ALMA MATER STUDIORUM - UNIVERSITÀ DI BOLOGNA

Python tools for Data Analysis and Machine Learning

L'iscrizione a questo corso è possibile solo su invito

This MOOC was produced as part of the Edvance project – Digital Education Hub per la Cultura Digitale Avanzata. The project is funded by the European Union – Next Generation EU, Component 1, Investment 3.4 “Didattica e competenze universitarie avanzate".

About This Course

The course aims to introduce fundamental concepts and tools to support data analysis, data processing, and Machine Learning (ML) workflows, in Python. Specifically, its purpose is to describe the essential Python libraries enabling the analysis and preparation of datasets and the construction of models for supervised (e.g., classification, regression) and unsupervised learning tasks (e.g., clustering, dimensionality reduction, and anomaly detection).

The course is organised in four weeks:

Week 1: Introduction to Machine Learning Workflows and Python Notebooks
Week 2: Fundamental Python libraries: Matplotlib, NumPy, Pandas
Week 3: Machine Learning Workflows with Scikit-Learn & Co.
Week 4: Putting it all together: Examples of Machine Learning in Python

Target

Studenti e professionisti con competenze digitali avanzate. Students and professionals with advanced digital skills

Outcomes

At the end of the course, the student is expected to be able to:

Describe and distinguish the main categories of machine learning problems and select appropriate modeling approaches for given real-world problems.
Use the Python environment and core libraries (NumPy, Pandas, Matplotlib) to represent, manipulate, and visualize data.
Analyze and prepare datasets by loading, exploring, cleaning, and preprocessing data for machine learning tasks.
Build and apply basic machine learning workflows in Python using Scikit-Learn.
Evaluate machine learning models by interpreting performance metrics and comparing alternative solutions.

Requirements

Basics of mathematics and linear algebra (functions, matrices)
Familiarity with the Python programming language

Activities

Video lessons and multiple-choice questions
In-depth analysis with text material and code examples

Open Badge

Participants who complete the course will be awarded an Open Badge from BESTR. Participants who log in to the platform with University of Bologna, EDUGAIN, CIE or Spid authentication and answer correctly at least 60% of the questions in total, will receive an email with instructions to download their Open Badge from the BESTR website the day after the completion of the course.

Subtitles

English subtitles available.

For better understanding, subtitles are available for each video and can be activated or not. If you want to revise some crucial passages you can move through the video content and click on the attached text.

EQF level

EQF Level 6 (Bachelor’s)

ISCED-F

0613 Software and applications development and analysis

Categories

ENG: Transdisciplinarity; Business and Management; Sustainability; Education; Personal development; Math, Phisics and Engineering; Design and Architecture; Arts and humanities; Information Technology and Computer Science; Health and Medicine

SDGS

SDG 4 – Quality Education - Development of advanced digital, data, and programming skills.

FAQ

For further information, see FAQ page.

Course Professor

casadei

Roberto Casadei

Roberto Casadei is a senior assistant professor at the University of Bologna (Italy). He has a PhD in Computer Science & Engineering from the same university, with a thesis awarded by the IEEE TCSC. His research interests revolve around software engineering and distributed artificial intelligence. He has 80+ publications in international journals and conferences on topics including collective intelligence, swarm robotics, self-* systems, and IoT/CPS. He also contributed to open-source programming frameworks and simulators (e.g., ScaFi). He have served in the organizing and program committees of many conferences such as ACSOS, DisCoTec, ICCCI, AAAI, ECAI, and SAC, as a guest editor and reviewer for renowned international journals, and associate editor of Elsevier IoT, JAISCR, and Wiley CCPE. He received the IEEE TCSC Award for Excellence'24 (ECR), for contributions to "paradigms supporting the engineering of large-scale collective and self-organizing systems". In 2025, he received, with his project FoMaSE on macro-programming, the FIS3 Starting Grant.