DATA MANAGEMENT

This 6 ECTS elective course is taught within the "affine/integrative" category of the "Economics, markets and finance" degree during the fourth term. The course is an introduction to big data management through relational databases and data analysis/visualization tools. The goal of the "data management" discipline is to effectively extract raw data, organize it in a database, and finally manipulate and analyze it through suitable data manipulation and visualization languages. This course has a strong practical component whose goal is to introduce the students to data management software tools. Particular attention will be spent to studying relational databases, the SQL language, and Python libraries for data analysis and visualization.

At the end of the course, the student will be able to manage and analyze big amounts of data using database and data visualization software tools. The goal of the course is to discuss the state-of-the-art and future developments of the "data management" field. The expected learning outcomes are divided into:

1. Knowledge and understanding:
At the end of the course the student will be able to recognize the most suitable data management techniques for solving specific data-intensive problems.

2. Ability to apply knowledge and understanding:
At the end of the course the student will be able to apply data management techniques (SQL databases and Python data visualization libraries) in order to solve typical big-data management and analysis problems.

3. Ability to make judgments:
At the end of the course the student will be able to apply the knowledge gained during the course to:
- design and manage efficient databases on big data sets through the SQL language.
- filter data and extract the necessary information for identifying interesting and useful trends.
- visualize the results of the analysis through professional data visualization software tools (Python Pandas and Seaborn)

Basic notion about calculus and probability.

Database systems:
- Introduction to databases
- Relational algebra
- Entity-Relation diagrams
- Logical Design
- Physical design and interrogation: SQL

Data analysis with Python:
- Pandas: series and dataframes
- Data visualization with Seaborn

- Database Systems: The Complete Book, Hector Garca-Molina, Jeffrey Ullman, and Jennifer Widom. Pearson Prentice Hall.
- Python for Data Analysis”, 2nd Edition, Wes McKinney (2017). O'Reilly Media, ISBN: 9781491957660

The exam consists of a group project and oral discussion.

Slides and Practical Sessions.

English

oral