DATA SCIENCE AND BUSINESS INTELLIGENCE

This course aims to introduce students to the founding ideas behind AI approaches for managing project data. This course focuses on the quantitative aspects of the master program, providing students with knowledge and skills in predictive data mining methods. It covers topics such as data analysis, AI approaches classification and regression, clustering and other unsupervised methods, and the exploitation of data mining software tools through Python programming. At the end of the course, students should be able to extract meaningful information from raw data and use the results of their analysis to make decisions and considerations.

At the end of the course, students should demonstrate a comprehensive understanding of the principles and applications of supervised and unsupervised learning algorithms, be able to apply the appropriate techniques to data, and manage the tools presented in lectures. They should also be able to compare different methods of data analysis, interpret results properly, and communicate their findings effectively in a comparative analysis report, including data representation.

Students should have achieved the learning outcomes of the course about Probability and Statistics.
It is desirable for the student to have knowledge of programming.

1. Introduction to Data Science
- Data-driven approaches and Big Data
- Data Science vs Business Intelligence
- What is Machine Learning and Data Mining: concepts of supervised and unsupervised approaches
- Kinds of data
- Managing a Data Science project
2. Clustering:
- Dimensionality reduction
- Clustering quality evaluation;
3. Supervised Learning
- Model training, validation, and tuning; Feature Engineering
- Classification; Regression; Decision Trees;
4. Similarity Search in Text
- Text representation; Tokenization, Stemming, Lemmatization; Vector space; Similarity measures;

- Lecture notes.
- Selected readings provided during the course.
- Python Data Science Handbook. Jake VanderPlas. O'Reilly. 2016.
- Python Machine Learning - Third Edition. Sebastian Raschka, Vahid Mirjalili. Packt. 2019.

A written exam and a project are conducted to evaluate students' learning outcomes. The written exam examines the student's theoretical knowledge through questions and exercises on the topics discussed during the course. The project requires the student to compare different tools applied to a given dataset or problem, select the most suitable solution, and deliver a report. This evaluates the student's ability to apply theoretical knowledge to a practical case. The written exam carries 2/3 of the final grade, whereas the project carries 1/3 of the final grade.

This course combines theory and hands-on sessions, using the Moodle platform to provide the material. Python programming is used to explore the topics further. Participants should bring their laptops to take advantage of the learning experience fully.

English

written and oral