Course 2024-2025 a.y.

• Introduction to data management and analytics.
• Data management architectures: relational databases (OLTP, Data warehouse and SQL language).
• Data management architectures: Big data and NoSQL databases (distributed file system, Hadoop, Spark and Data Lake concept).
• Data analytics: Data understanding and data preparation.
• Data analytics: Models and statistical techniques applied to Big Data.
  • Regression and classification trees.
  • Ensemble methods (random forest and boosted trees).
  • Logistic regression.
  • Supervised Artificial Neural Networks.
• Data analytics: Models' performance evaluation.

Get the following competences:

• Big Data ingestion and management.
• Data preparation and cleaning.
• Machine learning algorithms application.
• Machine learning model evaluation.

• Improve his skills to manage and to take advantages of the huge availability of data nowadays produced by a great variety of sources.

• Guest speaker's talks (in class or in distance)
• Practical Exercises
• Collaborative Works / Assignments

Two different approaches are used: theoretical and applicative.

• A number of data ingestion procedures and machine learning data analysis case histories are shown, on Big and Small Data, using specific data management anf machine learning software.
• At the end of the course, students are able to reply all the procedures and analysis by themselves.

The assignments assess the student's ability to perform data analysis. The written exam aims mainly to assess the knowledge and the understanding of the procedures required for data ingestion and management and the main features of the machine learning algorithms presented in the course.

The written exam aims mainly to assess the knowledge and the understanding of the procedures required for data ingestion and management, the main features of the machine learning algorithms presented in the course and the ability to interpret  the content of a machine learning software output.

• M. KUHN, K. JOHNSON, Applied predictive modeling, Springer, 2013.
• P. WILTON, J. W. COLBY, Beginning SQL, Wrox, March 04, 2005.
• N. DASGUPTA, Practical Big Data Analytics, Packt Publishing,  2018.
• Teachers' slides.