CSC - Data Analysis with R - 1&2/12/2025 (external link)
The topics of this introductory course include data importing and exporting, handling real-life data sets and creating publication-ready plots with R.
Part of Scientific Computing in Practice lecture series at Aalto University.
Audience: Everyone from anywhere in the world with an internet connection using python in their research work.
About the course: This is a medium-advanced course in Python tools such as NumPy, SciPy, Matplotlib, and Pandas. It is suitable for people who have a basic understanding of basic Python and want to know some internals and important libraries for science. Read the learner personas to see if the course is right for you.
This is an online course streamed via Twitch (the CodeRefinery channel) so that anyone may follow along without registration. There is a "Notes" link (collaborative edited notes) which is used for asking questions during the course. If you would like to attend for to get live help and do exercises, you may register via one of the links below (this is highly recommended!). The actual material is here.
The course is organized by Aalto Scientific Computing in joint collaboration with CodeRefinery, and other collaborators from the Nordic countries such as KTH Royal Institute of Technology, University of Oslo, NAISS, CSC, University of Iceland, UPPMAX Uppsala University, Nordic-RSE, the Finnish Reproducibility Network.
Please read description and prerequisites here: https://scicomp.aalto.fi/training/scip/python-for-scicomp-2025
Time, date: 25,26,27.Nov, 09:50-15:00 (with 1h lunch break 12:00-13:00). Please note, times are in EET (CET+1)
Place: online
Registration: https://scicomp.aalto.fi/training/scip/python-for-scicomp-2025
Cost: Free
Course prerequisite requirements and other details: Please read the prerequisites at the course page https://scicomp.aalto.fi/training/scip/python-for-scicomp-2025
Additional course info at: scip -at- aalto.fi
The topics of this introductory course include data importing and exporting, handling real-life data sets and creating publication-ready plots with R.
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