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REFLECTIONS AND LESSON LEARNED (2026)
REFLECTIONS AND LESSON LEARNED (2026)
by M.P. (Ho Chi Minh, Vietnam), U.M. (Tashkent, Uzbekistan), G.P. and M.M (Trieste, Italy) , students of MIB TRIESTE SCHOOL OF MANAGEMENT
"This project pushed us to think about data not as static files but as a living, connected resource. The most significant challenge was ensuring consistency across six different tools and services, each with its own data format, error behaviour, and rate limits. We learned to build defensively: every stage was designed with fallbacks so that incomplete data would degrade gracefully rather than cause failures downstream.
Working with MAKE.com taught us to think visually about process flow. At the same time, the Airtable and AI integration showed us how generative models can be embedded as functional components within a data pipeline rather than used only as conversational tools. Web scraping reinforced the importance of handling real-world
inconsistency in online data sources.
Overall, this course gave us a concrete, end-to-end experience of building a data product, from input form to interactive visualisation, and left us with a working system we could extend with problems and new data sources going forward!"
Content
Content
Tools, not just slideware.
This course provides a clear and comprehensive overview of the nature and importance of Data Analytics—often called Big Data—in today’s business environment. It is designed to equip participants with practical knowledge of the essential tools needed for decision making based on data rather than intuition.
Particular emphasis is placed on the capabilities of modern No-Code/Low-Code solutions, especially in database management, workflow automation, web scraping, machine learning, and the newly available generative AI tools, which enable professionals to achieve significant improvements in their work with unprecedented speed and efficiency.
Theory: Welcome to big data world
Theory: Welcome to big data world
Introduction to the course, History and evolution of data, Privacy and GDPR, Data quality and Data cleansing
Big Data, Data Driven Society, Data Governance, EU Regulations: Data Act and AI Act, Ethics
Big Data for Technical Excellence in Insurance; Credit Scoring and Data Analytics in Finance, Data Strategy, Data driven insight, Workflow automation
HTML and CSS, Data Hunting on the web, Regular Expressions (RegEx) to extract insights from data
Scraping and scraping tools, Captcha, AI Foundations and Core Concepts, Machine Learning, Neural Networks, Natural Language Processing
AI Evolution, Transformers, Large Language Models (LLMs); Core characteristics of Generative AI, Key industry players; Techniques for mitigating hallucinations; Prompt Engineering; Agentic AI, ongoing debate regarding AI ethics
Practice: hands on the digital toolbox
Practice: hands on the digital toolbox
Google Workspace: GDrive, GForm, GSheets, GSites; Geocoding; Awesome Tables
API, Google App Script(GAS), Foundations of No-Code/Low-Code programming and its tools, JSON, Data hunting on Wikipedia
Airtable and its automation, Workflow management for structured automation of tasks, ensuring consistent data flows and orchestration across processes, MAKE.com
Scraping techniques using MAKE.com and RegEx, “Monitor Website” individual assignment
Tools for automatic scraping, Data Miner, Instant Data Scraper, Thunderbit, Browse.ai;
GPTforWORK, Mistral.ai; Gen-AI embedded within Airtable; Gen-AI integrations via Make.com
Leonardo Felician, professor of:
Data Analytics for Finance and Insurance, MIB Trieste School of Management, Trieste, Italy
Insurance Management, BBS-Bologna Business School, Bologna, Italy
Data Driven Decision Making, H-Farm College, Venezia, Italy
No Code Fundamentals, Albert School, Milan, Italy
Big Data and Generative AI: No-Code Tools for Research and Productivity, Scuola Superiore, Udine, Italy
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