DATA MANAGEMENT

This 6 ECTS elective course is taught within the "affine/integrative" category of the "Economics, markets and finance" degree during the second 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 tools. This course has a strong practical component whose goal is to introduce the students to data management using python. Particular attention will be given to studying relational databases 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 (Python pandas and data analysis and 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 efficient databases on big data sets.
- 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)

No specific requirements.

Database systems:
- Introduction to databases
- Relational algebra
- Entity-Relation diagrams
- Application of the learned principles in Python using the Pandas library

Data analysis:
- Data cleaning and preparation
- Summarizing and visualizing data
- Some algorithms for information extraction
- Extracting information from networks

- Database Systems: The Complete Book, Hector Garca-Molina, Jeffrey Ullman, and Jennifer Widom. Pearson Prentice Hall.
- Mathematical statistics and data analysis. John A. Rice. Cengage Learning.
- Python for Data Analysis. Wes McKinney. O'Reilly Media.

The exam for this course consists of a 120 mins written exam. The exam will consist of two parts. In a first part (30 mins), you will solve programming exercises on a computer. In the second part (90 mins) you will answer questions concerning the theoretical material of the course.

written

28-30L: the student masters the topics presented in the course;
he is capable to think computationally and to manage and analyze data;
his exposition is very clear and scholarly

26-27: the student has a good knowledge of the topics presented in the course;
he generally succeeds to think computationally and to manage and analyze data;
his exposition is very clear

24-25: the student does not thoroughly know all topics presented in the course;
he mostly succeeds to think computationally and to manage and analyze data;
his exposition is clear

22-23: the student has a mostly superficial knowledge of the topics presented in the course;
he usually succeeds to think computationally and to manage and analyze data;
his exposition is not always clear

18-21: the student has a very superficial knowledge of the topics presented in the course;
he has problems to think computationally and to manage and analyze data;
his exposition is confused

Blackboard and practical sessions.