Bi7550 Practical Analysis of Biological Data – Seminar

Faculty of Science
autumn 2021
Extent and Intensity
0/2/0. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: k (colloquium).
Teacher(s)
Mgr. Kateřina Kintrová, Ph.D. (seminar tutor)
prof. Mgr. Stanislav Pekár, Ph.D. (seminar tutor)
doc. RNDr. Jakub Těšitel, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. Jakub Těšitel, Ph.D.
Department of Botany and Zoology – Biology Section – Faculty of Science
Contact Person: doc. RNDr. Jakub Těšitel, Ph.D.
Supplier department: Department of Botany and Zoology – Biology Section – Faculty of Science
Timetable
Wed 12:00–13:50 D31/238
Prerequisites
Bi5560 Basics of statistics for biol. || Bi6050 Introduction to Biostatistics
Students are required to have their own research data at the beginning of the course.
Students need to be familiar with the R software including data manipulation and analysis, and graph plotting.
Knowledge of at least basic statistics (ANOVA, linear regression, general linear models) is required.
Besides compulsory prerequisities (Bi5560 or Bi6050), we recommend to complete some advanced course (Bi7540 or Bi7920, optionally Bi7921) before this seminar.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 16 fields of study the course is directly associated with, display
Course objectives
The course aims at guiding the students through the analysis of data originating from their own research during Master or PhD studies. The course facilitates the choosing and appropriate use of statistical tools in master/Ph.D. research and improves students’ skills in the presentation of data and analysis results. An essential aspect of the course is experience sharing among the students and discussions of the strategies of analysis.
The course specifically focuses on advanced data analyses such as multiple regression, multivariate statistics, analysis of structured data, etc. Individual topics will be discussed based on the nature of the students’ data.
Learning outcomes
After completing the course, the students will be able to: choose an appropriate method/model, discuss, interpret, and present the results of analysis (including the graphical outputs) in a way suitable for a scientific publication.
Syllabus
  • 1. Introduction: presentations of students introducing their research data in the first two classes.
  • 2. Group work on the data analysis. Students analyze their data and help the other with the analysis under the supervision of the course teachers. This part will extend across 2/3 of the semester.
  • 3. Discussion of interesting topics and common problems of data analysis. Enriched by ad-hoc presentations contributed by teachers or students.
  • 4. Presentation of the analysis results in the last two classes.
Literature
  • PEKÁR, Stanislav and Marek BRABEC. Moderní analýza biologických dat 1 - 1. díl. Zobecněné lineární modely v prostředí R. 2. přepracované vydání. Brno: Masarykova univerzita, 2020, 278 pp. ISBN 978-80-210-9622-6. info
  • ŠMILAUER, Petr and Jan LEPŠ. Multivariate Analysis of Ecological Data using CANOCO 5. 2nd ed. Cambridge: University Press, 2014, xii, 362. ISBN 9781107694408. info
  • PEKÁR, Stanislav and Marek BRABEC. Moderní analýza biologických dat 2. Lineární modely s korelacemi v prostředí R (Modern Analysis of Biological Data 2. Linear Models with Correlations in R). 1st ed. Brno: Masarykova universita, 2012, 256 pp. ISBN 978-80-210-5812-5. info
  • BORCARD, Daniel, François GILLET and Pierre LEGENDRE. Numerical ecology with R. New York: Springer, 2011, xi, 306. ISBN 9781441979759. info
  • BORCARD, Daniel, François GILLET and Pierre LEGENDRE. Numerical ecology with R. New York: Springer, 2011, xi, 306. ISBN 9781441979759. info
  • ZUUR, Alain F., Elena N. IENO and Graham M. SMITH. Analysing Ecological Data. Springer-Verlag New York, 2007, 672 pp. ISBN 978-0-387-45967-7. Available from: https://dx.doi.org/10.1007/978-0-387-45972-1. URL info
Teaching methods
Weekly practicals dedicated to the presentation of the research or data analysis conducted in the R software. Students are required to bring their OWN LAPTOP.
Assessment methods
Active participation in the practicals is required. Essay structured as methods and results sections of a research paper/thesis supported by high-quality graphical outputs based on the analysis of student’s data.
Language of instruction
English
Further Comments
Study Materials
The course can also be completed outside the examination period.
The course is taught annually.
The course is also listed under the following terms Spring 2011 - only for the accreditation, Spring 2011, spring 2012 - acreditation, Spring 2013, Spring 2015, Spring 2017, Spring 2020, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (autumn 2021, recent)
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