BIG DATA IN CORPORATE FINANCE AND BANKING

The "BIG DATA IN CORPORATE FINANCE AND BANKING" course allows students to understand the connections between some fundamental themes of the degree course: technological topics (BIG DATA & ANAYTICS), Corporate finance and banking. In particular, through lectures, case studies, applications, we understand how we move from conceptual aspects (of product and process) to data structures and analytical processes.
The aim of the course is to learn statistical methods and operational techniques to analyze financial data, in the capital markets, corporate finance, risk management, also thanks to the use of specialized software.

The course is characterized by 3 parts: lectures, case studies led by the teacher, laboratory activities (with data Analytics software: QLIK or Google Platform).
At the end of the course the student is in possession of the following
Knowledge
• Datawarehouse (bases and modeling)
• Data Analytics: descriptive and predictive methods
• BIG DATA: definitions, applications, tools
• Advanced statistical methods: Cluster analysis Classification trees, Advanced Regression
• Data structures for finance and banking
• Concepts of ETL and data cleaning
• Scope of BIG DATA and Analytics

Operational Skills
• Design a data model for analyzes (datawarehouse)
• Know the main techniques of quantitative and qualitative data analysis
• Set up simple data ETL techniques
• Design analytics and data KPIs
• Create simple reports and data analysis dashboards
• Use a data analytics software

To attend the course it is strongly recommended to have the following knowledge and skills:
• basic concepts of data modeling (relational database)
• basic statistical methods
• good propensity to use software
• good knowledge of corporate finance
• good knowledge of the financial markets

Design of databases
• Datawarehouse: objectives, structures, data models
• The Dimensions: flat and hierarchical
• The metrics (additive and non-additive)
• Business Intelligence and data Analytics: principles and definitions
• Basic Business Intelligence: OLAP reporting and analysis
• Advanced Business Intelligence: Data Mining and What if Simulation
• Review on the software market
• BIG DATA: size, variety and speed
• Some technologies for BIG DATA
• BIG DATA: data storage
• BIG DATA: advanced and predictive analysis techniques
• Applications to corporate finance
• Banking applications
• Laboratory: organization of financial data through Relational Database
• Laboratory: Google Data Studio learning
• Laboratory: analysis dashboards

Mandatory
- Texts Lecture notes by the teacher


Optional texts and supplementary readings
- Data Analytics For Beginners - Author Victor Finch
- Performance Dashboards: Measuring, Monitoring, and Managing Your Business - Author Wayne W. Eckerson
- WIKIPEDIA
- Web sites

The written test is divided into two parts, and is aimed at measuring theoretical and applicative knowledge.
A first part is characterized by multiple choice tests, the second consists in the creation of a set of financial data analysis KPIs by creating Google Data Studio

Theoretical and practical lectures.
Applications using Google data Studio

English

written