Practical Applications of Biomathematics

Data

Official data in SubjectManager for the following academic year: 2023-2024

Topic

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 of 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 offers the opportunity to acquire basic mathematical, computer science, and statistical skills that can be helpful in other natural sciences and in preparing diploma work, thesis, or other scientific presentations.

Lectures

• 1. Introduction, data, data management and statistics. - Dr. Bugyi Beáta
• 2. Introduction to software tools. - Dr. Bugyi Beáta
• 3. Principles and applications of descriptive statistics. - Zalai Zsófia
• 4. Principles and applications of descriptive statistics. - Zalai Zsófia
• 5. Principles and applications of inductive statistics. - Dr. Bugyi Beáta
• 6. Principles and applications of inductive statistics. - Dr. Bugyi Beáta
• 7. Correlation and regression analysis. - Leipoldné Dr. Vig Andrea Teréz
• 8. Correlation and regression analysis. - Leipoldné Dr. Vig Andrea Teréz
• 9. Workshop. Discussion of case studies and Students' research results. - Dr. Bugyi Beáta
• 10. Workshop. Discussion of case studies and Students' research results. - Dr. Bugyi Beáta
• 11. Workshop. Students' projects and presentations. - Dr. Bugyi Beáta
• 12. Workshop. Students' projects and presentations. - Dr. Bugyi Beáta

Practices

Seminars

• 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: https://aok.pte.hu/en/egyseg/10/oktatasi-anyagok

Notes

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.

Examiners

Instructor / tutor of practices and seminars

• Dr. Bugyi Beáta
• Leipoldné Dr. Vig Andrea Teréz
• Zalai Zsófia