This course introduces the principles and practical methods of conducting research in computer science and data science. Students learn how to identify and formulate research problems, conduct systematic literature searches and reviews, develop research questions, design experiments, analyse data, evaluate research results, and communicate findings through scientific papers and presentations. The course combines short lectures with hands-on individual and group activities, peer review, academic writing, research ethics, and proposal development.
Research Methodology for Computer and Data Science provides a practical introduction to the complete research process in computing and data-driven disciplines. The course guides students from the identification of a research problem through literature review, research-question formulation, methodological design, experimentation, data analysis, scientific writing, peer review, and research proposal development.
The course is strongly practice-oriented. Students work with authentic research topics and progressively develop a literature review, identify research gaps, formulate research questions, design appropriate research methods, and communicate their findings. Particular attention is given to academic integrity, responsible research practices, citation, plagiarism prevention, reproducibility, and ethical considerations in data-driven research.
Students participate in individual and collaborative activities, including research-topic selection, literature analysis, peer review of scientific articles, scientific writing using LaTeX, research presentations, and proposal development. The course culminates in the preparation of a research-based mini-proposal that can serve as a foundation for further research, thesis work, or project development.
The course can be delivered in a hybrid format, allowing students from different CHARM-EU partner universities to participate remotely while maintaining interactive classroom activities and collaborative work.
At the end of the course, the learner will be able to:
1. identify and formulate relevant research problems in computer science and data science;
2. conduct and critically evaluate a literature search using appropriate academic databases and scholarly sources;
3. synthesize academic literature and identify research gaps that motivate research questions;
4. formulate clear and researchable research questions based on identified research gaps;
5. design appropriate research methodologies, experiments, and data-collection strategies;
6. analyse and interpret research data using appropriate qualitative and quantitative methods;
7. evaluate research results using suitable metrics, comparisons, and scientific reasoning;
8. apply principles of academic integrity, research ethics, responsible data use, and appropriate citation practices;
9. prepare and communicate scientific work through academic papers, presentations, and peer-review activities;
10. develop a structured research proposal including objectives, research questions, methodology, expected outcomes, and relevant literature.
No formal prerequisites. Basic familiarity with computer science or data science concepts is recommended.
References:
[1] Booth, W. C., Colomb, G. G., Williams, J. M., Bizup, J., & Fitzgerald, W. T. (2016). The Craft of Research (4th ed.). University of Chicago Press.
[2] Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). SAGE Publications.
[3] Kothari, C. R., & Garg, G. (2019). Research Methodology: Methods and Techniques (4th ed.). New Age International Publishers.
[4] National Academies of Sciences, Engineering, and Medicine. (2017). Fostering Integrity in Research. The National Academies Press. https://doi.org/10.17226/21896
[5] National Academies of Sciences, Engineering, and Medicine. (2019). Reproducibility and Replicability in Science. The National Academies Press. https://doi.org/10.17226/25303
[6] ACM. (2018). ACM Code of Ethics and Professional Conduct. Association for Computing Machinery. https://www.acm.org/code-of-ethics
The course combines short interactive lectures, guided discussions, individual research activities, collaborative group work, peer review, practical writing exercises, research presentations, and instructor feedback.
Students work with authentic research topics throughout the course and progressively develop their own literature review, research questions, methodological design, scientific writing, and research proposal. Technology-enhanced learning tools are used for literature searching, collaborative work, scientific writing, reference management, data analysis, and presentation.
The course uses a student-centred and research-based approach in which students actively develop and communicate their own research ideas rather than only receiving theoretical instruction.
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