Universität Wien

300155 UE Introduction to Biological Data Science (2023W)

(Python and R)

5.00 ECTS (3.00 SWS), SPL 30 - Biologie
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 15 participants
Language: English

Lecturers

Classes

Lectures: 13:00-15:00 Wednesdays, University of Vienna Biology Building, 5th floor meeting room (Deparment of Cognitive Biology), Rm 5.021

Intro to data science with biological, cognitive and neural data.
Participants are expected to have some experience with both R and Python programming.
If you do not have Python experience, please complete the basic tutorials on learnpython.org, BEFORE the start of the course.

Information

Aims, contents and method of the course

The goal of this course is to introduce both theoretical and practical aspects of the most important data science methods in the biological sciences. Classes will consist of a combination of lectures and tutorials.
The course will start with an introduction to both the R and the Python programming languages, followed by some introductory concepts of data analysis. Next, we will focus on data visualisation methods, followed by an introduction to machine learning.
At the final part of the we will look at more specialised methods for biology, focusing on the analysis of behavioural, neural and evolutionary data.

Assessment and permitted materials

Programming homeworks, final project and a final exam.

Active class participation is expected from all students.

Minimum requirements and assessment criteria

Active participation in class and programming tutorials.

[Assessment criteria]
1: >87%
2: 76 - 87%
3: 64 - 75%
4: 51 - 63%
5: <=50%

Examination topics

Able to use both Python and R for basic data analysis and visualisation tasks

familiar with the most important concepts of data modelling and machine learning

has in depth understanding of some at least one complex biological datasets (Phylogenetic trees, behavioural time-series, neural data), demonstrated in the final project.

Reading list

Zhang, N. (2020). A tour of data science: learn R and Python in parallel. CRC Press.

Statistical Thinking for the 21st Century:
https://statsthinking21.org/
https://statsthinking21.github.io/statsthinking21-python/index.html
https://statsthinking21.github.io/statsthinking21-figures-R/intro.html

Revell, L. J., & Harmon, L. J. (2022). Phylogenetic comparative methods in R. Princeton University Press.

Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O'Reilly Media, Inc.".

Association in the course directory

CoBeNe 3

Last modified: Th 12.10.2023 13:28