Use basic statistical concepts and techniques and perform appropriate statistical tests
Choose and apply suitable visualization techniques
Analyze and model data using linear regression, clustering, decision tree mining and association rules learning
Read and make simple database schemes and simple queries to a database.
Clean data, choose and apply data transformations, data reduction, and data discretization
Understand, interpret, and document data and information in the context of realistic scenarios, structure open problems along the phases of the data life cycle, formulate hypotheses, incorporate ethical considerations and reflect on analysis strengths and limitations
Use tools for implementing data engineering tasks (Python with Jupiter Notebooks) in a structured way
Choose and communicate interesting findings in the language understandable for their end user (visually or textually).
