Course on Data Profiling

Lecturer: Prof. Felix Naumann

May 9 – May 13, 2022 (12 hours)
Monday - Thursday
11:00-12:30 Lecture 1
14:00-15:30 Lecture 2
16:00 – 17:00 Hands on session

11:00-12:30 Lecture 1
14:00-15:30 Lecture 2
16:00 – 17:00 Exam

Data profiling is the process of examining the data available in an existing data source and collecting statistics and information about that data. It encompasses a vast array of methods to examine data sets and produce metadata. Among the simpler results are statistics, such as the number of null values and distinct values in a column, its data type, or the most frequent patterns of its data values. Metadata that are more difficult to compute involve multiple columns, such as inclusion dependencies or functional dependencies. Data profiling is relevant as a preparatory step to many use cases, such as query optimization, data mining, data integration, and data cleansing. Topics of the lecture include the deficient detection of unique column combinations, functional dependencies, inclusion dependencies, order dependencies, and denial constraints, and the semantic interpretation of profiling results.

Written exam – 1h

CFD: 4