This course provides a basic understanding, knowledge and practical skills of data-science as well as the application of the main data-driven methods for analysing and processing material science data. The students understand the importance of a statistical description of data sets and can apply the concepts of moments of a distribution (average, median, variance, skewness) on arbitrary data sets. They can perform the significance test to identify influence parameters that show a strong impact on the data sets. Furthermore, they are able to formulate and test different hypotheses and to provide a significance level for the acceptance or rejection of a hypothesis. The students will be able write their own Python code to conduct such tests and apply it to formulating mathematical (statistical learning) models to describe data sets. The students are given an overview of statistical methods for data analysis and processing, including main steps such as data preparation, outlier detection,
identification of main influence parameters, formulation and solution of regression and classification problems. The lecture materials and practical hands-on in Python are provided for each topic.
- Kursleiter/in: Irina Roslyakova