Data Analytics for Finance and Insurance
Exclusively reserved for MIB Trieste Students
17, 24 FEB - 2, 9, 17, 23 MAR
DURATION: 24 hours, 6 lessons of 3 hours, each followed by 1 hour exercise practice
LECTURERS: Leonardo Felician, member of the Core Faculty of MIB Trieste School of Management
This course aims to provide a general understanding of the nature and relevance of Data Analytics in modern business management. In addition, the course will provide participants with practical applications and several hands-on experience of different No-Code tools in use to manage data.
Main skills learnt and practiced in the course:
Course contents
Lesson 1
Theory: Importance of Data Analytics and Background
Introduction to the course and content, pillars to become a data-driven enterprise, history and evolution of data and storage techniques, data normalization, data cleansing, importance of data analytics, and main features.
Practice: Google Workspace: GDrive, GForm, GSheets, GSites; Geocoding; Awesome Tables
Lesson 2
Theory: Privacy and GDPR; New EU Regulations: Data Act and AI Act; Data democratization; Data governance; Debate on social use of data and potential risks and ethical issues; Big Data and the development of effective data strategies; SWOT Analysis; Key Performance Indicators
Practice: Foundations of No-Code/Low-Code programming and its tools; Airtable and built-in automation functions; connections between Airtable and GSheet
Lesson 3
Theory: From measurements to predictions, understanding the challenges, Technical Excellence in Insurance; Data Analytics in Finance
Practice: Workflow management for structured automation of tasks, ensuring consistent data flows and orchestration across processes; Make.com, the most versatile no-code platform for workflow automation
Lesson 4
Theory: Conventional and Unconventional Big Data Sources; Geospatial Information Systems and Satellite Imagery; Discovering New Data Lakes; Scraping definition, tools, and techniques; Captcha protection
Practice: HTML, the language of the web; Regular Expressions (RegEx), the most powerful data «hunting» tool; Scraping techniques to capture data from webpages; “Website Monitor” individual assignment
Lesson 5
Theory: Introduction to Artificial Intelligence: origin and history; Machine Learning & Neural Networks; Natural Language Processing and Voice Generation; AI for Vision; Teachable Machines
Practice: Extracting data from Wikimedia in GSheet using formulas; JSON and JavaScript integration with other information sources and visualization
Lesson 6
Theory: AI Evolution and Large Language Models; Generative AI characteristics and major players; Prompt Engineering; Data Science and Data Scientists; AI ethical issues
Practice: GPTforWORK to integrate generative AI in GSheet; OpenAI capabilities with Make; Introduction of the final assignment for the exam
After each session, 1 extra hour of practice and exercise is provided to better master the tools introduced during the lesson.
17, 24 FEB - 2, 9, 17, 23 MAR
DURATION: 24 hours, 6 lessons of 3 hours, each followed by 1 hour exercise practice
LECTURERS: Leonardo Felician, member of the Core Faculty of MIB Trieste School of Management
This course aims to provide a general understanding of the nature and relevance of Data Analytics in modern business management. In addition, the course will provide participants with practical applications and several hands-on experience of different No-Code tools in use to manage data.
Main skills learnt and practiced in the course:
- how to search for valuable data sources on the web
- how to download, upload and clean data to use it profitably
- how to organize data in easy-to-use personal database management systems
- how to get impressive geographic maps to display suitable data
- how to set up workflow management and integrated automation between different apps
- how to use Machine Learning and Generative-AI within a spreadsheet
Course contents
Lesson 1
Theory: Importance of Data Analytics and Background
Introduction to the course and content, pillars to become a data-driven enterprise, history and evolution of data and storage techniques, data normalization, data cleansing, importance of data analytics, and main features.
Practice: Google Workspace: GDrive, GForm, GSheets, GSites; Geocoding; Awesome Tables
Lesson 2
Theory: Privacy and GDPR; New EU Regulations: Data Act and AI Act; Data democratization; Data governance; Debate on social use of data and potential risks and ethical issues; Big Data and the development of effective data strategies; SWOT Analysis; Key Performance Indicators
Practice: Foundations of No-Code/Low-Code programming and its tools; Airtable and built-in automation functions; connections between Airtable and GSheet
Lesson 3
Theory: From measurements to predictions, understanding the challenges, Technical Excellence in Insurance; Data Analytics in Finance
Practice: Workflow management for structured automation of tasks, ensuring consistent data flows and orchestration across processes; Make.com, the most versatile no-code platform for workflow automation
Lesson 4
Theory: Conventional and Unconventional Big Data Sources; Geospatial Information Systems and Satellite Imagery; Discovering New Data Lakes; Scraping definition, tools, and techniques; Captcha protection
Practice: HTML, the language of the web; Regular Expressions (RegEx), the most powerful data «hunting» tool; Scraping techniques to capture data from webpages; “Website Monitor” individual assignment
Lesson 5
Theory: Introduction to Artificial Intelligence: origin and history; Machine Learning & Neural Networks; Natural Language Processing and Voice Generation; AI for Vision; Teachable Machines
Practice: Extracting data from Wikimedia in GSheet using formulas; JSON and JavaScript integration with other information sources and visualization
Lesson 6
Theory: AI Evolution and Large Language Models; Generative AI characteristics and major players; Prompt Engineering; Data Science and Data Scientists; AI ethical issues
Practice: GPTforWORK to integrate generative AI in GSheet; OpenAI capabilities with Make; Introduction of the final assignment for the exam
After each session, 1 extra hour of practice and exercise is provided to better master the tools introduced during the lesson.
