The Computer Skills Lab provides first-year Business Administration students with practical digital competencies in two complementary areas: AI literacy and advanced spreadsheet-based data analysis. The first part of the course introduces the fundamental concepts, techniques and applications of Artificial Intelligence, with a strong emphasis on the effective, critical and responsible use of generative AI tools in academic and professional contexts. The second part develops advanced Microsoft Excel skills, focusing on formulas and PivotTable-based data analysis for business-oriented tasks. The course adopts a hands-on, lab-based approach that includes directly use of AI tools and Excel on realistic exercises and case studies.

Upon successful completion of the course, students will be able to: - explain what Artificial Intelligence is and distinguish its main subfields (machine learning, deep learning, natural language processing, generative AI); - describe the historical evolution of AI and the main technological milestones that led to current tools; - understand, at an introductory level, the main families of algorithms: supervised and unsupervised learning, neural networks and language models; - identify relevant applications of AI in business, individual productivity, creativity and data analysis; - critically assess the ethical, social and regulatory implications of AI, including bias, privacy, governance, reliability of outputs and responsible use; - carry out AI-assisted web research with appropriate source validation and produce reliable syntheses; - understand the notion of agentic AI, its opportunities, limits and risks; - build advanced Excel workbooks using logical, lookup, text, date/time and dynamic array functions; - analyze datasets using PivotTables and produce clear, well-structured data summaries and reports.

The course is organized into two parts. Part I - AI Literacy: fundamental concepts of Artificial Intelligence and its main subfields (machine learning, deep learning, natural language processing, generative AI); historical evolution of AI; main techniques and families of algorithms (supervised and unsupervised learning, neural networks, language models), presented with an introductory and applied perspective; applications of AI in business, individual productivity, creativity and data analysis; ethical, social and regulatory implications of AI (bias, privacy, governance, reliability of outputs, responsible use); AI-assisted web research, source validation and synthesis; integration of AI into productivity tools (Excel, Word, PowerPoint); generation of multimedia content (images, video, audio) and multimodal analysis of visual content; introduction to agentic AI, from reactive chatbots to task-oriented systems. Part II - Advanced Excel: advanced formulas (logical, lookup and reference, text, date/time, dynamic arrays); structured tables, data cleaning and conditional formatting; PivotTables and slicers; an integrated data-analysis case combining the techniques learned.

Lecture slides, lab worksheets, tutorials and datasets provided by the instructor on the course web page constitute the main reference material and are sufficient for exam preparation.

The course adopts a lab-based teaching approach combining interactive lectures with hands-on activities. Each session includes guided exercises, carried out individually or in small groups, using AI tools and Microsoft Excel on realistic, business-oriented datasets and scenarios. Live demonstrations, class discussion of results and the analysis of real-world cases complement the practical work. Students are required to bring their own laptop to class.

Attending students: the final grade is determined by a mid-term written exam (1/3 of the final grade), covering the AI literacy part of the course, and a final written exam (2/3 of the final grade), covering all of the topics. Non-attending students: the assessment consists of a single final written exam covering the entire course program (100% of the final grade). Written exams may include multiple-choice questions and practical exercises (e.g., interpretation of AI outputs, prompt design, Excel formulas and PivotTable scenarios).

Final assignments are allocated on the basis of the student's interest in the course topics (AI literacy, generative and agentic AI, data analysis with spreadsheets), the consistency of the proposed topic with the course contents, and the student's overall academic record. Students interested in a final assignment are invited to contact the instructor with a short written proposal.