Explore my research contributions through various academic platforms, including Google Scholar, GitHub, ResearcherID, and more. Access my latest publications, data repositories, and scholarly work, ensuring visibility across interdisciplinary research communities. Stay updated with my ongoing projects, peer-reviewed articles, and open-source collaborations that contribute to advancing knowledge in my field
With over a decade of interdisciplinary experience, I have thrived in roles as an educator, researcher, and data analyst. My teaching portfolio covers a range of subjects, including Information Behavior, Finance, Tax Accounting, and Application of Computer in Accounting. As a researcher, I have contributed to projects like the Mobile Futures initiative, focusing on trust and values in European labor markets. My expertise in data analytics, particularly with Python, has enabled me to generate impactful insights into societal challenges and deliver actionable outcomes.
I thrive on autonomy in both academic and professional settings. Whether independently delivering courses or conducting complex research, I take initiative to ensure high-quality outcomes. My ability to manage diverse tasks—from teaching advanced concepts to performing data-driven investigations—demonstrates my self-reliant approach to problem-solving and innovation.
Collaboration and active engagement are at the core of my work. As a dedicated educator, I involve students through interactive learning and group assignments. In research, I engage with interdisciplinary teams to explore critical topics like cross-cultural behavior and immigration attitudes. My commitment to teamwork ensures that my contributions consistently enhance project outcomes and learning environments.
I engage in interdisciplinary research on scientific misinformation, examining how media misrepresentation and retracted articles shape public perceptions and decision-making. My role involves:
✔ Conducting survey-based and media content analysis on science communication and public trust.
✔ Applying NLP, Python (pandas, NumPy, scikit-learn), and SEM to detect patterns in large-scale datasets.
✔ Designing and analyzing public opinion surveys related to trust in scientific information.
✔ Collaborating with researchers across disciplines to produce peer-reviewed articles and public outreach materials.
I immersed myself in data-driven migration research, analyzing European Social Survey data to understand how trust and information exposure influence European managers’ attitudes toward immigrants. My role involved:
✔ Data processing, cleaning, and visualization using Python, pandas, NumPy, and Matplotlib.
✔ Developing statistical models such as regression analysis and Structural Equation Modelling (SEM) to uncover hidden insights.
✔ Applying machine learning techniques to detect trends in large-scale migration data.
✔ Collaborating with interdisciplinary researchers to translate data insights into impactful policy recommendations.
During my doctoral research, I delved into the intersection of information science, health behavior, and digital ecosystems. My work focused on understanding how immigrants navigate digital health information landscapes in Nordic countries. Key contributions:
✔ Conducted both qualitative and quantitative research, utilizing NVivo for thematic analysis, SmartPLS for structural equation modeling, and SPSS for statistical testing.
✔ Designed and analyzed large-scale surveys, exploring immigrant health information-seeking behavior.
✔ Lectured in Information Behaviour 1 & 2, mentoring students on digital literacy, data analytics, and health information systems.
I engaged in humanitarian fundraising efforts, ensuring sustainable financial support for crisis response initiatives. My contributions included:
✔ Securing donor commitments through impactful face-to-face interactions.
✔ Ensuring compliance with data privacy regulations while handling donor information.
I navigated the intricate world of financial compliance, auditing, and risk assessment, delivering precise financial evaluations. My key responsibilities:
✔ Conducted financial audits and compliance checks across various industries.
✔ Designed custom financial reporting spreadsheets in Microsoft Excel, improving efficiency and accuracy.
In this leadership role, I spearheaded the transformation of financial operations by integrating enterprise financial management software. Key achievements:
✔ Managed financial reporting, budgeting, and taxation, ensuring seamless financial operations.
✔ Led a digital transformation project, transitioning operations to Hamkaran System’s enterprise software.
✔ Developed SQL-based custom reports, optimizing data analysis for strategic decision-making.
My early career in accounting honed my skills in financial operations, bookkeeping, and data analytics. Key responsibilities:
✔ Managed daily financial transactions, ensuring accuracy in financial reporting.
✔ Designed Excel-based financial tracking tools, improving data-driven decision-making.

I’ve built a strong foundation in data-driven decision-making by working with analytics, machine learning, and data engineering across several courses. Through this specialisation, I developed the ability to turn complex data into meaningful insights and practical solutions.
Along the way, I gained experience in:
✔ Introduction to Analytics (5 ECTS) – Learning the fundamentals of data processing, statistical analysis, and how to use data to support decision-making.
✔ Machine Learning for Predictive Problems (5 ECTS) – Building models to forecast trends and make informed predictions based on data.
✔ Visual Analytics (5 ECTS) – Communicating insights clearly through data visualization and interactive dashboards.
✔ Machine Learning for Descriptive Problems (5 ECTS) – Exploring data with unsupervised learning techniques to uncover patterns and relationships.
✔ Cloud Computing and Data Engineering (5 ECTS) – Working with modern data infrastructure and tools to manage, process, and scale data solutions.
✔ Service Design (5 ECTS) – Understanding how to design user-centred services and connect data insights with real-world business needs.
I embarked on an enriching journey into pedagogical sciences, blending theory with practice to master the art of teaching, guidance, and competency-based learning. This internationally recognized qualification equips me with the skills to teach in vocational institutions and universities of applied sciences, as well as to develop staff training programs in corporate and professional settings.
Through collaborative and diverse learning environments, I am gaining expertise in:
✔ Curriculum planning & educational theory
✔ Teaching in multicultural & digital classrooms
✔ Guidance methods and competency-based assessment
✔ Networking & fostering well-being in educational settings
This program strengthens my ability to develop learning environments that adapt to evolving professional landscapes, ensuring that education remains dynamic and inclusive.
I immersed myself in the field of Information Studies, delving into the complexities of health information behavior and its influence on immigrant communities in Nordic countries. Through advanced data analytics, mixed-method research, and digital information modeling, I navigated the intricate relationship between trust, cultural adaptation, and digital health literacy, forging a data-driven path to better understand marginalized communities.
I embarked on an intellectual journey into knowledge management, mastering the art of structuring, analyzing, and optimizing information flows in digital and healthcare contexts. With a focus on e-health and digital literacy, I explored how older adults navigate health information ecosystems, forging a connection between user behavior, technology, and healthcare accessibility
Immersing myself in the intricate world of financial modeling and investment strategies, I developed expertise in risk assessment, capital markets, and asset pricing theories. Navigating the complexities of systematic and non-systematic risk, I cultivated a data-driven approach to financial decision-making, blending quantitative analysis with economic foresight to assess investment performance.
I delved deep into the fundamentals of accounting and taxation, mastering the principles of financial reporting, tax law, and auditing. Exploring the intricate balance between regulatory compliance and economic efficiency, I developed a strong analytical mindset, navigating the realms of corporate finance, fiscal policy, and strategic financial planning.
As a Project Researcher at the Migration Institute of Finland and Åbo Akademi University, I conduct quantitative research for the Mobile Futures project, funded by the Research Council of Finland. My work focuses on analyzing European Social Survey (ESS) data using Python to examine how trust, values, and digital information encounters shape European managers' attitudes toward immigrants. Specifically, I contribute to:
I process, clean, and visualize large-scale datasets using Pandas, NumPy, and Matplotlib. Additionally, I apply advanced statistical techniques, including Multilevel Linear Regression, to analyze hierarchical data structures and uncover patterns in managers' trust and value systems. My research contributes to ongoing academic discussions, with findings under review in leading migration studies journals, supporting evidence-based policymaking on labor market inclusion and migration governance in Europe.
This postdoctoral research project, funded by the Finnish Cultural Foundation, is scheduled to begin on March 1, 2027, at the Migration Institute of Finland. This project will explore how AI-driven services can enhance the labour market integration of international students in Finland, addressing key barriers such as language proficiency, discrimination in hiring, and lack of labour market knowledge. The study will be conducted in multiple phases, including meta-analysis, qualitative research with students and employers, and the development of an AI-powered employment framework.
As a Doctoral Researcher at Åbo Akademi University, I investigate immigrants' health beliefs, healthcare needs, and the use of healthcare services across Finland, Sweden, and Norway. This research is part of my PhD study and is funded by the Finnish Cultural Foundation, Karl-Erik Henriksson Foundation, and Åbo Akademis Jubileumsfond. The project aims to provide evidence-based recommendations for healthcare providers, policymakers, and research institutions working with minority populations in the Nordic region.
Specifically, I contribute to:
I design and implement quantitative and qualitative data collection strategies, ensuring methodological rigour in cross-country comparative research. Additionally, I analyse survey and interview data using advanced statistical and qualitative techniques to uncover patterns in health-seeking behaviour.
Findings from this research have been published in peer-reviewed journals such as Library & Information Science Research (Jufo 3) and Informaatiotutkimus (Jufo 1), contributing to academic discussions on immigrant health, cultural adaptation, and information-seeking behaviour. My work supports policymakers and healthcare organisations in developing inclusive, data-driven healthcare strategies for diverse immigrant populations in the Nordic region.
As a Doctoral Researcher at Åbo Akademi University, I investigated health information-seeking behavior among Persian-speaking minorities in Finland during the COVID-19 pandemic. This research was part of my PhD study and was funded by the OTTO A. MALM FOUNDATION and KAKS (Municipal Sector Development Foundation). The project aimed to provide evidence-based insights into how non-native minorities accessed, evaluated, and utilized health information during a global health crisis, contributing to improved public health communication strategies.
Specifically, I contributed to:
I designed and implemented qualitative data collection strategies, ensuring methodological rigor in capturing immigrants' lived experiences during the pandemic. Additionally, I applied thematic analysis techniques to uncover patterns in health information-seeking behavior and identify key factors influencing trust, misinformation exposure, and health decision-making.
Findings from this research have been published in high-impact peer-reviewed journals, including Library & Information Science Research (Jufo 3) and Journal of Documentation (Jufo 3). These publications contribute to academic discussions on health communication, immigrant information behavior, and crisis response strategies. My work supports policymakers, healthcare providers, and public health institutions in developing inclusive, evidence-based approaches to healthcare communication for minority populations, particularly in times of crisis.

This project, led by Kim Holmberg and conducted from 2024 to 2026, investigates how mainstream media misrepresents scientific findings and how such misrepresentations influence public trust in science. As a Senior Researcher, I contribute to analysing media narratives, evaluating their alignment with original scientific content, and assessing public reactions.
This project, running from 2024 to 2028 under the leadership of Kim Holmberg, explores how retracted scientific articles continue to shape public opinion, policy-making, and evidence-informed decisions. In my role as a Senior Researcher, I study the dissemination of unreliable science on social and mainstream media platforms and its consequences for public trust and health communication.
AI-Driven Solutions for Labor Market Integration of International Students in Finland
Immigrants’ Health Beliefs, Health Needs, and the Use of Healthcare Services in Finland, Sweden, and Norway
Health Information-Seeking Behavior During the COVID-19 Outbreak Among Non-Native Minorities in Finland
Unreliable Science: Unraveling the Impact of Mainstream Media Misrepresentation
Societal Impact of Unreliable Scientific Information
Mobile Futures: Analyzing Migration Perceptions with Data Science
Earned the Google Data Analytics Certificate, demonstrating proficiency in data analysis techniques, including data cleaning, visualization, and interpretation using tools like Excel, SQL, and R. This certification enhances my ability to derive actionable insights from complex datasets.
Completed the Intermediate Pandas Python Library for Data Science course, advancing my skills in data manipulation, analysis, and visualization using Pandas. Gained expertise in handling large datasets, performing grouping, merging, reshaping, and advanced indexing. Developed efficiency in time series analysis and data preprocessing for machine learning workflows.
Completed the Pandas Python Library for Beginners in Data Science course, gaining foundational skills in data manipulation and analysis using Python. Learned essential Pandas functions for reading, cleaning, filtering, and summarizing datasets. Developed a strong understanding of data structures like Series and DataFrames, preparing data for further analysis in machine learning and statistical modeling.
Completed an 18-hour hands-on course from the University of Washington with a grade of 95.79%, covering fundamental machine learning concepts. Developed expertise in regression, classification, clustering, retrieval, recommender systems, and deep learning. Applied these techniques to real-world case studies, including predicting house prices, sentiment analysis, and product recommendations. Gained proficiency in using Python for implementing machine learning models and evaluating their performance with relevant error metrics.
Completed the MATLAB Onramp training, gaining foundational skills in MATLAB programming, data visualization, and mathematical computing. Developed expertise in handling matrices and arrays, writing scripts, debugging code, and performing basic numerical analysis. Acquired hands-on experience with MATLAB’s built-in functions for data manipulation and visualization.
Completed NVivo Online Training (Blended Learning) with Alfasoft in collaboration with Åbo Akademi University. Acquired expertise in qualitative data analysis, including thematic coding, content analysis, and data visualization techniques. Developed skills in managing large textual datasets, analyzing qualitative responses, and deriving insights for academic and industry research
Completed a comprehensive introduction to the Internet of Things (IoT), covering key concepts such as systems, information and networks, probability, game theory, and IoT applications. Gained insights into different types of data and their impact on device communication. Developed the ability to identify real-world IoT applications and perform basic system analysis.
Gained expertise in creating advanced path models using SmartPLS, useful in both research and industry decision-making. Learned to analyze how attitudes and subjective norms impact behavioral change. Developed skills in running models across groups (e.g., gender-based analysis) and interpreting results to provide actionable recommendations.
Acquired hands-on experience in building path models using SmartPLS for data-driven decision-making in research and industry. Developed skills in uploading datasets, designing structural models, and executing Partial Least Squares Structural Equation Modeling (PLS-SEM). Gained proficiency in interpreting model outputs to derive meaningful insights and recommendations.
Earned the Google Data Analytics Certificate, demonstrating proficiency in data analysis techniques, including data cleaning, visualization, and interpretation using tools like Excel, SQL, and R. This certification enhances my ability to derive actionable insights from complex datasets.
Completed the Intermediate Pandas Python Library for Data Science course, advancing my skills in data manipulation, analysis, and visualization using Pandas. Gained expertise in handling large datasets, performing grouping, merging, reshaping, and advanced indexing. Developed efficiency in time series analysis and data preprocessing for machine learning workflows.
Completed the Pandas Python Library for Beginners in Data Science course, gaining foundational skills in data manipulation and analysis using Python. Learned essential Pandas functions for reading, cleaning, filtering, and summarizing datasets. Developed a strong understanding of data structures like Series and DataFrames, preparing data for further analysis in machine learning and statistical modeling.
Completed an 18-hour hands-on course from the University of Washington with a grade of 95.79%, covering fundamental machine learning concepts. Developed expertise in regression, classification, clustering, retrieval, recommender systems, and deep learning. Applied these techniques to real-world case studies, including predicting house prices, sentiment analysis, and product recommendations. Gained proficiency in using Python for implementing machine learning models and evaluating their performance with relevant error metrics.
Completed the MATLAB Onramp training, gaining foundational skills in MATLAB programming, data visualization, and mathematical computing. Developed expertise in handling matrices and arrays, writing scripts, debugging code, and performing basic numerical analysis. Acquired hands-on experience with MATLAB’s built-in functions for data manipulation and visualization.
Completed NVivo Online Training (Blended Learning) with Alfasoft in collaboration with Åbo Akademi University. Acquired expertise in qualitative data analysis, including thematic coding, content analysis, and data visualization techniques. Developed skills in managing large textual datasets, analyzing qualitative responses, and deriving insights for academic and industry research
Completed a comprehensive introduction to the Internet of Things (IoT), covering key concepts such as systems, information and networks, probability, game theory, and IoT applications. Gained insights into different types of data and their impact on device communication. Developed the ability to identify real-world IoT applications and perform basic system analysis.
Gained expertise in creating advanced path models using SmartPLS, useful in both research and industry decision-making. Learned to analyze how attitudes and subjective norms impact behavioral change. Developed skills in running models across groups (e.g., gender-based analysis) and interpreting results to provide actionable recommendations.
Acquired hands-on experience in building path models using SmartPLS for data-driven decision-making in research and industry. Developed skills in uploading datasets, designing structural models, and executing Partial Least Squares Structural Equation Modeling (PLS-SEM). Gained proficiency in interpreting model outputs to derive meaningful insights and recommendations.

2025 Finnish Cultural Foundation, Two-year Post Doctoral Project Grant.
2024 Åbo Akademi University, Travel Grant for Conference Participation at NFF2024 (1200€).
2022 Åbo Akademis Jubileumsfond, Grant for research visit at University of Borås (4 months, 27,600 SEK).
2022 Åbo Akademi University, Travel Grant for research visit at OSLOMET (1 months, 1200€).
2022 Karl-Erik Henriksson Foundation, Grant for scientific research at OSLOMET (850€).
2022 Finnish Cultural Foundation, One-year doctoral thesis grant (12 months, 26,000€).
2021 KAKS (Municipal Sector Development Foundation), One-year doctoral thesis grant (12 months, 20,000€).
2020 Otto A. Malm Foundation, Six-month doctoral thesis grant (6 months, 13,000€).
24/06/2024 Åbo Akademi University, Publication Bonuse (100€).
02/02/2024 Åbo Akademi University, Publication Bonuse (1000€).
31/10/2022 Åbo Akademi University, Publication Bonuse (267€).
30/06/2022 Åbo Akademi University, Publication Bonuse (800€).
17/06/2021 Åbo Akademi University, Publication Bonuse (634€).
University of Borås, Sweden
Research Visitor, Information Practices and Digital Cultures Group
1.9.2022- 15.12.2022
Skills: NVivo, Data Collection, Intercultural Communication, Qualitative & Quantitative Research, Mixed-Methods Design, Research Planning & Management, Data Analysis, Research Evaluation.
Oslo Metropolitan University, Oslo
Research Visitor, INFUSE Research Group
1.5.2022-1.6.2022
Skills: NVivo, Data Collection (Qualitative & Quantitative), Mixed-Methods Research, Research Design, Intercultural Communication, Planning & Evaluation.
Since Augest 2025, I have been serving as a peer reviewer for Journal of Documentation. In this role, I critically assess submitted manuscripts, offer constructive feedback to authors, and help uphold the journal’s scholarly standards in the field of information science.

Since July 2025, I have been serving as a peer reviewer for Library and Information Science Research. In this role, I critically assess submitted manuscripts, offer constructive feedback to authors, and help uphold the journal’s scholarly standards in the field of Library and Information Science.
Serving as an Academic Editor for PLOS ONE (PLOS) (Impact Factor: 3.7), I contribute to the peer review process by overseeing the evaluation of scholarly manuscripts, ensuring rigorous academic standards, and facilitating high-quality research dissemination. My role involves assessing submissions, coordinating peer reviews, and making editorial decisions to uphold the journal's commitment to open-access scientific publishing
As an Article Editor for SAGE Open (SAGE Publications) (Impact Factor: 2.032), I contribute to the peer review and editorial process by evaluating manuscripts, coordinating reviews, and ensuring the publication of high-quality, interdisciplinary research. My role supports the journal's commitment to open-access publishing and scholarly excellence
Since Jun 2025, I have been serving as a peer reviewer for Social Sciences & Humanities Open. In this role, I critically evaluate submitted manuscripts, provide constructive feedback to authors, and contribute to maintaining the journal’s high scholarly standards in Social Sciences & Humanities.
As a peer reviewer for the Journal of International Migration and Integration (Impact Factor: 1.3), I critically assess research manuscripts, provide constructive feedback to authors, and contribute to maintaining the journal’s high academic and methodological standards in migration and integration studies.
Since 2022, I have served as a peer reviewer for the Journal of Public Health (Oxford University Press, Impact Factor: 5.058). In this role, I critically evaluate submitted manuscripts, provide constructive feedback to authors, and contribute to maintaining the journal’s high scholarly standards in public health research.
As a peer reviewer for BMC Public Health (BioMed Central) (Impact Factor: 4.5), I critically assess research manuscripts, provide constructive feedback to authors, and contribute to maintaining the journal’s high academic and methodological standards in public health research.
As a peer reviewer for Scientific Reports (Nature) (Impact Factor: 3.8), I evaluate submitted manuscripts, provide constructive feedback, and ensure the publication of high-quality research across diverse scientific disciplines.
As a peer reviewer for Health Policy (Elsevier) (Impact Factor: 3.3), I critically evaluate research manuscripts, provide constructive feedback to authors, and contribute to maintaining the journal’s high academic standards in health policy and healthcare system research.
As a peer reviewer for Archives of Public Health (BioMed Central) (Impact Factor: 3.3), I evaluate research manuscripts, provide constructive feedback, and contribute to ensuring the publication of high-quality studies in public health. My role supports the journal’s mission of advancing evidence-based research and public health policy.
Since 2023, I have served as a peer reviewer for the International Journal of Public Health (Springer Nature) (Impact Factor: 2.6). In this capacity, I critically assess submitted manuscripts, offer constructive feedback to authors, and contribute to upholding the journal’s rigorous academic standards in public health research
Since 2021, I have served as a peer reviewer for Europen Management Review (Wiley, Impact Factor: 2.534). In this role, I critically evaluate submitted manuscripts, provide constructive feedback to authors, and contribute to maintaining the journal’s high scholarly standards in management research.
Since 2022, I have served as a peer reviewer for Social Work in Public Health (Taylor & Francis) (Impact Factor: 2.313).
Since 2024, I have served as a peer reviewer for Public Health Nursing (Springer Nature) (Impact Factor: 2.1). In this role, I critically evaluate submitted manuscripts, provide constructive feedback to authors, and contribute to upholding the journal’s high scholarly standards in public health and nursing research.
Since 2023, I have served as a peer reviewer for Information Systems Management (Taylor & Francis) (Impact Factor: 2.098). In this role, I critically evaluate submitted manuscripts, provide constructive feedback to authors, and contribute to maintaining the journal’s high scholarly standards in information systems and management research.
Since 2023, I have served as a peer reviewer for International Migration (Wiley, Impact Factor: 1.946). In this role, I critically evaluate submitted manuscripts, provide constructive feedback to authors, and contribute to maintaining the journal’s high scholarly standards in information systems and management research.
Since 2011, I have served as a peer reviewer for Applied Economics (Taylor & Francis) (Impact Factor: 1.835). In this role, I critically assess submitted manuscripts, provide constructive feedback to authors, and contribute to maintaining the journal’s rigorous academic standards in economic research and applied economic analysis.
Since 2023, I have served as a peer reviewer for Cogent Social Sciences (Taylor & Francis, Impact Factor: 1.5).
Since 2024, I have served as a peer reviewer for Information Research (published by the University of Borås, Sweden). In this role, I critically evaluate submitted manuscripts, provide constructive feedback to authors, and contribute to maintaining the journal’s high scholarly standards in information science and research.
In 2022, I served as an Associate Reviewer for InSITE 2022: Exploring Virtual Connections as Mechanisms for Teaching and Research, part of the Informing Science + IT Education Conferences organized by the Informing Science Institute. In this role, I evaluated submitted conference papers, provided constructive feedback to authors, and contributed to maintaining the academic rigor of the conference proceedings.
In 2018, I served as a Co-Editor for the WIS Conference: Well-Being in the Information Society – Fighting Inequalities, held in Turku, Finland, from May to August 2018. In this role, I contributed to the editorial process, ensuring the quality and coherence of the conference proceedings while facilitating scholarly discussions on well-being and information society challenges.
I was featured in the Migration Institute of Finland’s Newsletter (December 2023) as part of the 'New Institute Members' section, highlighting my academic contributions and research focus.
I was interviewed by Polemiikki magazine, a journal published by The Foundation for Municipal Development (KAKS), about my doctoral research project on immigrants’ health-seeking behavior. The interview was published as 'Tavoittaako terveydenhuolto kasvavan maahanmuuttajaväestön?' in Issue 1/2021.
Attending the ECSR 2026 Conference in Dublin was an important opportunity for me to reflect on current debates in social stratification research. One of the strongest moments of the conference was the keynote lecture by Professor Moris Triventi from the University of Milan, titled “Social Stratification Research in the Age of Big Data: Challenges and Opportunities Ahead".
The keynote was not simply about big data. It was about what big data can do for social stratification research when it is used carefully, theoretically, and cumulatively. Professor Triventi’s central message was clear: big data and big models are not enough by themselves. They become valuable only when they are connected to strong theory, shared questions, careful measurement, transparent inference, and a serious scientific programme.

Before moving into the main argument, Professor Triventi used a background slide with a comic-style visual that placed the talk in a more personal and reflective frame. I found this opening effective because it reminded the audience that the keynote was not only about methods or data. It was also about how a research field thinks about its past, present, and future.
The slide visually connected Dublin, Milan, and the work of social stratification research. It created a lighter moment at the beginning of the keynote, but it also introduced a serious question: how should scholars reflect on what the field has already achieved while preparing for the methodological and substantive challenges ahead?
This background helped set the tone for the rest of the lecture. The keynote was forward-looking, but it was not detached from the history of the field. Professor Triventi’s argument was that social stratification research should engage with new data and methods without losing the theoretical, empirical, and cumulative traditions that have made the field strong.

After the background slide, Professor Triventi presented the roadmap for the keynote. This outline was useful because it showed that the talk was not organised around big data alone. Instead, it moved through a broader intellectual sequence: what the field has already learned, what has changed, what risks accompany the expansion of data and methods, where the field should go, and why these questions matter.
I found this structure helpful because it framed the keynote as both a review and an agenda-setting lecture. The talk first returned to the foundations of social stratification research, then considered how new data and methods are changing the field, and finally asked how stratification research can remain theoretically grounded, empirically rigorous, and relevant to public problems.
The roadmap also made clear that Professor Triventi was not arguing for methodological novelty for its own sake. The keynote was about how new tools can strengthen the field only when they are connected to shared questions, cumulative knowledge, and better explanations of inequality.

Before moving into the main argument, Professor Triventi also clarified what the keynote would not cover. He explicitly placed artificial intelligence, data privacy, ethics, institutional development, and relations with policymakers outside the main scope of the talk.
I found this clarification useful because it narrowed the lecture in a disciplined way. In a keynote about big data, it would have been easy to move into broad debates about AI, governance, privacy, or institutional reform. Instead, Professor Triventi focused on a more specific question: how can new data and methods strengthen social stratification research as a scientific field?
This helped the audience understand that the talk was not about every possible implication of big data. It was about the internal scientific programme of stratification research: its questions, concepts, methods, risks, and opportunities.

The talk then placed social stratification research within the wider transformation of the social sciences. Researchers now have access to larger datasets, administrative records, longitudinal data, social media data, mobile phone data, satellite data, and increasingly powerful computational methods. These tools allow researchers to study inequalities in ways that were not possible before.
Professor Triventi showed examples of studies using millions of observations and large-scale computational approaches. These examples demonstrated the scale of contemporary empirical research. However, he did not present big data as a magic solution. Instead, he warned that larger datasets and more complex models do not automatically produce better social science.

This was one of the most important points of the keynote. The value of big data depends on the questions we ask with it. Without clear concepts and theory, big data can produce impressive-looking results without strong sociological meaning.
A particularly useful part of the keynote was the contrast between two positions.
On one side is the computational enthusiast. This position emphasises data mining, algorithms, prediction, scalability, pattern detection, and computational performance. It brings important strengths, especially when researchers need to detect complex patterns or work with large and heterogeneous datasets. But it also carries risks: weak theoretical grounding, unclear constructs, and black-box explanations.
On the other side is the traditional stratification researcher. This position emphasises theory, hypotheses, mechanisms, causal interpretation, social meaning, and cumulative knowledge. It provides the conceptual backbone of the field. But it can also become too restrictive if it ignores complexity, heterogeneity, and the opportunities offered by new data and methods.

The keynote did not argue that one position should replace the other. Instead, it called for integration. Computational methods can strengthen social stratification research, but only when they are embedded in theory, clear concepts, and disciplined research designs.
In this sense, the keynote offered a constructive middle position. It did not reject computational methods, nor did it abandon traditional sociological explanation. It asked how the two can be combined in a more rigorous research programme.
Professor Triventi defined social stratification research as the study of how advantages and disadvantages are structured, reproduced, and transmitted across individuals, groups, and generations through systems of unequal life chances.
This definition is useful because it reminds us that stratification is not only about income, education, class, or occupation separately. It is about how different forms of advantage and disadvantage are connected across the life course and across generations.

The traditional focus of the field has often been the relation between social origin, education, and destination. This classic framework remains important because family background continues to shape educational opportunities, and education continues to shape later life chances. But the keynote also showed that the field has expanded far beyond this model.

Professor Triventi’s slide on the expansion of the field made this shift very clear. The traditional core of stratification research remains important: parents’ social class shapes education and academic performance, and these in turn shape later social class. But the field now studies a much wider set of origins and outcomes.
On the origin side, stratification research increasingly examines family education, income, wealth, gender, ethnic and migration background, and geographic origin. On the outcome side, it now extends beyond social class to labour-market outcomes, family trajectories, health, consumption, attitudes, and political ideology and behaviour.
I found this slide useful because it showed both continuity and change. The field has not abandoned its classic questions about social origin, education, and destination. Instead, it has expanded those questions into a broader research agenda about how multiple forms of advantage and disadvantage are connected across the life course.
The expansion of the field is important. Social stratification research now examines gender, migration background, ethnicity, geographic origin, health, consumption, attitudes, political behaviour, and many other outcomes. This broader scope creates exciting possibilities, but it also requires stronger conceptual organisation. Without shared questions and comparable measures, an expanding field can easily become fragmented.
One of the strengths of the keynote was that it did not treat social stratification research as a field starting from zero. Professor Triventi reminded the audience that the field has already accumulated important empirical knowledge.
We know that occupational hierarchies and mobility patterns show considerable stability across countries and over time. We know that education remains a major channel of both upward mobility and intergenerational reproduction. We know that inequalities persist beyond education, because social origin continues to shape occupational rewards even after educational attainment is considered. We also know that educational institutions matter, including tracking systems, school structures, and institutional arrangements.

This part of the keynote was important because it showed the value of cumulative research. The field has already built a strong foundation. The challenge now is to connect new data and methods to that foundation rather than letting the field fragment into disconnected studies.
The message was not that previous stratification research is obsolete. The opposite was true. Professor Triventi’s argument was that the field should use new data and methods to extend and refine existing knowledge, not to forget it.
One of the strongest messages of the keynote was that the real transformation is not simply about having more data. What matters is what new data allow researchers to observe.
New data can offer temporal depth, allowing researchers to study trajectories, transitions, and cumulative processes over time. They can offer contextual precision, allowing researchers to examine local settings, schools, regions, and institutions in more detail. They can offer relational embeddedness, linking individuals to families, peers, teachers, institutions, and networks. They can also offer analytical flexibility, allowing researchers to combine description, causal inference, and prediction.
This point is essential. Big data should not only make old models larger. It should help researchers ask better questions about inequality.
Professor Triventi also showed that improved data and methods allow researchers to study inequalities that are distributional, intersecting, dynamic, and spatially embedded. This means moving beyond average outcomes, taking seriously how multiple social characteristics combine, following inequalities across the life course, and recognising that local contexts shape opportunity.

For me, this was one of the most valuable parts of the keynote. It showed that new methods are not just technical instruments. They can change the kinds of sociological questions we are able to ask. They allow us to see inequality not only as a static gap but also as a dynamic, contextual, and relational process.
Professor Triventi also discussed different types of data infrastructures that can enrich inequality research. These included multi-actor data, multilevel data, longitudinal data, administrative data, text data, social media data, mobile phone data, and satellite data.
Each type of data offers something different. Multi-actor data can connect students, parents, teachers, peers, and institutions. Multilevel data can locate individuals within schools, neighbourhoods, regions, and countries. Longitudinal data can follow life-course trajectories. Administrative data can provide broad coverage from public and institutional systems. Text and social media data can capture communication, attitudes, and digital traces.

At the same time, no data source is perfect. Survey data often contain rich concepts, but they may face declining response rates. Social media data can be large-scale and timely, but they are affected by selective participation and platform bias. Administrative data can be broad and complete, but they may include fewer theoretically meaningful variables.
This is why data triangulation matters. Stronger research often comes from combining different sources, not from assuming that one source can answer every question.
The keynote also identified risks that come with the expansion of social stratification research. One risk is fragmentation. As the field expands across topics, methods, and data sources, researchers may lose shared questions and common concepts.
Another risk is data quality and representativeness. Large datasets are not automatically good datasets. They may be incomplete, biased, or poorly aligned with the concepts researchers want to study.
A third risk is data-driven research replacing question-driven research. When research begins from available data rather than theoretically meaningful questions, the agenda can become shaped by convenience.
A fourth risk is description without explanation. Description is necessary, but it is not enough. Researchers also need theory and mechanisms to explain why inequalities emerge and how they might be reduced.

The slide on the challenges of an expanding field brought these risks together clearly. Professor Triventi identified four dangers that come with new data and new methods: fragmentation of the field, problems of data quality and representativeness, data-driven rather than question-driven research, and description without explanation.
What I found especially important was that the slide also pointed toward possible responses. Fragmentation requires shared questions. Data quality problems require triangulation. Data-driven research requires theory-driven designs. Description without explanation requires integrated analytical tools that connect micro and macro mechanisms.
This framing made the keynote more interesting for me. The problem is not that the field is expanding. Expansion is necessary and productive. The problem is that expansion can weaken cumulative knowledge if it is not organised by common questions, robust concepts, and explanatory aims.

After discussing the risks of an expanding field, Professor Triventi presented a broader framework for developing a rigorous and cumulative social stratification field. The slide organised the agenda around six pillars: theory; data triangulation, measurement, integrated analytical tools, evidence synthesis and reproducibility; and policy relevance.
I found this slide especially important because it connected many parts of the keynote into one integrated research programme. The point was not simply that the field needs better data or more advanced methods. It needs a structure that links theory, measurement, evidence, explanation, prediction, and policy relevance.
The six pillars also clarified how cumulative knowledge can be built. Theory, data triangulation, and measurement provide the foundations. Integrated analytical tools then help generate stronger evidence through description, causal inference, and prediction. Evidence synthesis and reproducibility allow findings to accumulate and be validated. Policy relevance connects scientific knowledge back to social problems and public interventions.
For me, this was one of the clearest summaries of the keynote’s main argument: social stratification research can benefit from big data, but only if these tools are embedded in a coherent scientific programme.
One of Professor Triventi’s central proposals was that social stratification research should rebuild cumulative knowledge around shared questions. These questions can help organise the field across different topics, datasets, and methods.
The core questions are 'Who gets what?' Through which mechanisms? Under which conditions? With what intergenerational consequences? And what can reduce inequality?
These questions are simple, but they are powerful. They can be applied to education, health, labour markets, migration, class, gender, and many other areas. They also help connect descriptive, explanatory, and policy-orientated research.
For example, in education, we might ask why children from advantaged families achieve more. In health, we might ask why disadvantaged groups have worse health and shorter lives. The specific mechanisms differ, but the structure of inquiry remains comparable.
For me, this was one of the most useful parts of the keynote. A field becomes cumulative not only by producing more studies but also by organising studies around questions that can speak to each other.

One of the most useful sections of the keynote focused on theory. Professor Triventi argued that empirical research should continue to be embedded in solid theoretical work.
Good theory should go beyond common sense. It should challenge taken-for-granted assumptions and reveal what is not obvious. It should avoid sterile overcomplication. It should specify mechanisms, generate testable propositions, clarify empirical implications, and specify scope conditions.

This was a powerful reminder that theory is not decoration. Theory should guide the whole research design. It should shape the research question, the concepts, the measurement strategy, the interpretation of results, and the limits of the claim.
Good theory does not just explain the past. It guides inquiry, structures evidence, and helps findings travel beyond one case.
Professor Triventi also emphasised data triangulation as a major opportunity for the field. This point became especially clear in the slide comparing survey data, social media data, and administrative data.
Survey data are strong because they contain rich concepts and carefully designed questions, but they may suffer from declining response rates and limited territorial detail. Social media data are large-scale and timely, but they raise problems of selective participation and platform bias. Administrative data often provide broad coverage and completeness, but they may contain fewer theoretically meaningful variables.

The conclusion was not that one type of data should replace the others. Stronger research comes from combining data sources carefully. Triangulation matters because different data sources can compensate for each other’s weaknesses and provide a fuller picture of inequality.
This was a useful corrective to simplistic claims about big data. A large administrative dataset may offer coverage but not always meaning. A survey may offer meaning, but not always scale. Social media data may offer timeliness, but not always representativeness. The strongest research designs often combine these strengths while being transparent about their limitations.
Another opportunity Professor Triventi emphasised was synthesis and reproducibility. This part of the keynote connected strongly with the idea of cumulative knowledge. More data become more useful only when results are comparable, reproducible, replicable, and synthesizable.
The slide identified four elements: shared indicators, reproducibility, replicability, and synthesis. Shared indicators allow researchers to compare concepts and measures across studies. Reproducibility means that the same data and code should lead to the same result. Replicability means that similar findings should appear in new data or settings when the underlying relationship is robust. Synthesis means building systematic reviews and meta-analyses that identify robust patterns, boundary conditions, and research gaps.

For me, this was one of the most important methodological messages of the keynote. A field does not become cumulative simply because it produces more studies. It becomes cumulative when studies can speak to one another. That requires comparable concepts, transparent workflows, shared measures, and a willingness to build knowledge beyond individual papers.
Another important argument in the keynote was that description, causal inference, and prediction should not be treated as competing approaches. They are three complementary analytical tasks.
Description remains indispensable because it forces researchers to document patterns carefully. It helps identify gradients, map variation, document new forms of inequality, and locate where disparities are greatest.
Causal inference disciplines explanation by asking what would happen under alternative conditions. It forces researchers to specify the causal question, the estimand, the research design, and the assumptions.
Prediction disciplines explanation by asking whether our explanations generate expectations beyond the observed case. It helps distinguish signals from overfitting and well-grounded explanations from post-hoc rationalisation.

Professor Triventi’s slide on prediction made this point more concrete. Prediction was not presented as a replacement for explanation. Instead, it was presented as a tool for measurement, diagnosis, and theory-testing.
Prediction can help researchers measure structured disadvantage by identifying who is most exposed to risks or lower chances. It can also help detect heterogeneity by showing whether predictive patterns differ across groups or contexts. It can test generalisation by asking whether a model travels across countries, cohorts, or settings. Finally, it can compare mechanisms by assessing the predictive power of models based on different theoretical explanations.
I found this framing useful because it avoids a false opposition between prediction and explanation. Used carefully, prediction can discipline explanation. It asks whether our theoretical claims produce expectations that hold beyond the original case.
This integrated view was one of my main takeaways. The description tells us where inequality is and how large it is. Causal inference asks why it emerges and what might change it. Prediction asks whether an explanation travels beyond the original case. Together, these tasks can produce stronger and more credible inequality research.

This integrated view was one of my main takeaways. The description tells us where inequality is and how large it is. Causal inference asks why it emerges and what might change it. Prediction asks whether an explanation travels beyond the original case. Together, these tasks can produce stronger and more credible inequality research.
A particularly important methodological point concerned causal inference. Professor Triventi argued that improved causal inference should go beyond internal validity alone. It should also pay attention to construct validity, external validity, treatment prevalence, and selection.
Construct validity asks what researchers are actually measuring. External validity asks to what settings and populations findings can be generalised. Treatment prevalence asks how common the treatment or intervention is. Selection asks who receives the treatment and why.

This was important because it presented causal inference as more than a technical procedure. It is also a substantive sociological task. Understanding selection is part of understanding inequality. Who receives an intervention, who is excluded from it, and why those patterns emerge are not secondary details. They are part of the explanation.
The keynote also raised the question of how social stratification research can inform policy. Professor Triventi was careful here. He did not suggest that every study should end with simple policy advice. Instead, he argued that stratification research can contribute to policy by explaining mechanisms, defining concepts and indicators, identifying contextual enablers and blockers, estimating policy effects, detecting heterogeneous and unintended effects, and improving policy design.

This distinction matters. Stratification research is often very good at diagnosing inequality. It can show where inequality exists, how large it is, and which groups are affected. But the harder task is identifying feasible levers for reducing inequality. To do that, researchers need to connect diagnosis with mechanisms and intervention.
One of the strongest messages here was that policy relevance does not mean making quick recommendations. It means producing knowledge that can explain mechanisms, identify conditions, assess effects, and improve the design of interventions.
The keynote concluded with a clear agenda for the future of social stratification research.
The field should keep theory and mechanisms central. It should move toward estimand-driven, transparent, and robust inference. It should triangulate across methods and data sources. It should build a cumulative scientific programme. It should reconnect diagnosis with intervention.

For me, the keynote’s main message was that the future of social stratification research is not simply about bigger data or more advanced models. It is about building a more rigorous, theory-driven, and cumulative science of inequality.
The best research will not necessarily be the research with the largest dataset or the most complex method. It will be the research that asks important questions, measures concepts carefully, explains mechanisms, tests claims transparently, and contributes to knowledge that matters.
As a participant at ECSR 2026, I found this keynote intellectually rich and very relevant to current debates in sociology and inequality research. It reminded me that methodological innovation should not move us away from the core sociological questions. Instead, new data and methods should help us ask those questions better. The keynote also encouraged me to reflect on my own research practice. Am I asking questions that connect to a wider cumulative agenda? Are my concepts clear? Are my analytical choices transparent? Do my findings contribute to broader knowledge about inequality?
That, for me, was the value of Professor Triventi’s keynote. It did not simply celebrate big data. It asked what kind of scientific field we want to build with it. The answer, as I understood it, is a field that is open to new data and methods but not driven by them alone. A field that values description but does not stop at description. A field that uses causal inference and prediction but keeps theory and mechanisms at the centre. A field that diagnoses inequality but also asks how inequality might be reduced. That is a challenging agenda, but also an important one.
#ECSR2026 #SocialStratification #InequalityResearch #BigData #Sociology #SocialMobility #EducationInequality #ResearchMethods #CausalInference #DataTriangulation #AcademicConference #DublinConference #ComputationalSocialScience #EvidenceBasedPolicy #TheoryAndMethods #CumulativeKnowledge
I came across this image on LinkedIn, and it really made me pause and reflect.
As someone involved in research and teaching for many years, I strongly resonate with this message. AI is a powerful tool and will continue to shape the future of education — but it cannot replace the human essence of teaching.
Teaching has never been just about delivering information. It’s about connection — recognising when a student is struggling, offering the right support at the right time, and building trust that allows real learning to happen. It requires emotional intelligence, patience, and empathy.
What stood out to me most is the idea of teachers as role models. Beyond knowledge, we help shape how students think, grow, and see themselves. That influence comes from human interaction, not algorithms.
AI can enhance education, but it cannot replace the “heart” that teachers bring into the classroom.
I believe human connection will always remain at the core of meaningful education.
🎉 My new study is now published!
Everyday integration in Nordic welfare societies often hinges on something deceptively simple: access to clear, reliable, and usable information. From understanding healthcare systems to navigating the labour market, education, and social life, immigrants’ sense of belonging is shaped through everyday information encounters. This insight motivated my newly published article in Library & Information Science Research:
“Belonging through information: Mapping immigrant integration needs in Nordic societies”
🔗 https://doi.org/10.1016/j.lisr.2026.101400
The article is part of Mobile Futures - Research Consortium and was carried out in collaboration with Migration Institute of Finland & Åbo Akademi University.
📊 Conceptual focus of the study
The article maps immigrant integration through five interconnected dimensions, placing belonging at the centre and showing how information needs cut across all areas of everyday life:
1. Social integration – information about social networks, community activities, family support, childcare services, volunteering, and peer support.
2. Health integration – information on how healthcare systems work in practice, public vs. private services, entitlements, interpreters, mental health support, and medical communication.
3. Labour market integration – information about recognition of foreign qualifications, job-search practices, labour laws, workplace rights, and professional networks.
4. Cultural integration – information on local norms and customs, cultural events, community centres, media, and opportunities to express and maintain one’s own cultural traditions.
5. Educational integration – information about language learning, the education system, student services, scholarships, and educational and career pathways for both adults and children.
Based on semi-structured interviews with 54 first-generation immigrants in Finland, Sweden, and Norway, the findings show that integration challenges are not only about policies or services, but about whether information is understandable, accessible in familiar languages, and actionable in everyday situations. When institutional information is fragmented or unclear, people rely heavily on informal networks—often at the cost of confidence, wellbeing, and a sense of belonging.
📄 Read the article:
#ImmigrantIntegration #InformationBehaviour #Belonging #NordicCountries #LibraryAndInformationScience #QualitativeResearch #MigrationStudies #MobileFutures #ÅboAkademi #MigrationInstituteOfFinland #Finland #Sweden #Norway #HamedAhmadinia
I'm excited to share that I have officially completed the Practical Teacher Training (IPTE, 60 ECTS) program at Häme University of Applied Sciences (HAMK), Finland! 🎉
As part of this journey, I designed and delivered an 8-week university course titled
"Python for Data Analytics & Statistics" at Metropolia University of Applied Sciences.
This experience allowed me to merge educational theory with hands-on teaching, while applying student-centred, inclusive, and feedback-rich learning approaches in a real classroom setting.
📘 You can now read my full Learning Diary, documenting the full process from planning to outcomes:
🔗 https://lnkd.in/dkAwXYiH
💬 Special Thanks
This achievement wouldn’t have been possible without the support of:
Janne Salonen, my supervising teacher at Metropolia University of Applied Sciences, whose guidance, feedback, and encouragement were instrumental throughout my teaching journey.
Vesa Parkkonen, my tutor teacher at HAMK, for his thoughtful mentoring and for bridging the connection between pedagogical theory and practical implementation.
Your belief in my work made this learning experience not only successful but deeply meaningful. 🙏
👨🏫 What This Means
With this milestone, I am now a certified teacher in the Finnish higher education system. I’ve gained first-hand experience in course design, student engagement, and digital pedagogy—with practical tools like Jupyter, GitHub, and Kahoot integrated into my curriculum.
Whether in academia or industry education, I’m now even more prepared to contribute to impactful, learner-focused programs.
#Education #CertifiedTeacher #IPTE #HAMK#Metropolia #PythonTeaching #Pedagogy #DataAnalytics #TeachingJourney #OpenEducation #JupyterNotebooks #LearningDiary #StudentCentredLearning #DigitalPedagogy #GitHubEducation #FinnishEducation #EdTech
I’ll be honest—doing multidisciplinary research isn’t always smooth sailing. It’s exciting, yes, but it also comes with its fair share of challenges. During my time in the Mobile Futures project, I found myself constantly juggling different perspectives, methodologies, and even ways of thinking. Bringing together data science, sociology, psychology, and economics into one cohesive study felt like solving multiple puzzles at once—each with its own rules and missing pieces.
But that’s the beauty of it, right? The challenge is also the reward.
One of the key tools that made this research possible was Python—and I can’t imagine doing this kind of work without it. Here’s why. 👇
Multidisciplinary research means working with different types of data—from structured survey datasets to unstructured behavioral data. The beauty of Python is that it lets me seamlessly integrate diverse methodologies, whether I’m running statistical models, performing data cleaning, or visualizing behavioral trends.
💡 Why Python?
✔ Flexibility: Python works across disciplines—great for both statistical analysis and machine learning.
✔ Efficiency: Automating repetitive tasks (like data wrangling) saves hours of manual work.
✔ Powerful Libraries: Pandas, NumPy, and Scikit-learn make handling complex data much easier.
Instead of struggling with manual data processing, I was able to focus on making sense of the findings—which is what research should really be about.
If you’ve ever spent hours debugging code, you’ll understand why writing clean, efficient Python scripts is crucial. Early in my research, I realized that messy code = messy analysis.
📷 Below is a snapshot from my Jupyter Notebook, showing the essential Python libraries I used for data processing and visualization.

I relied heavily on Pandas for data manipulation, Matplotlib & Seaborn for visualization, and Scikit-learn for statistical modeling. But even with these great tools, I ran into issues—data inconsistencies, missing values, and errors that took hours to debug.
🚀 What helped?
✔ Writing reusable functions instead of copy-pasting code.
✔ Version-controlling my scripts with Git to track changes.
✔ Using Jupyter Notebooks to document my workflow and visualize results interactively.
These small tweaks saved me so much time in the long run and made my workflow more efficient.
One of the biggest challenges I faced was handling missing and inconsistent survey responses in the European Social Survey (ESS) dataset. If not properly addressed, missing values like "Refusal" or "Don't know" could introduce bias and distort the results.
📷 Here’s an example from the ESS dataset builder, showing how survey responses include missing values that need careful handling.

🧐 How Python helped:
✔ Pandas allowed me to quickly filter, clean, and structure survey data.
✔ Missingno (a Python library) helped visualize missing patterns.
✔ Multiple Imputation in Scikit-learn provided a robust way to estimate missing values.
Without Python, this would have been an exhausting manual process. Instead, I could automate data cleaning, ensuring that my analysis was accurate and reliable.
One of my research questions focused on how different demographic groups engage with the internet. But behavioral data is messy—patterns are influenced by external factors like cultural norms, technological adoption, and accessibility gaps.
📷 Here’s a visualization of the frequency distribution of internet use from different ESS rounds. These graphs illustrate how internet habits shift over time.

📱 How Python made analysis easier:
✔ Seaborn & Matplotlib helped me visualize usage trends over time.
✔ Groupby functions in Pandas allowed me to break data down by demographics.
✔ Scikit-learn helped identify correlations between internet use and attitudes toward migration.
The biggest takeaway? Numbers alone don’t tell the whole story. Python gave me the tools to explore not just the what, but the why behind behavioral shifts.
Working with survey data means dealing with ambiguous and missing responses. Ignoring them wasn’t an option, but incorrectly handling them could skew my results.
📌 Python’s role in fixing this:
✔ Pandas & NumPy helped detect and clean missing data efficiently.
✔ Scikit-learn’s imputation techniques ensured my dataset remained robust.
✔ Sensitivity analysis scripts let me test how different approaches impacted findings.
The result? A dataset I could trust—one that didn’t just fill gaps but preserved the integrity of the analysis.
Multidisciplinary research isn’t easy, but Python made it manageable. It allowed me to:
✅ Automate tedious tasks (instead of getting lost in spreadsheets).
✅ Analyze large datasets quickly (without endless manual cleaning).
✅ Visualize trends in ways that made insights clear and compelling.
Looking back, I can’t imagine tackling this research without Python’s flexibility, efficiency, and powerful libraries. The biggest lesson? The right tools don’t just make research easier—they make better research possible.
💡 What about you? Have you used Python for research? What challenges did you face? Drop a comment—I’d love to hear your experiences! 👇
As part of my practical teacher training at Häme University of Applied Sciences, I designed and delivered an 8-week course titled Data Analytics & Statistics in Python (3 ECTS) at Metropolia UAS in Spring 2025. The course was tailored for Master’s students in Information Technology, focusing on real-world data analysis using Python, Pandas, NumPy, Seaborn, and Matplotlib.
The course structure combined short theoretical lectures with hands-on coding in Jupyter Notebooks, Kahoot quizzes, peer collaboration via Slack, and a final mini-project. Topics ranged from descriptive statistics and probability to time-series and predictive analytics, including a special focus on cryptocurrency market analysis.
I taught 31 students and supervised 19 mini-projects, each aligned with the learners' academic or professional interests (e.g., healthcare, finance, sustainability, media trends). Students uploaded their projects to GitHub and presented them live, showcasing their end-to-end data workflows.
Key Highlights:
– Live online sessions via Zoom (Wednesdays 16:00–18:00 EET)
– Weekly hands-on assignments (30%)
– Capstone mini-project (70%)
– Open resources: GitHub, cheat sheets, videos, discussion forums
| Academic Year | Course | Module Taught | Responsibilities | Hours | Resources |
|---|---|---|---|---|---|
| Spring 2025 | Data Analytics & Statistics in Python | Python for Data Analysis and Visualization | Designing and teaching 8-week course, creating materials, mentoring 19 mini-projects, preparing Jupyter notebooks, quizzes, and grading deliverables | 16 hours teaching + 40 hours course design + 20 hours supervision + 32.5 hours coding |
GitHub Repo DOI: 10.5281/zenodo.15254796 Wakatime: 32 hrs 30 mins |

As a doctoral candidate who recently graduated from Åbo Akademi University's Faculty of Social Sciences, Business and Economics, and Law, I have had the honour of becoming involved in teaching, especially in the field of information studies. I have had the privilege of creating and presenting some modules for the International Master's program in Governance of Digitalisation, namely "Information Behaviour I" and "Information Behaviour II".
I tought the "Application of Literacies" module for the "Information Behaviour I" course during the autumn term of 2022. I designed my section to equip students with the necessary skills to navigate our increasingly digital world by thoroughly examining information and digital literacy. Subsequently, during the spring terms of 2022, 2023, and 2024, I had the opportunity to lead a section of 'Information Behaviour II', concentrating on practical application within health information behaviour. These modules were deeply informed by my ongoing doctoral research and spotlighted significant subjects like health information seeking behaviour and the health belief model. These responsibilities are detailed in Table 2.
| Academic Year | Course | Module Taught | Responsibilities | Hours |
|---|---|---|---|---|
| Spring 2024 | Information Behaviour II | Health Information Behaviour (Focus on Health Information Seeking Behaviour and the Health Belief Model) | Preparing and recording lectures, engaging students in group assignments, and learning diaries, grading activities | 1 hour’s teaching + 15 hours supervision |
| Spring 2023 | Information Behaviour II | Health Information Behaviour (Focus on Health Information Seeking Behaviour and the Health Belief Model) | Preparing and recording lectures, engaging students in group assignments, and learning diaries, grading activities | 1 hour’s teaching + 15 hours supervision |
| Autumn 2022 | Information Behaviour I | Application of Literacies (Focus on Information and Digital Literacy) | Preparing and recording lectures, engaging students in group assignments, and learning diaries, grading activities | 1 hour’s teaching + 15 hours supervision |
| Spring 2022 | Information Behaviour II | Health Information Behaviour (Focus on Health Information Seeking Behaviour and the Health Belief Model) | Preparing and recording lectures, engaging students in group assignments, and learning diaries, grading activities | 1 hour’s teaching + 15 hours supervision |
From 2011 to 2014, I worked as a lecturer and researcher at Islamic Azad University before moving to Finland to pursue my second master's degree and subsequent doctorate. This position provided me with invaluable experience in effectively teaching undergraduates intricate concepts in business administration, taxation, finance, and accounting.
My pedagogical approach was based on helping students improve their scientific knowledge and research skills through creative and engaging teaching methods, while also acknowledging their accomplishments. These experiences, combined with my ongoing commitment to professional development, have given me greater confidence in my ability to contribute meaningfully to scientific research and academic lecturing. Table 2 provides more information on the courses I taught at Azad University from 2011 to 2014, including semester/year, course code, level, type, credits, and teaching hours per semester. These responsibilities are detailed in Table 3.
| Course | Semester/Year | Code | Level | Type | Credits | HPS[1] |
|---|---|---|---|---|---|---|
| Investing in Stock Exchange | Summer 2012-2013 | 718 | Bachelor | Core | 2 | 32 |
| Finance 2 | Summer 2012-2013 | 1309 | Bachelor | Core | 3 | 48 |
| Tax Accounting | 2nd 2012-2013 | 4092 | Associate | Core - Elective | 2 | 32 |
| Tax Accounting | 2nd 2012-2013 | 4091 | Associate | Core - Elective | 2 | 32 |
| Finance Class | 2nd 2010-2011 | 7356 | Bachelor | Core | 3 | 48 |
| Tax Accounting | 1st 2013-2014 | 1603 | Associate | Core - Elective | 2 | 32 |
| Finance 1 | 1st 2011-2012 | 4883 | Bachelor | Core | 3 | 48 |
| Computer Application in Accounting - 2 | 1st 2011-2012 | 11511 | Bachelor | Core - Elective | 2 | 32 |
For more information about teaching hours and course credits at Islamic Azad University, refer to:
“Education is not to be viewed as something like filling a vessel with water but, rather, assisting a flower to grow in its own way”My teaching philosophy prioritizes creating a safe and engaging learning environment where students feel comfortable expressing their ideas and are encouraged to learn and grow. To achieve this, I use a variety of teaching techniques, including the use of technology and storytelling, to help students better understand complex concepts.
I believe in using concrete examples to explain abstract financial concepts, as this approach helps students connect theoretical concepts to real-life situations and provides them with practical knowledge. For instance, when teaching about present and future value, I relate it to a concrete example of a person saving for a vehicle purchase, which helps students better grasp the concept.
1- The Principles of Digital Pedagogy in Instruction and Teaching (2 ECTS) - (complited) - University of Eastern Finland
2- Introduction to Educational Leadership (5ECTS) - (complited) - Tampere University of Applied Sciences (TAMK)
1- Didactical design of courses in higher education (5ECTS) - (complited) - Åbo Akademi University
2- Teaching Practice 1 (5 ECTS) - (complited)- Åbo Akademi University
3- Digital learning in higher education (5 ECTS) - (complited)- Åbo Akademi University
4- Digital Didaktik in Higher Education (5 ECTS) - (complited)- Åbo Akademi University

1. The Teacher as an Expert of Learning – 28 ECTS
2. The Teacher as a Pedagogical Actor – 15 ECTS
3. Optional Studies – 5 ECTS