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Center for Research in Sports Administration

Data Science Internship (incl. Master's Thesis) with UEFA

Exciting Collaboration Opportunity: Advance UEFA's Data Projects and Pursue Your Master's Thesis!

Project 1 Internship_Project1 (PDF, 143 KB)

Data Science Internship & Master’s Thesis

Center for Research in Sports Administration (CRSA) in cooperation with UEFA

Support the Development of the Most Comprehensive Strategic Data Landscape in Women’s Football (WF).

In cooperation with the UEFA Intelligence Centre, the CRSA offers a (paid) data science internship position. The ideal candidate will be supporting the development of the most comprehensive strategic data landscape in Women’s Football. The students will be allowed to use the datasets for their Master’s thesis. Please note that students will need to find their supervisor (e.g., CRSA members) for their thesis.

This project will specifically focus on collecting and blending various strategic data from sources related to WF domestic and international competition match results, player performance statistics, player careers and transfers.

The database will activate various valuable analyses and visualizations related to:

  • Competitive balance (e.g., identifying repeat winners across all domestic leagues)
  • Player careers (e.g., nationality of club youth player graduations, clubs with most youth team player graduations)
  • Player transfers (e.g., total number of player movements by season, mapping of player movement/transfer flows)
  • How Using official APIs and data made available. Support is required preparing analysis scripts and automation of their execution. Main Responsibilities
  • Automate the integration of Women’s Football related data.
  • Design a data model for sourced data and establish structured data tables to be accessed and analyzed by business analysts.
  • Support data engineer integrate data model within SQL environment.
  • Support business analysts analyze and visualize final table outputs.

Technical Skills Required

  • Advanced abilities with data manipulation and preparation.
  • Experience working with APIs.
  • Fluent with high-level programming languages R (tidyverse) and/or Python (pandas).
  • Knowledge of SQL a plus.

 

Project 2 Internship_Project2 (PDF, 142 KB)

Data Science Internship & Master’s Thesis

Center for Research in Sports Administration (CRSA) in cooperation with UEFA

Lead the Integration of Online Data Repositories to Support Strategic Off-Pitch Football Research 

In cooperation with the UEFA Intelligence Centre, the CRSA offers a (paid) data science internship position. The ideal candidate will be working together with UEFA’s Intelligence Centre team and lead the integration of online data repositories to support strategic off-pitch football research. The students will be allowed to use the datasets for their Master’s thesis. Please note that students will need to find their supervisor (e.g., CRSA members) for their thesis.

The UEFA Intelligence Centre aims to identify and ingest a comprehensive set of data repositories in order to enrich the delivery of strategic off-pitch football research. This project will specifically focus on collecting and blending various sources related to demographics and population statistics, mapping, and geolocation data. 

The database will activate various valuable analyses and visualization related to:

  • Mapping county and state boundaries (e.g., UEFA based map of Europe with breakdown by country/states/regions)
  • Football participation across Europe (e.g., identifying level of pitch availability by country and population age group)
  • Distances between football related assets and nearest landmarks (e.g., distance of training stadiums to nearest airport or train station)

The candidate will be using APIs and data made available by the UEFA Intelligence Centre team. Support is required for preparing analysis scripts and automation their execution.

Technical Skills Required

  • Advanced abilities with data manipulation and preparation.
  • Experience working with APIs.
  • Fluent with high-level programming languages R (tidyverse) and/or Python (pandas).
  • Knowledge of SQL a plus.

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