The term Data Mining means a set of techniques and tools used to explore large amounts of data, with the aim of identifying / extracting significant information / knowledge, so as to make them available to decision-making processes.
This course aims to provide the fundamentals of the discipline, focusing the study on the most important Data Mining techniques of current application / industrial interest.
The course combines the theoretical knowledge of Data Mining with the use of open source Python software.
The course participants will be guided in finding patterns within datasets, and through the tools provided by Python they will learn to preprocess data, perform Clustering, Classification and Forecasting operations.
In summary, the educational objectives of teaching and the expected results of learning are the following:
- Acquire knowledge and ability to use the Python language and typical data mining libraries, in order to process datasets and execute machine learning algorithms.
- Acquire basic knowledge for data pre-processing. At the end of the course, the student will be able to deal with the main issues related to the data and, independently, face a real problem in the best way.
- Understanding of some machine learning algorithms, supervised or not, in order to extract information from the data. The training objective is to equip students with the knowledge necessary to solve real problems by identifying and developing the appropriate machine learning algorithms. The course will consist of a theoretical part describing classic Machine Learning algorithms and a practical part in which these methodologies are applied using the Python language.
- Ability to learn new methodologies typical of web data mining for the main techniques of analysis of the traces that users leave on the web. Learning will be supported by numerous developed case studies.