Data analysis workflows with R and Python¶
Attending the course 5.-14.10.?
See the course page here, below is the course material.
Data analysis is nowadays at the center of almost all scientific fields. Whether a researcher is doing experiments, running simulations or analyzing datasets, at some point of their career they will be required to do data analysis.
R and Python are two languages that have a rich and powerful data analysis libraries and many researchers use them to build their data analysis workflows. However, these libraries have been designed to work optimally in certain types of workflows. Thus if one wants to create reproducible, scalable and efficient data analysis workflows it is important to understand how to design a good workflow from the get-go.
This course contains four chapters:
First chapter is about understanding how data analysis workflows are commonly designed and how one should go about designing a new data analysis pipeline.
Second chapter is about data ingestion, tidy data format, and efficient data formats for input and output.
Third chapter is about calculating statistics, doing modeling and utilizing data analysis libraries.
Fourth chapter is about how one should think about scaling and how it can achieved.
Throughout this course the material has an underlying theme of three principles that one should keep in mind throughout the course:
Recognizing a pattern - Different languages, libraries, workflows etc. have common features. We will try to find these patterns so that we familiarize ourselves with them.
Replicating a pattern - After we have recognized some patterns it is a good idea to copy said pattern and see if it works as we assumed.
Generalizing a pattern - Once we have a grasp of a how our pattern works we can think about how we could use it in a different context. That is, how we can generalize it.
Prerequisites
Knowledge of Python or R in the context of scientific computing. If you’re unsure, our Python for Scientific computing-course is a good starting point.
Software installed via conda as described in installation instructions.
It will help if you know some Jupyter, since that’s how we do our exercises. You can study some in the CodeRefinery lesson.
Warning
Materials are still being worked on and the current state is not reflective of the eventual state.
120 min |
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120 min |
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120 min |
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120 min |
Who is the course for?¶
Researchers who are using or will soon be using R and Python for data analysis, who know how to program with these languages, but do not necessarily know what are the best practices for data analysis.
The course material is available in both R and Python, but this is not a course on the basics of scientific programming. If you wish to prep up your scientific programming skills, we recommend taking our Python for Scientific Computing-course.