In the big data era, programming skills are required in order to
efficiently handle datasets. Scientists in natural sciences are commonly
exposed to big datasets facing a most demanding task. It is most common
that SLU PhD students have to analyze datasets that require different
skillsets to traditional tools like Excel. Moreover, a common situation
requires datasets to be transformed in various formats in order to be
analyzed by specialized software. The latter in the case of big data can
be achieved only programmatically. Python is currently the most popular
programming language for data science. The latter could not be possible
without the Pandas library which greatly facilitates a wide range of
operations needed for data analysis, like transforming data format,
combining data stored in different files and producing insightful
summaries regarding data quality and interpretation. Moreover, the
extensive graphic-related ecosystem of Python like the Seaborn library
offers tremendous possibilities for constructing informative graphs both
for facilitating data interpretation and for publication purposes.
The course format will include morning lectures that will be followed by
practical exercises. The interactive development environment of Jupyter
(www.jupyter.org) will be used throughout the course. Basic Python
syntax will be introduced and thereafter students will gradually build
core data science related skills. In particular, the students will be
introduced to the Pandas library and practice data manipulation and
aggregation techniques in large datasets. Finally, the students will
gain experience in producing informative graphs using the Seaborn
library or similar.
Expected study time
Total: 54 hours
Own study prior to course: 10 hours
Lectures: 14 hours
Computer assignments: 30 hours
Extent:
2 credits
Prerequisites
Admitted to a PhD or residency program in biology, medicine, nursing,
veterinary medicine, animal science, food science, nutrition or similar
topics. No prior programming experience is required.
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