About the course
This course builds on the module Data Analysis and Data Management I, aiming to equip students with advanced concepts and tools as well as state-of-the-art strategies for data analysis and research data management. Participants will gain hands-on experience with key software platforms and libraries within a Python Notebook environment, including pandas, matplotlib, bokeh, and scikit-learn.
The course comprises lectures introducing theoretical concepts and tools, alongside hands-on sessions where students apply their knowledge to real-world data analysis challenges from physics experiments. The semester is structured into progressively complex chapters:
- Fundamentals of Data Processing: Introduction to data fitting and cleaning strategies for one-dimensional datasets.
- Advanced Data Management and Analysis: Handling higher-dimensional datasets and large-scale data analysis.
- Global Data Analysis: Focus on Fourier analysis and its applications.
- Machine Learning for Data Analysis: Introduction to fundamental machine-learning concepts, including decision trees, random forests, deep learning, and artificial intelligence.
Goals of the course
Students have a solid understanding of data analytics fundamentals, Fourier analysis, and machine-learning approaches for data analysis. They understand the structure of research data and are familiar with the FAIR principles for sustainable data management. Additionally, they are proficient in analyzing and managing large datasets and have hands-on experience with relevant software tools, with a focus on specialized Python packages and programming techniques.
- Trainer/in: Régis Decker
- Trainer/in: Ralph Ernstorfer
- Trainer/in: Tommaso Pincelli