Southampton Summer Statistics School

Date:

09/07/2025 - 11/07/2025

Organised by:

University of Southampton

Presenter:

Keynote: Professor Chris Brown, Southampton Education School

Level:

Entry (no or almost no prior knowledge)

Contact:

Dr James Hall
j.e.hall@soton.ac.uk

Map:

View in Google Maps  (SO17 1BJ)

Venue:

Tizard Building
Room 13/3019 - Seminar Room
Highfield Campus
University of Southampton

Description:

Elevate your statistical analysis skills at the Southampton Summer Statistics School (SSSS) 2025, a 3-day intensive program designed for PhD students, post-doctoral academics, and professionals seeking to enhance their expertise in advanced statistical techniques.

Hosted by the Southampton Education School, each day features workshops that combine 50% theory with 50% hands-on practical exercises. Attendees can choose to attend as many of the sessions detailed below as they wish - simply select which sessions you'd like to attend when registering.

Note registration closes on 15 June 2025.

 

Day 1: Wednesday, 9 July 2025

AM Session: 09:30 - 13:00:

PM Session: 14:00 - 16:30:

 

Day 2: Thursday, 10 July 2025

AM Session: 09:30 - 13:00:

PM Session: 14:00 - 16:30:

 

Day 3: Friday, 11 July 2025

AM Session: 0900 - 11:30:

PM Session: 12:30 - 16:30

 

To assist attendees, pre-session resources, including software installation guides and datasets, will be provided beforehand to maximise your learning and participation in hands-on activities.

Target Audience: Non-academics and Academics working with numeric data and social science. This includes professionals, PhD students, and Post-Docs from from all disciplines!
 

Cost:

Free

Website and registration:

Register for this course

Region:

South East

Keywords:

Multilevel Modelling , Structural equation models, Item response theory, Machine learning, Causal inference in Observational data


Related publications and presentations from our eprints archive:

Multilevel Modelling
Structural equation models
Item response theory
Machine learning

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