Seminar + Softwareproject: Data Science in the Life Sciences - Welcome!
This course offers an introduction to various types of data and analysis techniques which are typically used in the life sciences (e.g. omics technologies). The goal is to get a deeper understanding of advanced concepts and data analytical methods in the area of life sciences.
The focus will be on the following topics:
- acquisition and pre-processing of data from the area of life sciences,
- explorative analysis techniques,
- concepts and tools for reproducible research,
- theory and practice of methods and models for the analysis of data from the life sciences (statistical inference, regression models, methods of machine learning),
- introduction to methods of big data analysis.
After successful completion of this course, participants are able to evaluate, plan and conduct investigations in the life sciences using common methods.
SoSe21 focus: Data Science for fighting COVID-19
The COVID-19 pandemic is changing our lives globally. Although this is not the first time that humanity deals with a pandemic what is different today is the role of the technology in understanding how the virus is spreading, how it is mutating and what effects it is having in human lives across the world.
This is possible thanks, in part, to new datasets, advances in machine learning and analytical methods and computer power. Data science is a powerful tool that helps us fight this virus with applications such as early detection and diagnosis, contact tracing, projection of cases and mortality, development of drugs and vaccines, etc. However learning from data is a multi-step process which involves many assumptions associated with the data, learning algorithms, experimental evaluation, interpretation etc. It is important therefore to be aware of these assumptions and their potential impact on the conclusions drawn.
- The seminar is planned for Tuesdays, 08:00-12:00. The software project will be Tuesdays 2:15-5:45.
- The first meeting will take place jointly on Tuesday, 13.04.2021 at 2:15 pm.
- Meeting link:https://fu-berlin.webex.com/fu-berlin-en/j.php?MTID=m66268ca2702eb831402250bf546f9501 Meeting number:124 487 220 Password:hJ9A3eMCWm3
A combination of i) class-wide meetings (see first session slides for the exact plan) and ii) mentor-group meetings.
You need to participate in all class-wide meetings (formal requirement "Aktive Teilnahme")
For the class-wide meetings, please use the following:
Meeting number: 124 213 9900
Meeting password: yvF9RvfUy82
Weekly meeting Tuesday 2:15-5:45pm
- Meeting link:
- Meeting number:
- 124 150 4845
|6.7.2021||Final presentations groups 1, 7, 8, 9||Final presentations groups 1, 8, 12|
|13.7.2021||Final presentations groups 11, 12, 13, 14||Final presentations groups 7, 9, 11, 13, 14|
|End of July 2021||Final report due|
A good understanding of basic AI/ML models and algorithms is expected.
Based on class composition and background, impulse lectures might be offered to give an introduction to central content, like working with data, key (learning) methods and algorithms, how to read and present a paper, literature search etc - this will be discussed in the first session.