Overview

2 day training on open data science in the sports sector

Dates: Thursday 21st January and Friday 29th of January

Venue: Open Data Institute, 3rd floor, Quickhouse, 65 Clifton St, London, EC2A 4JE

Times: -

Agenda: Download PDF

As a partner of the RAIS project, the Open Data Institute will be delivering the first "hands-on" research training event in Q1 2021. This two day training event will focus on Data Innovation in the Sports Sector.

The training will match closely with the research areas of the RAIS project including:

• Distributed Sensing Infrastructure & Networking for Internet of Sports
• Security, Privacy, and Trust
• Data Mining and Edge Analytics
• Predictive Analytics

This hands-on training will act as a stimulus to help PhD students think about the opportunities of applying data science techniques in the sports sector. In addition to the taught material, a number of guest speakers will offer insights into the applications of data science in both the sports and other sectors.

The training will be split into a number of sessions covering the following topics:

Day 1

• Open Data Science - What, how and why?
• The internet of sports data - including guest speakers from the OpenActive project and external partners.
• Applying data science in the sports data landscape
• Hands-on - Building a dashboard from large quantities of sports data

Day 2

• Security, privacy and trust - Lessons from other sectors
• Practical steps to build and maintain trust
• Predictive analytics and machine learning
• Hands-on - Building a machine learning algorithm for predictive classification

save_alt Agenda

By the end of the course students will be able to apply a broad range of data science skills and knowledge into their own work. We will do this by:

1. Building a profile of a data scientist and identifying the knowledge and skills required
2. Exploring a number of case studies of data science applied in the sports and other sectors
3. Analysing the development of the sports data ecosystem, identifying future opportunities
4. Evaluating how to build and maintain trust, security and privacy when dealing with different sources of data
5. Applying a number of analysis techniques on data to discover insight
6. Examining the implications of applying predictive analytics and machine learning techniques to data
7. Creating a number of practical outputs to take away