- to understand the data collection, integration, processing, analysis and visualization issues as an integrated process - to be introduced to the main methodological and practical tools for data mining and data analytics
- techniques for data quality evaluation and preprocessing - techniques for numerosity and dimensionality reduction in data; - methods for frequent pattern analysis and association rules; - methods for classification problems; - cluster analysis; - R language programming.
The Information Management course aims at presenting the whole data journey as an integrated process. In particular, for each of the following steps, suitable methodologies are reviewed and compared: (a) data collection and statistical analysis, (b) data integration, (c) data governance, quality assessment and improvement, (d) data analytics, (e) data visualization. The core of the course is devoted to data analytics, and in particular to the advanced information management techniques that are collectively known as data mining. These techniques are oriented toward the automatic or semi-automatic extraction of knowledge from big data.