Training courses

We are excited to inform all attendees of the European Conference on Quality in Official Statistics that the first day, 2 June 2026, will feature a variety of informative and engaging training courses. These courses have been thoughtfully designed to provide valuable insights and practical knowledge to enhance your understanding of the role of quality in official statistics.

Four training courses will be delivered:

1. Statistics that Shape the Future through Dissemination and Communication

TRAINERS: Leo Mršić, Zdravko Kunić

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Leo Mršić

Professor of Algebra, Bernays University

Leo Mršić is an expert in business analytics, statistics, and digital transformation, focusing on how data can drive meaningful change through effective communication and dissemination. His research explores how statistical models impact business and decision-making, ensuring that insights are not just generated but applied to real-world challenges. At Algebra University, Mršić has played a key role in developing data-driven education and research programs, helping students and professionals harness the power of analytics. His work ensures that numbers aren’t just collected but communicated in a way that drives action and innovation. Beyond academia, he is an experienced consultant and court expert, applying statistical analysis to finance, business strategy, and policy development. His contributions to European digital competency initiatives highlight the importance of making data accessible and understandable, ensuring that organizations and policymakers can use structured information for better decision-making. Through research, teaching, and consulting, Mršić continues to bridge the gap between raw data and actionable intelligence, helping individuals and businesses navigate the complexities of an information-driven world.

Zdravko Kunić

Assistant Professorof Algebra, Bernays University

Zdravko Kunić is a dedicated educator and researcher, passionate about education, digital transformation, and communication. With a strong academic background, he has contributed to shaping modern learning experiences, ensuring that knowledge is effectively disseminated and applied. He holds degrees from the University of Zagreb and Central European University, specializing in humanities and education. His career has spanned roles in academic research, publishing, and educational program development, making him a key figure in advancing digital education in Croatia. At Algebra University, Kunić has played a crucial role in curriculum development and educational innovation, focusing on how digital tools can enhance learning. He has also led initiatives such as Algebra Junior, a program designed to introduce young learners to digital skills and creative thinking. Beyond academia, he has worked in publishing and educational consulting, helping shape textbooks, learning materials, and strategic educational programs. His contributions to start-up initiatives and digital education projects highlight his commitment to making knowledge accessible and engaging. Through his work, Zdravko Kunić continues to bridge the gap between education and technology, ensuring that learning is not just about acquiring information but about effective communication and real-world application.

Course description

This workshop offers a practical, collaborative approach to improving statistical communication. Participants work in small groups to analyse a real-world case, identifying key goals, audiences, communication channels, and messaging strategies. Groups present their findings to the full cohort, sparking discussion and exchange of ideas. Trainers provide personalized feedback to help refine strategies and highlight strengths. The workshop wraps up with a summary of key insights and best practices. Participants reflect on their learning and explore how to apply new skills in their professional roles. See more

Main goals

Enhance understanding of the role of dissemination and communication
Equip participants with frameworks and best practices
Develop skills in case-based problem solving
Improve presentation and communication capabilities
Foster interactive learning and peer exchange
Build confidence in designing user-focused dissemination strategies
Ensure readiness for practical application

Target audience

Professionals involved in the communication and dissemination of official statistics.

Logistics

Max participants 35
Laptop required: Yes (set up instructions will be provided)

Vlado Cetl

Full Professor at University North – Varaždin, Croatia. Former EC JRC expert in spatial data infrastructures and AI in geomatics

Professor Vlado Cetl, a leading expert in geoinformation and spatial data infrastructure, has advanced European SDIs by integrating AI into statistical systems. At University North in Croatia, his work spans environmental mapping, remote sensing, and land use analysis, showcasing AI’s practical impact on spatial data. Beyond academia, Vlado Cetl has been instrumental in policy-driven geospatial initiatives, such as the INSPIRE Directive, ensuring spatial data integration into decision-making processes with AI-powered efficiency. His forward-thinking approach bridges legal frameworks with modern technological advancements, enabling more insightful and data-driven statistical applications. Committed to fostering innovation, Vlado Cetl continues to lead training efforts in leveraging AI and new technologies for improved statistical production. His contributions remain invaluable in shaping future geospatial strategies and advancing AI-driven analytics.

Olga Bjelotomić Oršulić

Assistant Professor at University North – Varaždin, Croatia. Expert in Geostatistics, spatial data analysis, and AI applications in geosciences

Ms. Olga Bjelotomić Oršulić is an Assistant Professor at the Department of Geodesy and Geomatics, University North, Croatia. She holds a PhD in Geodesy from the University of Zagreb (2015). Olga has experience in both academia and industry, having worked as a research associate at the University of Zagreb and later as a project manager overseeing the implementation of large-scale geoinformatics systems. At University North, she teaches courses in Geostatistics, Geodetic Reference Frames, Remote Sensing, and Physical Geodesy. Her work focuses on the application of statistical methods and data analysis in spatial and environmental contexts, with recent projects integrating AI-based and machine learning approaches in geospatial modelling, natural hazard assessment, and big data environments.

Course description

This applied course introduces participants to ESS Quality Indicators and how AI/ML (machine learning) methods enhance data quality in official statistics. Participants will examine a case study on regional indicators and acquire methodological expertise in the use of open source tools for constructing reproducible workflows encompassing data ingestion, visualization, and automated reporting, thereby developing transferable skills applicable to their own research and professional projects. A dedicated AI segment will demonstrate coding acceleration, anomaly detection, and basic interpretability using machine learning and language models. See more

Main goals

Apply Quality Assurance indicators (e.g. missingness, plausibility) in real workflows
Build reproducible pipelines with spatial and temporal QA
Use AI to automate coding and enhance statistical interpretation

Target audience

Statisticians, analysts, and Quality Assurance professionals seeking practical AI-based solutions using open tools.

Logistics

Max participants 35
Laptop required: Yes (set up instructions will be provided)

Maria João Zilhão

Member of the Management Board of Statistics Portugal (INE)

Maria João Zilhão is member of the Management Board of Statistics Portugal (INE) since January 2018, after having served as the Director of the Planning, Control and Quality Unit at INE, since 2007. She has a Degree in Economics, a master’s degree in Statistics and Information Management, and has completed in 2025 a course in Organizational Leadership at Harvard Business School Online. She started working at INE as a statistician in economic agriculture statistics in 1990. She has been involved in Quality Management at the ESS since 1999, coordinating the implementation of INE’s Quality Management System, that got its certification process along the ISO norm 9001 in 2024. She has been member of several groups having participated in the LEG on Quality and on the Sponsorship on Quality and has also been involved in the preparation of European quality tools such as the ESS QAF, the revision of the European Statistics Code of Practice and in the methodology of the Peer Review exercises, chairing one of the teams of Peer Review Experts in the 3rd round. She has been a trainer in the field of quality in statistics at ESS level since 2001 and has also extensive experience in international cooperation in systematic quality management and as official representative in international organizations, such as the United Nations. She has chaired the Council Working Party on Statistics in 2021, during the Portuguese Presidency of the EU. She represents Statistics Portugal at the Portuguese National Statistics Council, being President of the Standing-Section on Statistics Coordination within the National Statistical System, since 2018. She has been a member of the Programme Committee for several editions of the European Quality Conference, being part of the organizer team and hosting Country of the Q2024.

Magda Ribeiro

Head of the Planning, Control and Quality Unit at Statistics Portugal (INE)

Magda is the Head of the Planning, Control and Quality Unit at Statistics Portugal (INE) since 2018, and she has been working in this unit since 2007. She has a Degree in Mathematics, a master’s degree in Statistics and Information Management and a Post-graduation in Data. Mining and Data Analysis, working in INE since 1997. Quality has been one of her areas of greatest interest, particularly the development and application of the GSBPM at INE. Additionally, she is responsible at INE for the management of its Quality Management Systems and its certification process within the INE’s Integrated Management Systems along the ISO norms 9001 and 27001. She is, since 2019, Co-trainer on the ESTP Course – Quality Management in Statistical Agencies – Introductory Course, and she has been actively involved in the last rounds of Peer Reviews in the ESS, being the national coordinator in the 3rd round. She regularly participates in national and international working groups, as well as cooperation projects regarding the areas of Quality, Dissemination and Strategic Planning. In the Portuguese National Statistics Council, she has been actively participating in the elaboration of the planning, reporting and monitoring exercises for INE and the National Statistical System.

Course description

The power of quality management in official statistics lies in the ability to ensure that the systems underlying its production processes deliver relevant, accurate, accessible, timely, coherent, comparable, protected and confidential statistics thus upholding public trust and the institutional legitimacy of the organisations that produce them.
In a context of a fast-evolving technological transformation and data ecosystems, regulatory frameworks, and growing demands, Quality management has become a cornerstone of institutional resilience, credibility and trust.
This course aims at offering a practical and integrated approach to quality management in official statistics, based on widely recognised international frameworks. Special attention is given to the integration between ISO-based quality management systems and the operational and ethical frameworks of statistical systems, regardless of their level of institutional maturity, by using practical examples at process level. Discussion on the impact of Artificial Intelligence (AI) in statistical production will also be addressed. See more

Main goals

Apply international quality frameworks to official statistics (UN, ESS QAF, ISO 9001, GSBPM)
Integrate risk management into statistical processes (e.g. ISO 31000)
Evaluate and improve process maturity (CMMI model)
The importance of knowledge management to ensure organisational continuity (e.g. ISO 30401)
Discuss AI challenges to statistical quality and trust in line with European and international principles
The role of audits for monitoring, improvement and accountability (ISO, peer reviews)
Explore Quality 4.0 in official statistical production

Target audience

The course is addressed to professionals involved in the production of official statistics, as well as those from other organizations, who are interested in reflecting on approaches to quality management systems and the adoption of normative frameworks.

Logistics

Max participants 35

Petra Posedel Šimović

Assistant Professor at the University of Zagreb Faculty of Agriculture and visiting, Assistant Professor at the University of Zagreb Faculty of Electrical Engineering and Computing

Petra Posedel Šimović is an expert in statistics, financial mathematics, and business analytics, focused on leveraging data to enhance decision-making. She studied mathematics, statistics, and computing at the University of Zagreb, later specializing in statistical modelling for complex financial systems. Her career spans education, research, and applied data science, with academic roles at several institutions including the Faculty of Economics and the Faculty of Agriculture at the University of Zagreb, where she teaches business statistics and data analysis. Petra has led research in financial econometrics, risk modelling, and statistical inference, using large datasets to build robust analytical models for finance, agriculture, and policy. Her work with the Croatian Science Foundation and global organizations like the World Bank has advanced data-driven methodologies. She collaborates internationally with Aarhus University, TU Vienna, and Caltech, and consults on data integration, risk analysis, and predictive modelling, helping organizations turn data into strategic insights. Through her multifaceted work, she bridges raw data and actionable intelligence to deliver impactful solutions.

Azra Tafro

Associate Professor at the University of Zagreb Faculty of Forestry and Wood Technology

Azra Tafro applies quantitative methods, statistics, and digital image processing to natural and technical sciences and other fields, ensuring that data is effectively used to enhance research and decision-making. Her work at the University of Zagreb Faculty of Forestry and Wood Technology emphasizes data literacy and predictive modelling, helping students and professionals apply large datasets to real-world challenges in forestry, sustainability, and resource management. She contributes to scientific communication as a member of the Editorial Board of the journal Drvna industrija, supporting the exchange of knowledge in wood technology and applied sciences. Beyond research, she is active in professional development and biometric sciences, serving as Secretary of the Croatian Biometric Society – Croatian Region of the International Biometric Society, where she fosters collaboration in statistical modelling and data-driven innovation. Her work demonstrates how structured data, when effectively used and communicated, can improve scientific insights, and drive better decisions across industries. By integrating administrative and privately held data sources, she ensures that research is not just theoretical but practical, impactful, and accessible, helping shape policies, industry strategies, and sustainable solutions.

Course description

This hands-on course helps public-sector professionals modernize statistical production by integrating administrative and privately held data. Participants explore key data types - like tax records and digital Participants explore key data types, such as tax records and digital traces, along with governance and legal issues. Using RStudio, they learn practical techniques for cleaning, harmonizing, and reconciling data, with a focus on income and wealth estimation. The training wraps up with a case study, equipping participants with both technical skills and a strategic framework for using external data in official statistics. See more

Main goals

Train analysts to integrate administrative and privately held data into official statistics for better quality and coverage
Teach practical techniques to clean and align survey and administrative data, especially for income and wealth estimates
Provide hands-on experience with a full statistical workflow, adaptable to their own institutions

Target audience

Public-sector professionals (statisticians, analysts, researchers, and data managers) who work with or plan to use administrative or privately held data.

Logistics

Max participants 40
Laptop required: Yes (set up instructions will be provided)