Overordnede kursusmål
Get students to adopt Python in their research.
Læringsmål
En studerende, der fuldt ud har opfyldt kursets mål, vil kunne:
- To use the Unix shell for working with files and directories,
pipes and filters, loops, shell scripts, and searching.
- Use Python for data analysis and task automation, including the
import of libraries, reading and plotting of data, selection and
filtering of data, writing of conditional statements and functions,
and debugging.
- Utilize basic version control of data and programming code with
Git.
- Adopt a modern development and reporting environment for Python
in the form of Jupyter notebooks.
- Clean, filter, transform and summarize tabular data with
Pandas
- Visualize data using the Python plotting libraries matplotlib
and altair
- Apply scikit-learn for basic Machine Learning such as
classification, regression, clustering, PCA etc.
- Apply biopython for basic DNA sequence handling
- Simulate and plan of experiments involving the creation of
recombinant DNA using pydna
- Perform basic image processing using scikit-image
Kursusindhold
With data generation and genetic engineering becoming evermore easy
in biology, life scientists and bioengineers are increasingly
facing challenges in processing and analyzing data and automating
experimental workflows in their line of work. For example, simple
tasks (such as designing primers) can become a huge drain on
scientists’ time as they repetitively copy and paste information
into web interfaces instead of running batch operations.
Furthermore, qualifications demanded of biotechnologists in the
industry are shifting away from pipetting towards the analysis of
data and automation of workflows. Therefore, it is essential that
life science and biotechnology PhD students are trained in the
computational tools needed for data analysis and task/lab
automation.
This PhD course aims to get programming novices (little to no
experience) off the ground with adopting Python (instead of Excel
and Word) in their daily work. In contrast to many existing Python
courses targeting computer scientists and software engineers, this
course is specifically tailored towards Biotechnology. It focuses
primarily on Python as a tool for data analysis and automation,
deemphasizing parts that are relevant to software development only.
Furthermore, participants are provided with knowledge about data
analytics and relevant machine learning methods, including best
practice approaches, troubleshooting and avoiding common pitfalls.
This course is based on the Software and Data Carpentry curricula
(
https://carpentries.org) and
style of teaching (live coding, hands-on exercise etc.). Since
1998, Software Carpentry has been teaching basic lab skills for
research computing to scientists and engineers and course materials
have continuously been adapted and tailored to their problems and
needs. The course materials for this course have been tailored
extensively by us towards life science and biotech related problems
that can be solved with Python and specifically target life science
and biotech PhD students.
The course is 100% interactive and relies on the proven approach of
teachers conveying the knowledge through live coding while the
participants follow along (supported by teaching assistants).
Furthermore, live coding is frequently interrupted by hands-on
exercises in which the participants develop programming solutions
to appropriate tasks on their own (with the help of the teachers
and teaching assistants).
This course will provide you with theoretical and practical
knowledge about:
* Obtain a working knowledge of Python basics and fundamentals
relevant to data analysis and automation.
* Adopt a modern development and reporting environment for Python
in the form of Jupyter notebooks.
* Obtain a good overview of key Python libraries covering
Bioinformatics/Sequence analysis (Biopython, pydna), data analysis
and statistics (Pandas), machine learning (scikit-learn), and image
processing (scikit-image).
Litteraturhenvisninger
https://software-carpentry.org/lessons/https://datacarpentry.org/lessons/Sidst opdateret
10. august, 2022
