Practical Applications of Biomathematics


Official data in SubjectManager for the following academic year: 2022-2023

Course director

Number of hours/semester

lectures: 12 hours

practices: 0 hours

seminars: 0 hours

total of: 12 hours

Subject data

  • Code of subject: OAE-GBM-T
  • 1 kredit
  • General Medicine
  • Elective modul
  • spring


Course headcount limitations

min. 1 – max. 20

Available as Campus course for 20 fő számára. Campus-karok: ÁOK GYTK TTK


The medical, pharmaceutical, and biotechnological sciences and research methodologies increasingly rely on the use and management of databases and the tools of descriptive and inferential statistics. The course provides overviews about the basics and applications of database management and statistical software packages and support. The Students can get experience and practical skills by solving software-based exercises and problem solving (e.g., Microsoft Excel, RStudio, GPower, Microcal Origin, Minitab). Special emphasis is laid on the application of modern teaching methods (flipped learning, thematic student presentations, and projects, discussion of the Students’ research results, and case studies) in order to provide students with the knowledge, understanding, and hands-on experience in database management and in the mathematical and software toolkit of statistical analysis. The course provides an opportunity to acquire basic mathematical, computer science, and statistical skills that can be helpful in other natural sciences, as well as in preparing diploma work, thesis, or other scientific presentations.


  • 1. Bevezetés: adatok, adatmenedzsment, a statisztika alapelvei. - Dr. Bugyi Beáta
  • 2. Programok, szoftveres felületek bemutatása. - Dr. Bugyi Beáta
  • 3. A leíró statisztika alapelvei, alkalmazások. - Dr. Bugyi Beáta
  • 4. Adatok, adatmenedzsment. - Zalai Zsófia
  • 5. Adatok, adatbázisok. - Zalai Zsófia
  • 6. Az induktív statisztika alapelvei, alkalmazások. - Dr. Bódis Emőke
  • 7. Korreláció-, és regresszióanalízis. - Dr. Bódis Emőke
  • 8. Modellek, becslés és előrejelzés. - Dr. Bukovics Péter
  • 9. Szakirodalomból származó esettanulmányok, saját kutatási eredmények bemutatása és megbeszélése 1. Műhelymunka. - Dr. Bukovics Péter
  • 10. Szakirodalomból származó esettanulmányok, saját kutatási eredmények bemutatása és megbeszélése 2. Műhelymunka. - Dr. Bukovics Péter
  • 11. Hallgatói prezentációk, projektek. - Leipoldné Dr. Vig Andrea Teréz
  • 12. Összefoglalás. Projektprezentáció. - Leipoldné Dr. Vig Andrea Teréz



  • 1. Introduction, data, data management and statistics.
  • 2. Introduction to software tools.
  • 3. Principles and applications of descriptive statistics.
  • 4. Data, data management.
  • 5. Data, databases.
  • 6. Principles and applications of inferential statistics.
  • 7. Correlation-, and regression analysis.
  • 8. Models, estimation and predictions.
  • 9. Discussion of case studies and Students’ research results 1. Workshop.
  • 10. Discussion of case studies and Students’ research results 2. Workshop.
  • 11. Students’ presentation and projects.
  • 12. Revision. Project presentation.

Reading material

Obligatory literature

Literature developed by the Department

Homepage of the Department of Biophysics:


Recommended literature

Statistics Openstax, ISBN-10: 1-947172-05-0, ISBN-13: 978-1-947172-05-0
Allan G. Bluman: Elementary statistics, ISBN 978–0–07–338610–2
Myra L- Samuels, Jeffrey A. Witmer, Andrew A. Schaffner: Statistics for the life sciences, ISBN-13: 978-1-292-10181-1
James Stewart, Troy Day: Biocalculus, ISBN-13: 978-1-133-10963-1
J. Pezzullo: Biostatistics for dummies, 2013, Wiley, ISBN 978-1-118-55399-2

Conditions for acceptance of the semester

Maximum of 25 % absence allowed

Mid-term exams

Grading policy
The grade is based on the average result of the following evaluation methods:
result of a written test (expected date: week 12th),
result of Students’ presentations and case studies.
The grade can be improved based on the rules of the Code of Studies and Examinations.

Making up for missed classes

The opportunity to make up for absence can be discussed with the course leader.

Exam topics/questions

No exam is scheduled in the exam period.


Instructor / tutor of practices and seminars

  • Dr. Bódis Emőke
  • Dr. Bugyi Beáta
  • Dr. Bukovics Péter
  • Leipoldné Dr. Vig Andrea Teréz
  • Zalai Zsófia