Data
Official data in SubjectManager for the following academic year: 2022-2023
Course director
-
Dr. Beáta BUGYI
associate professor,
Department of Biophysics -
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
-
Course headcount limitations
min. 1 – max. 20
Available as Campus course for 20 fő számára. Campus-karok: ÁOK 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 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.
Lectures
- 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
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. Bódis Emőke
- Dr. Bugyi Beáta
- Dr. Bukovics Péter
- Leipoldné Dr. Vig Andrea Teréz
- Zalai Zsófia