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) pipeline; Data Lake and ELT pipeline; 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. It is targeted at 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 to support the decision process
• To understand the main methodologies for designing 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 reports and data analysis and organize them into interactive dashboards

Making judgements

• 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.

• Basic knowledge of database systems
• Basic knowledge of SQL
• Basic knowledge of Python

. Introduction to Business Intelligence and Big Data Analytics 

• Goal and rationale of BI systems
• The value of data-driven decision making
• The structure and evolution of BI and Big Data analytics systems
• OLAP vs OLTP
• Data warehouse and Business intelligence
• Data Lake and ELT pipeline.
• 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

4. Data Visualization 

• Introduction to Visualization
• Data Visualization fundamentals;
• Charts and standard views: relevance, appropriateness, and best practices
• Dashboard Design
• Advanced and innovative tools for data visualization: the Tableau 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.