Revision as of 13:20, 14 February 2022

Advanced Databases

Course name: Advanced Databases
Course number: XYZ
Knowledge area: Data Science

Course characteristics

Administrative details

Faculty: Computer Science and Engineering
Year of instruction: 4th year of BS
Semester of instruction: 2nd semester
No. of Credits: 4 ECTS
Total workload on average: 144 hours overall
Class lecture hours: 2 per week
Class tutorial hours: 0 per week
Lab hours: 4 per week
Individual lab hours: 0
Frequency: weekly throughout the semester
Grading mode: letters: A, B, C, D

Prerequisites

Data Modelling and Databases I
Data Modelling and Databases II
Data Structures and Algorithms I
Data Structures and Algorithms II
Discrete Math and Logic
Introduction to Programming I
Introduction to Programming II

Course outline

Data Modelling and Databases I and II mostly focus on the relational model, its design and the implementation details of RDBMS. This course is focused on alternative paradigms, generally defined under the hat of NoSQL, and parallel and distributed architectures with all the theoretical and practical consideration of the case.

Expected learning outcomes

Devise appropriate ways to store and index data
Use persistency tools in the context of modern software architectures and the Cloud
Understanding of NoSQL and CAP theorem
Fluency with Graph DB and relatd query languages
Understanding parallel and distributed databases

Expected acquired core competences

Software Design
Software Engineering
Software Construction
Relational Databases
Data Modeling
Database Design
Database Systems
Query Languages: Implementation and Optimization
Implementation of Database Systems
Indexing
Information Storage and Retrieval
NoSQL
Document-based databases
Graph databases
Non-relational query languages
Theory of distributed systems
Algorithms for distributed systems
CAP theorem
Parallel and distributed databases
MapReduce

Textbook

Reference material

Lecturing and lab slides and material will be provided
Several resources are available online and will be pointed during the course

Required computer resources

Students should have laptops.

Evaluation

Assignments and project (30%)
Mid-term Exam (30 %)
Written Final (40%)

@@ Line 5: / Line 5: @@
 * <span>'''Knowledge area:'''</span> Data Science
+== Course characteristics ==
-== Administrative details ==
+=== Administrative details ===
 * <span>'''Faculty:'''</span> Computer Science and Engineering
@@ Line 19: / Line 21: @@
 * <span>'''Grading mode:'''</span> letters: A, B, C, D
-== Prerequisites ==
+=== Prerequisites ===
 * Data Modelling and Databases I
@@ Line 29: / Line 31: @@
 * Introduction to Programming II
-== Course outline ==
+=== Course outline ===
 Data Modelling and Databases I and II mostly focus on the relational model, its design and the implementation details of RDBMS. This course is focused on alternative paradigms, generally defined under the hat of NoSQL, and parallel and distributed architectures with all the theoretical and practical consideration of the case.
-== Expected learning outcomes ==
+=== Expected learning outcomes ===
 * Devise appropriate ways to store and index data
@@ Line 41: / Line 43: @@
 * Understanding parallel and distributed databases
-== Expected acquired core competences ==
+=== Expected acquired core competences ===
 * Software Design
@@ Line 64: / Line 66: @@
 * MapReduce
-== Textbook ==
+=== Textbook ===
 *
 *
-== Reference material ==
+=== Reference material ===
 * Lecturing and lab slides and material will be provided
 * Several resources are available online and will be pointed during the course
-== Required computer resources ==
+=== Required computer resources ===
 Students should have laptops.
-== Evaluation ==
+=== Evaluation ===
 * Assignments and project (30%)

Difference between revisions of "BSc: Advanced Databases"