Introduction to Scientific Computing for Neuroscientists (9CP)

Automated data processing and analysis are integral components of modern scientific work. They are essential for data analyses, experiment control, managing vast amounts of data, and conducting simulations of neuronal processing. Automation plays a critical role in ensuring the reproducibility of scientific findings and objectivity of analyses. Solid knowledge of statistical methods is critical to justify conclusions and evaluate results. Therefore, proficiency in higher programming languages such as Python, Matlab, or R is indispensable for aspiring scientists. Moreover, a strong
grasp of programming, data analysis and statistics also qualifies for various job opportunities outside sciences.

You will

• learn a programming language and learn to handle modern programming tools to run up-to-date analyses on neuroscientific data.
• train your skills to transform scientific questions into analysis algorithms.
• gain a solid understanding of statistical methods used in neurobiological research.
• be able to create publication-ready figures that accurately represent the data directly from within the analysis pipeline.
• work on a small data-analysis project and train your presentation skills.