DATABASE

Università degli Studi
"G. d'Annunzio"

Chieti - Pescara

General Module

Course Sheet

Course code:

000376L

Academic Year of enrolment:

2020

Year of supply:

2021/2022

Type of Course:

Related/Supplementary

Degree:

Course of 2nd Cycle Degree in ECONOMICS AND BUSINESS ANALYTICS

Disciplinary Sector:

Computer Science

Is part of:

BIG DATA

Location:

PESCARA

Credits:

Programme Year:

Professor and Collaborators:

FIORAVANTI Fabio

Cycle:

First semester

Hours of classroom activity:

Individual study hours:

Objectives

The learning objectives can be associated with the following expected learning outcomes:
Knowledge and understanding
The course aims to provide the basic methodological and application knowledge of the main tools and methods for managing and querying big amounts of data

Applying knowledge and understanding

At the end of the course, also because of the case studies addressed during the lab classes, the student will also be able to extract and manipulate data from the web, from files and from databases, including those of big size.

Contents

- Introduction to the Big Data phenomenon
- Relational and non-relational (NoSQL) databases
- Structured Query Language (SQL)
- MongoDB
- Lab & tools

Extended Syllabus

• Introduction to databases and big data
• Relational databases. Tables, relationships and attributes. Incomplete information and null values. Integrity constraints.
• Relational algebra.
• Structured Query Language (SQL). Selection, insertion, update and deletion queries. Join. Nested queries. Aggregate operators. Indexes.
• ACID Transactions: atomicity, consistency, isolation and durability
• Non-relational databases (NoSQL). Non-relational models: key-value, Wide Column, document-based, graph-oriented, object-oriented.
• CAP (or Brewer's) theorem. Consistency, Availability, Partition Tolerance.
• MongoDB. Collections and documents. Query Language. Aggregation framework. Indexing, replication, sharding.

Recommended Bibliography

Course slides.

Further readings:
Databases Essentials. Antonio Albano.
http://fondamentidibasididati.it/
Shannon Bradshaw et al. MongoDB: The Definitive Guide: Powerful and Scalable Data Storage. O’Reilly

Teaching Methods

Lectures. Practice and exercises in the computer lab.

Evaluation methods

Verification of learning:

Knowledge and understanding
The verification of the learning outcomes will be carried out through a written and oral examination (the latter being optional or potentially required by the teacher). The score of the exam is assigned by a mark expressed in 30ths and is based on both the written and oral examinations.

Applying knowledge and understanding

During the exam, students' ability to apply the knowledge given in the course is verified. In particular, student should be able to extract and manipulate data from the web, from files and from databases, including those of big size.

Contacts/More Information

Email fabio.fioravanti@unich.it

Office hours: by appointment.

Other courses

Course year: 1

DATA SCIENCE IN ECONOMICS
9 Credits - Characterising
ECONOMICS OF DIGITAL MARKETS
9 Credits - Characterising
ECONOMICS OF GLOBAL MARKETS
9 Credits - Characterising
ECONOMY OF INNOVATION
6 Credits - Characterising
MICROECONOMICS FOR DIGITAL MARKETS
6 Credits - Characterising
STRATEGIC MANAGEMENT
9 Credits - Characterising
CYBERSECURITY
6 Credits - Related/Supplementary
IT LAW
6 Credits - Related/Supplementary
ELECTIVE COURSE
9 Credits - Elective Course

Course year: 2

BIG DATA
9 Credits
BUSINESS INFORMATION SYSTEMS
6 Credits - Characterising
DATA SCIENCE IN ECONOMICS LABORATORY
6 Credits - Characterising
DEEP NEURAL NETWORKS
6 Credits - Characterising
ECONOMIC INNOVATION LAW
6 Credits - Characterising
STATISTICAL LEARNING
6 Credits - Characterising
WEB MARKETING
6 Credits - Characterising
FINAL EXAMINATION
20 Credits - Language/Final Examination
INTERNSHIP
7 Credits - For stages and internships

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