9793926 - DATA BASE AND BIG DATA ANALYTICS
Modulo BIG DATA ANALYTICS

This module covers the fundamental concepts of management and design of a business intelligence system. Topics include data models for building a data warehouse; ETL (extract, transform and load) functionalities; OLAP analysis; basic data mining; reporting and interactive dashboards, evolution of BI architectures on large datasets. The module covers techniques and algorithms for data visualization and exploratory analysis based on principles and techniques from graphic design, perceptual psychology and cognitive science. It is targeted to using visualization in their data analytics work. The learning objectives are as follows:

Knowledge and understanding

• To understand the most important methodologies and techniques used by industries to analyse data in order to support the decision process
• To understand the main methodologies to design a data warehouse
• To understand the main methodologies to transform data into sources of knowledge through visual representation

Applying knowledge and understanding

• To be able to apply methodologies and techniques to analyse data.
• To be able to design a data warehouse.
• To be able to build report and data analysis and organize them into interactive dashboards

Making judgements

• To be able to evaluate the different alternatives and techniques when analyzing data with different characteristics.

Communication skills

• Students will be able to visually represent the result of data analysis by using the most appropriate type of chart
• Students will know and will be able to apply the basic principles of data and information visualization

Learning skills

• Students will be able to learn and use new data analysis techniques as technology evolves

• Students will be able to learn and use new tools for data analysis and visualization

The main teaching methods are as follows:

• Lectures, to provide theoretical and methodological knowledge of the subject;
• Hands-on exercises, to provide “problem solving” skills and to apply design methodology;
• Laboratories, to learn and test the usage of related tools

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

• Basic knowledge of database systems
• Basic knowledge of SQL

1. Introduction to Business Intelligence and Big Data Analytics 

• Goal and rationale of BI systems
• The value of knowledge - data driven decision making
• The structure and evolution of BI and Big Data analytics systems
• OLAP vs OLTP
• Data warehouse and Business intelligence
• Advanced tools and platforms for BI and analytics

2. Data models for data warehouse 

• Conceptual modeling
• Dimensions and facts
• Multi-dimensional data model
• Conceptual, logical and physical design

3. BI Architecture 

• ETL (extract, transform and load) functionalities
• OLAP analysis
• OLAP query
• Reporting and Interactive Dashboard
• Overview on commercial and open-source BI platforms

4. Data Visualization 

• Introduction to Visualization
• Data Visualization fundamentals: Visual Perception and Preattentive Attributes
• Charts and standard views: relevance, appropriateness and best practices
• Use of colors in data visualization
• Dashboard Design
• Advanced and innovative tools for data visualization: the Tableau platform

1. [GoRi] Golfarelli, Rizzi. Data Warehouse Design: Modern Principles and Methodologies, McGraw Hill
2. [Dash] Steve Wexler, Jeffrey Shaffer, Andy Cotgreave. The Big Book Dashboards: Visualizing Your Data Using Real-World Business Scenarios. Wiley (2017)
3. [Few1] Stephen Few. Show Me the Numbers: Designing Tables and Graphs to Enlighten, 2nd edition, Analytics Press (2012)
4. [Few2] Stephen Few. Information Dashboard Design: Displaying Data for At-a-Glance Monitoring, 2nd edition, O’Reilly Media (2013)
5. [Notes] Instructor’s notes (published on Studium and/or the Microsoft Teams platform)

The final exam consists of

1. a project work aiming at assessing the capabilities in developing a BI system including the analysis and the visualization of relevant information,
2. an oral exam that will consist of the discussion of the project work.

Assessment criteria include: depth of analysis, adequacy, quality and correctness of the proposed solutions to the project work, ability to justify and critically evaluate the adopted solutions, clarity.

The vote on the Big Data Analytics module will account for 50% of the total grade for the entire course.

Learning assessment may also be carried out on line, should the conditions require it.