Official School of Record Data Analytics and Artificial Intelligence in Cancer Research
An interdisciplinary field-based course introducing undergraduate students to the role of data analytics and artificial intelligence in contemporary cancer research — combining academic study with site-based learning and engagement with biomedical researchers and health organizations in Athens.
Data Science · Public Health · Biomedical Data Analytics
Offered by the Hellenic Center for Interdisciplinary Studies and Research (HCISR)
School of Record: Arcadia Abroad
Why This Course and Why Now?
The rapid growth in the volume and complexity of biomedical data has transformed how researchers investigate cancer and evaluate potential treatments. Artificial intelligence and machine learning are increasingly used to identify patterns in clinical variables, biological markers, and disease outcomes — creating a timely opportunity for students to explore how data-driven methods are applied in contemporary cancer research.
Through this program in Athens, students explore cancer data science beyond the classroom, engaging with biomedical researchers, health organizations, and real-world applications of data-driven research.
Learning Outcomes
Guided by expert faculty in data analytics and biomedical research, students develop analytical and applied skills through seminars, data interpretation workshops, case studies, guest lectures from biomedical researchers, and site-based academic engagement in Athens.
Interpret Biomedical Data Patterns
Examine and interpret patterns in biomedical datasets related to cancer research, understanding how clinical variables and biological markers relate to disease outcomes.
Analyze Clinical Variables
Analyze relationships between clinical variables, biological markers, and disease outcomes in curated cancer datasets using guided analytical exercises.
Understand Machine Learning in Research
Explain how analytical tools and machine learning methods — including Python-based tools, KNIME, and H2O-3 — are used in biomedical research.
Evaluate Data-Driven Evidence
Assess the strengths and limitations of data-driven approaches in cancer research, including ethical considerations in AI and health data.
Communicate Data-Informed Insights
Prepare a concise analytical brief interpreting a curated cancer dataset, communicating findings and research implications clearly and accurately.
Engage with AI Ethics
Reflect on the ethical dimensions of artificial intelligence in biomedical research, including data privacy, algorithmic bias, and social justice in oncology.
Who Is This Course For?
No formal prerequisites are required. The course is open to undergraduate students from diverse academic backgrounds. Basic familiarity with statistics, data analysis, or introductory programming may be helpful but is not required.
Data Science & Computer Science
Students interested in applying Python, machine learning, and AI tools to real-world biomedical research problems.
Public Health
Students seeking hands-on exposure to biomedical data analysis and how data-driven evidence informs health research and policy.
Biology & Biomedical Sciences
Students wanting to understand how computational tools and AI are transforming cancer research and clinical data analysis.
Pre-Med & Medicine
Medical students who want to understand how data analytics and AI are reshaping cancer diagnosis, treatment research, and clinical outcomes.
Health Informatics
Students preparing for careers at the intersection of healthcare and technology, where biomedical data management and analysis are central.
Statistics & Mathematics
Students interested in applying quantitative methods to biomedical datasets and understanding the analytical logic behind machine learning in health research.
Global Health & International Affairs
Students exploring how digital health technologies and AI-driven research are shaping global approaches to cancer prevention and treatment.
Curriculum: 3 Modules, 12 Sessions
The course moves from the foundations of biomedical data and cancer research, through machine learning tools and analytical methods, to applied data interpretation and AI ethics — culminating in a Final Data Interpretation Brief.
Biomedical Data Foundations
Introduction to biomedical data in cancer research, biomedical variables and research questions, interpreting patterns in cancer data, data visualization in biomedical research.
Machine Learning & Analytical Tools
Introduction to machine learning in cancer research, analytical tools in biomedical data research (Python, H2O-3, KNIME — including AutoML), interpreting analytical outputs, evaluating data-driven evidence.
Ethics, Synthesis & Presentation
Ethics in AI and biomedical data, biomedical research in practice (site-based activity), analytical synthesis workshop, course synthesis and student presentations.
Assignments
Assessment is designed to build progressively — from active engagement and analytical reflection to data interpretation exercises and a final integrative brief.
Seminar Participation & Discussion
Active participation in seminar discussions, engagement with assigned readings, and contributions to guided discussions on how biomedical datasets are used in cancer research, including guest lectures and site-based activities.
Guided Data Interpretation Exercises
Short analytical exercises based on curated cancer datasets provided by the instructor. Students examine patterns in clinical or biological variables and interpret simplified analytical outputs used in biomedical research.
Short Analytical Reflections
Short written reflections (~500 words) responding to specific case studies discussed in class. Reflections connect course concepts — data interpretation, analytical tools, and ethical considerations in AI and health data — with real-world examples from cancer research.
Final Data Interpretation Brief
A short analytical brief (~800 words) interpreting a curated cancer dataset. Students address a specific analytical prompt assigned by the instructor, focusing on interpreting observed patterns, discussing research implications, and evaluating the limitations of data-driven approaches in cancer research.
Program Experience
A rich, multi-format learning environment combining academic seminars, data interpretation workshops, guest lectures from biomedical researchers and data scientists, and structured site-based academic activities across Athens.
Faculty-Led Seminars
12 structured academic sessions covering biomedical data analysis, machine learning in cancer research, data visualization, AI ethics, and analytical synthesis.
Data Interpretation Workshops
Guided exercises with curated cancer datasets — students examine patterns in clinical variables and interpret simplified analytical outputs generated in Python, H2O-3, and KNIME.
Guest Lectures
Guest lectures from biomedical researchers, data scientists, and health research professionals — connecting analytical methods with real-world applications in cancer research.
Site-Based Academic Activity
Structured academic engagement with local research and health organizations in Athens, connecting data analytics with real-world perspectives on cancer care, prevention, and health research.
Information & Evidence Workshop
Site-based academic activity with local research and health organizations, connecting analytical methods with real-world applications in biomedical and public health research.
Orientation & Athens Exploration
Arrival, orientation, and guided exploration of the Asklepion on the south slope of the Acropolis — exploring the historical roots of health and healing and their place within the broader evolution of medicine.
Meet the Faculty
Learn from an expert practitioner in data analytics and health economics, embedded in the Greek academic and research landscape.
Theoktisti Ntoumani, MSc
Data Analyst & Health Economics Faculty — HCISR
Theoktisti Ntoumani is a data analytics professional with over a decade of experience in European, international, and national projects across both the private and nonprofit sectors. She specializes in advanced data-driven methodologies, predictive modeling, and computational approaches. MSc in Data Analytics; extensive experience in EU, ESA, EDIDP, IOM, UNHCR, and UNICEF-funded projects.
Course Details
- Course Code: HCISR_DATAPUBH 350_01012026
- Course Title: Data Analytics and Artificial Intelligence in Cancer Research
- Subject: Data Science, Public Health, Biomedical Data Analytics
- Credits: 3 undergraduate credits
- Contact Hours: 40
- Term: Summer 2027
- Dates: 21 June – 17 July 2027
- Location: Athens, Greece
- Format: On-site, field-based
- School of Record: Arcadia Abroad (Arcadia University | The College of Global Studies)
- On-site Provider: HCISR
Contact & Apply
This program is offered in partnership with Arcadia Abroad, the official School of Record, to support undergraduate students studying abroad in Athens.
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