Graduates of this course are able to: perform data engineering and analyses tasks; understand and apply machine learning and data analytics techniques for the systematic analyses of large datasets; identify, devise and evaluate meaningful features for analyses and modeling; evaluate and compare data engineering and analysis techniques for a given use case; Carry out reproducible, documented, and efficient data analyses and communicate the results; create datasets (including gathering, generation, cleaning, and validation thereof); understand the limitations, weaknesses, and potential biases of data collection and analysis methods.

Data set creation, data preparation, data quality, feature engineering, exploratory and descriptive dataset analyses, data visualization, dimensionality reduction, evaluation and errors, modeling and prediction, biases.

Interactive lecture

exam at the end of the semester.

cf. further readings on OLAT