September 28 – November 9
A hands-on introduction to how modern AI tools work and how to apply them effectively across teaching, research, and administrative contexts.
- Course format: Online
- Type of program: E-Skill program
- Target group: Academic and Non-academic Staff
- Language: English
- Cost: Free
- Available places: 60
- Teaching hours: 27
- ECTS: 3
Deadline for admission is 16 September 2026
Audience
This course is designed for university professionals including academic staff, researchers, and administrative staff who want to gain a practical understanding of data analysis and Artificial Intelligence tools. It focuses on the use of tabular and textual data analysis techniques, machine learning basics, and Large Language Models to support everyday activities such as data processing, information retrieval, documentation, and workflow automation. It also introduces the fundamentals of AI agents and their application to simple automated tasks.
Pre-requisites
- B2 or similar level of English is necessary.
- Basic knowledge of Python is required (e.g., understanding variables, functions, and basic scripting).
- Basic digital skills, such as using documents and email, are required.
- A Google account is required to access and run the practical activities on Google Colab notebooks.
Short Description
This course provides a practical introduction to data analysis, machine learning, and Generative Artificial Intelligence, with a particular focus on applications relevant to university technical and administrative staff.
Participants will learn how to acquire, manipulate, analyze, and visualize tabular data using Python-based tools, and how to develop machine learning workflows for classification, regression, and clustering tasks. The course will then introduce Large Language Models (LLMs) and Generative AI techniques for text analysis, semantic search, information extraction, and workflow automation.
The course combines theoretical foundations with extensive hands-on activities based on Google Colab notebooks and real-world case studies. Particular attention will be devoted to practical problem solving, enabling participants to apply AI and data-driven approaches to common institutional scenarios involving both structured and unstructured data.
Participants will:
- explore AI agents and workflow automation solutions.
- learn how to manipulate and transform tabular datasets using Pandas;
- create effective visualizations and perform exploratory data analysis;
- understand and apply machine learning techniques for classification, regression, and clustering;
- develop complete data analysis pipelines using Scikit-learn;
- understand the fundamental principles of Generative AI and Large Language Models;
- apply semantic search, text classification, and information extraction techniques;
- design effective prompts for AI-assisted tasks;
- explore AI agents and workflow automation solutions.
Content
Module 1 – Tabular Data Management with Pandas
- Reading and writing data from different file formats;
- Series and DataFrame structures;
- Data selection, indexing, and filtering;
- Data cleaning and transformation techniques;
- Practical exercises on real-world data manipulation tasks.
Module 2 – Data Visualization
- Principles of data visualization;
- Introduction to Matplotlib and Seaborn;
- Common visualization techniques and plot types;
- Univariate and multivariate data analysis;
- Visualization-driven exploratory data analysis.
Module 3 – Machine Learning with Scikit-learn
- Introduction to machine learning workflows;
- Data preprocessing and feature transformations;
- Scikit-learn estimators and pipelines;
- Classification methods and evaluation metrics;
- Regression methods and evaluation metrics;
- Clustering techniques and dimensionality reduction (PCA);
- End-to-end machine learning pipelines.
Module 4 – Text Analytics with Large Language Models
- Introduction to Artificial Intelligence and Large Language Models;
- Transformer-based architectures;
- Semantic search and Retrieval-Augmented Generation concepts;
- Text classification and clustering;
- Prompt engineering techniques;
- Practical applications on institutional documents and textual data.
Module 5 – Agentic AI and Workflow Automation
- Introduction to AI agents;
- Tool integration and workflow orchestration;
- Practical examples of AI-assisted automation.
Learning Outcomes
After completing the course, students will be able to:
- acquire, manipulate, and transform tabular datasets using Pandas;
- perform exploratory data analysis through appropriate visualizations;
- develop machine learning workflows for classification, regression, and clustering tasks;
- evaluate machine learning models using suitable performance metrics;
- build complete data processing and analysis pipelines using Scikit-learn;
- understand the fundamental principles of Generative AI and Large Language Models;
- apply semantic search and text analytics techniques to institutional documents;
- design effective prompts for AI-assisted tasks;
- understand the principles of AI agents and workflow automation tools
Duration
- Synchronous Activities (Lectures): 27 hours
- Asynchronous Activities (Exercises): 24 hours
- Self-Learning: 24 hours
- Total Student Workload: 75 hours
Schedule:
- Monday, September 28, 2026 09:00-12:00
- Thursday, October 1, 2026 09:00-11:00
- Thursday, October 8, 2026 09:00-13:00
- Monday, October 12, 2026 09:00-12:00
- Thursday, October 15, 2026 09:00-11:00
- Monday, October 19, 2026 09:00-12:00
- Thursday, October 22, 2026 09:00-11:00
- Monday, November 2, 2026 09:00-12:00
- Thursday, November 5, 2026 09:00-11:00
- Monday, November 9, 2026 09:00-12:00
Assessment
The evaluation of the course is based on the completion of the practical assignments proposed during the course.
Participants are required to submit the assigned notebooks before the synchronous lecture in which the corresponding solutions will be discussed. Each notebook will be evaluated according to the completion of the assigned exercises and the correctness of the proposed solutions.
The course completion requirement is achieved by:
- attending at least 80% of the synchronous sessions.
- submitting all assigned notebooks;
- successfully completing at least 70% of the proposed exercises across the practical activities.
Exercises will contribute equally to the final evaluation, and partial completion of an exercise will be considered when assessing the overall completion level. The evaluation is intended to verify the participant’s ability to apply the concepts introduced during the course rather than to assess theoretical knowledge only.
How to Apply
If you are interested in this course, register by clicking on the green bar (at the top and the botton of this page during the registration period).
You will then be taken to the Boost platform to fill in your personal details and complete your pre-registration.
You must use your institutional account from your home university to book this course, If you do not have one, you must request access credentials to this course Administrative Support team.
Once you have them, you will be able to log in to Boost and complete your registration.
Finally, Administrative Support will confirm your registration and provide you with the login details for the online course through which you will be able to take this course.
Administrative Support: [email protected]