (an MDataGov module)
Date:
10/11/2025 - 14/11/2025
Organised by:
University of Southampton
Presenter:
Dr Jason Hilton and Dr Francesco Pantalone
Level:
Advanced (specialised prior knowledge)
Contact:
Helen Davies
helen.davies@soton.ac.uk
Map:
View in Google Maps (SO17 1BJ)
Venue:
University of Southampton
Highfield Campus
University Road
Southampton
Description:
This module is part of a series of short (CPD - Continuous Professional Development) courses in Social/Official statistics delivered at the University of Southampton - Highfield campus.
Data Science Foundations aims to present a range of data science concepts, including dealing with administrative and big data sources, and to present some basic methods for data analysis. This module will provide students with an understanding of the different types of data sources available across government (admin data, survey data, open data, big data, etc); how to collect data, including innovative data collection methods, e.g. web scraping; the challenges with unstructured and messy data; exploratory data analysis; how to deal with different data types in processing and analysis; and how to undertake basic data analysis with structured and unstructured data. Teaching will include lectures, and practical computer workshop in R.
For more information see: Data Science Foundations | STAT6114 | University of Southampton
Registration
Registration is by application which should be submitted at least one month before the start of the course. For further information about the application process, please get in touch with Helen Davies at helen.davies@soton.ac.uk
Assessment
This course can be taken with or without assessment. The latter offers the possibility of accumulation of credits for the MSc in Data Analytics for Government https://www.southampton.ac.uk/courses/data-analytics-for-government-masters-msc
Cost:
£1,000
Website and registration:
Region:
South East
Keywords:
Big data analytics, Data Collection, Big data, Data Collection (other), R, Administrative data, web scraping
Related publications and presentations from our eprints archive:
Big data analytics
Data Collection
Big data
Data Collection (other)
R