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- Introduction to Sports Analytics
Introduction to Sports Analytics
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Short Overview
The programme provides a comprehensive introduction to Sports Analytics and is designed for those who want to understand how data can be transformed into meaningful insights for analysing and evaluating sports performance. Participants will be introduced to key concepts, methods and tools used in data analysis, with a particular focus on applications in sports.
The programme covers the fundamentals of programming and data analysis using R and Python, together with essential mathematical and statistical methods used in Sports Analytics. Participants will also learn techniques for data visualisation and Data Science Storytelling, enabling them to communicate analytical results clearly and effectively. Particular emphasis is placed on sports performance analysis and the practical use of real-world sports data.
Through practical examples and applications, participants will learn how to use their skills in professional settings, including player and team evaluation, analysis of match and performance data, decision support for coaches and scouts, and the development of dashboards and analytical reports. The programme is also relevant to professionals working in sports organisations, sports technology, betting and gaming companies. The overall aim is to develop both practical technical skills and the ability to use data effectively to support evidence-based decision-making in sports.
Learning Goals
Upon successful completion of the program, participants will be able to:
- Utilize fundamental programming commands and syntax structures in R and Python for sports data acquisition, manipulation, and analysis.
- Preprocess, clean, and explore empirical sports datasets to identify key characteristics, underlying structures, and behavioral patterns.
- Implement appropriate descriptive and exploratory data analysis techniques to extract actionable insights from athletic performance data.
- Apply core mathematical concepts and analytical tools essential for understanding modeling frameworks and methodologies in Sports Analytics.
- Formulate and solve foundational mathematical problems related to the evaluation and modeling of athletic performance.
- Design and construct tailored data visualizations for the effective, high-impact graphical communication of sports metrics
- Synthesize analytical findings into coherent, evidence-based data narratives (Data Science Storytelling) tailored to heterogeneous stakeholder and practitioner audiences.
- Analyze sports performance datasets and deploy domain-specific performance indicators for quantitative player and team evaluation.
- Leverage data-driven insights to support decision-making processes concerning competitive performance, talent assessment, and tactical planning.
- Select and apply fundamental statistical methods for rigorous sports data analysis.
- Interpret the empirical outputs of core statistical analyses and critically assess the uncertainty associated with data-derived inferences.
Program Value
Upon successful completion of the programme, participants will acquire fundamental knowledge and practical skills in Sports Analytics, including the use of R and Python, sports data analysis and visualisation, and statistical methods. They will be able to apply these skills to evaluate sports performance, interpret data and support evidence-based decision-making in professional sports environments.
Section Organization
All instructional modules will be delivered via live streaming, with the exception of the final unit (Sports Analytics Labs), which will be conducted in person at the facilities of the Center of Continuing Education and Lifelong Learning (KEDIVIM) or the Athens University of Economics and Business (AUEB).
The total duration of the program is twelve (12) weeks. The instructional period comprises ten (10) weekly sessions consisting of three (3) contact hours per module, whereas the final thematic unit (Sports Analytics Labs) will entail two (2) contact hours weekly. The remaining two (2) weeks are allocated for on-site examinations and oral project presentations hosted at KEDIVIM or AUEB premises.
To qualify for program completion and certification, participants are required to successfully fulfill the academic requirements of at least two (2) thematic units.
Teaching Language
English
Application deadline: 30/10/2026
Program start: 02/11/2026
Program completion: 31/01/2027
Scientific area
Way to follow
ECTS units: 10
Hours of live training: 170
Includes training hours with classroom, laboratory, or remote (via live streaming) instruction along with any breaks.
Includes training hours with classroom, laboratory, or remote (via live streaming) instruction along with any breaks.
Hours of asynchronous training: 115
Includes training hours (indicative) using an asynchronous distance learning (eLearning) platform.
Includes training hours (indicative) using an asynchronous distance learning (eLearning) platform.
Additional hours of employment: 15
Additional hours of work are included (indicative) and may include individual study, writing assignments, participation in field visits, participation in exams, etc.
Additional hours of work are included (indicative) and may include individual study, writing assignments, participation in field visits, participation in exams, etc.
Total hours: 300
The total hours include hours of synchronous and asynchronous training, as well as additional hours of employment.
The total hours include hours of synchronous and asynchronous training, as well as additional hours of employment.
Weeks of training: 12
Weeks in which activities of any kind are planned are included.
Weeks in which activities of any kind are planned are included.
Maximum number of absences: 3
Vocational Education and Training Certificate
Trainees who successfully complete the program are granted a Vocational Education and Training Certificate of the Center for Education and Lifelong Learning of the Athens University of Economics and Business, which is accompanied by a Supplement to the Certificate, detailing the subject of the program, the thematic units attended by the trainee , as well as the training methodology followed.
Target Audience
The programme is aimed at university students and graduates, as well as professionals who wish to
develop or strengthen their knowledge and skills in Sports Analytics. It is particularly relevant to
statisticians, mathematicians, data scientists and data analysts who wish to specialise their expertise in
the sports domain. It also targets professionals working in sports clubs, federations, sports companies
and organisations, including coaches, scouts and sports analysts. In addition, the programme is suitable
for professionals working in betting and gaming companies, as well as in sports marketing and
management, who wish to use data and statistical methods to analyse sporting events, evaluate players
and teams, and support evidence-based decision-making.
Scientific Coordinator
Program Tutors
Cost
IN-PERSON ATTENDANCE: 1000€
Trainees are informed of the installment amounts and related deadlines via email upon their acceptance into the program.
CONTACT
45, Kefallinias Str., 11257, Athens
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dummy secretariat@diaviou.aueb.gr
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dummy+30 210 8203 913
For the in Class programs:
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dummydiazosis@diaviou.aueb.gr
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dummy+30 210 8203 916, 912, 914
For the eLearning Programs:
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dummyelearning@diaviou.aueb.gr
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dummy+30 210 8203 753
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