DATA BASE AND BIG DATA ANALYTICS

Academic Year 2020/2021 - 1° Year
Teaching Staff
  • Databases: Simone Palazzo
  • Big Data Analytics: Orazio Tomarchio
Credit Value: 12
Taught classes: 80 hours
Term / Semester: 1° and 2°

Learning Objectives

  • Databases

    The course covers the fundamental concepts of management and design of database systems.

    Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.

    The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.

  • 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

Course Structure

  • Databases

    Lectures, hands-on exercises, paper reading, student presentations and seminars.

    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.

  • Big Data Analytics

    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.


Required Prerequisites

  • Databases

    Basic programming skills

  • Big Data Analytics
    • Basic knowledge of database systems
    • Basic knowledge of SQL

Attendance of Lessons

  • Databases

    Strongly recommended. Attending and actively participating in the classroom activities will contribute positively towards the overall assessment of the oral exam.

  • Big Data Analytics

    Strongly recommended. Attending and actively participating in the classroom activities will contribute positively towards the overall assessment of the oral exam.


Detailed Course Content

  • Databases

    1) Models and Languages for Database Management

    • Fundamentals of Database Management Systems (DBMS)

    • Relational Model: basic concepts, integrity constraints and keys.

    • SQL language: data definition, data modification, queries, views, transactions.

    • NO-SQL database: MongoDB

    2) Querying and processing big data

    • Apache Spark SQL with Python

    • Dataset and Dataframes

    • Examples of data analysis with Spark SQL

  • Big Data Analytics

    1. Introduction to Business Intelligence and Big Data Analytics (6 hours)

    • 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 (10 hours)

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

    3. BI Architecture (8 hours)

    • 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 (16 hours)

    • 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

Textbook Information

  • Databases
    1. R. Elmasri and S. Navathe, "Fundamentals of Database Systems", 7th Edition, Pearson, 2016.

    2. B. Chambers, M. Zaharia, "Spark: the definitive guide", O'Reilly, 2018.

    3. Instructor’s notes

  • Big Data Analytics
    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)

Course Planning

Databases
 SubjectsText References
1Introduction to databases: Concepts and ArchitectureBook 1 - Chapter 1 and 2  
2Relational Data Model Book 1 - Chapter 5  
3Basic SQL: data definition, SQL query, update instruction set. Book 1 - Chapter 6 + Notes  
4Advanced SQL: Complex Queries, Triggers, ViewsBook 1 - Chapter 7 + Notes  
5Query processing and optimizationBook 1 - Chapter 18 and 19  
6NOSQL Databases and Big Data Storage SystemsBook 1 - Chapter 24 + Notes  
7Active, Temporal, Spatial, Multimedia, and Deductive DatabasesBook 1 - Chapter 26  
8Getting started with Spark SQL for Data ProcessingBook 2 - Chapter 1 and 2 + Notes  
9Spark SQL for Data ExplorationBook 2 - Chapter 3 + Notes  
10Spark SQL for Learning ApplicationsBook 2 - Chapter 6 and 10 + Notes  
11Multimedia benchmarks for bias identification and analysisResearch paper list on course web site  
Big Data Analytics
 SubjectsText References
1Introduction to Big Data Analytics.[Notes] 
2Business intelligence: introduction, fundamental concepts and architectures[Notes]
[GoRi] Chap. 1 
3The structure and evolution of BI and Big Data analytics systems[Notes] 
4Data models for data warehouse: conceptual modeling and design[GoRi] Chap. 2-6 
5Multi-dimensional data model[GoRi] Chap. 5 
6Data models for data warehouse: logical modeling and design[GoRi] Chap. 8-9 
7ETL (extract, transform and load) process[GoRi] Chap. 10
[Notes] 
8OLAP analysis and query[GoRi] Chap. 7
[Notes] 
9Introduction to Data Visualization. Visual Perception and Preattentive Attributes[Dash] Chap. 1
[Few2] Chap. 4 
10Charts and standard views: relevance, appropriateness and best practices[Few1] 
11Use of colors in data visualization[Dash] Chap. 1 
12Advanced and innovative tools for data visualization: the Tableau platform[Notes] 
13Dashboard design principles. Exploratory vs. Explanatory dashboards.[Few2] 
14Data visualization: infographics and storytelling[Few2] 

Learning Assessment

Learning Assessment Procedures

  • Databases

    Written exam with SQL and noSQL exercises.

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

  • Big Data Analytics

    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.


Examples of frequently asked questions and / or exercises

  • Databases
    • Implement a query using relational algebra
    • Implement a query in SQL
    • Implement a query in MongoDB
    • Define the entity-relation model for a given scenario
  • Big Data Analytics

    Examples of questions and exercises are available on the Studium platform and/or the Microsoft Teams platform