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Education

Practical Applications of Biomathematics

Data

Official data in SubjectManager for the following academic year: 2024-2025

Course director

  • Bugyi Beáta

    associate professor,
    Department of Medical Biology and Central Electron Microscope Laboratory

Number of hours/semester

lectures: 12 hours

practices: 0 hours

seminars: 0 hours

total of: 12 hours

Subject data

  • Code of subject: OBF-GBM-T
  • 1 kredit
  • Biotechnology MSc
  • Optional modul
  • spring
Prerequisites:

-

Exam course:

Course headcount limitations

min. 5 – max. 20

Available as Campus course for . Campus-karok: GYTK TTK

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 TDK and diploma work, thesis, or other scientific presentations.

Lectures

  • 1. Introduction, data, data management and statistics. - Bugyi Beáta
  • 2. Introduction to software tools. - Bugyi Beáta
  • 3.

    Principles and applications of descriptive statistics.

    - Leipoldné Vig Andrea Teréz
  • 4.

    Principles and applications of descriptive statistics.

    - Leipoldné Vig Andrea Teréz
  • 5.

    Principles and applications of inductive statistics.

    - Gaszler Péter
  • 6.

    Principles and applications of inductive statistics.

    - Gaszler Péter
  • 7. Correlation and regression analysis. - Leipoldné Vig Andrea Teréz
  • 8. Correlation and regression analysis. - Leipoldné Vig Andrea Teréz
  • 9.

    Introduction to R.

    - Bugyi Beáta
  • 10.

    Introduction to R.

    - Bugyi Beáta
  • 11.

    Basics of biological data analysis in R.

    - Bugyi Beáta
  • 12.

    Basics of biological data analysis in R.

    - Bugyi Beáta

Practices

Seminars

Reading material

Obligatory literature

Literature developed by the Department

Can be found on MS Teams group of the course.

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

A maximum of 25% absence is allowed.

Mid-term exams

Grading policy

The grade is based on the result of

a written test,

or short (5-10 minute) presentations of Students’ projects or laboratory notebooks.

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

  • Bugyi Beáta
  • Gaszler Péter
  • Leipoldné Vig Andrea Teréz
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