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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Data Science and Databases
Module Code CA119
School School of Computing
Module Co-ordinatorSemester 1: Mark Roantree
Semester 2: Mark Roantree
Autumn: Mark Roantree
Module TeachersMark Roantree
NFQ level 8 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

This module provides an overview to data management aspects of Data Science. It provides students with an introduction to Databases. Students should learn how to design and create a database using the entity-relationship model, express queries in SQL, understand relational database theory and validation concepts such as normalisation and functional dependencies.

Learning Outcomes

1. To understand the relational model theory that underpins database design.
2. To translate an informal problem specification into a well-formed Entity-Relationship model and map this to an appropriate relational schema.
3. To demonstrate a proficiency in writing SQL expressions to query and alter the database.
4. To understand the advantages of applying normalisation theory to validate database schemas.
5. To be able to apply summarisation and cleaning techniques in database applications for data science.

Workload Full-time hours per semester
Type Hours Description
Lecture24No Description
Laboratory24No Description
Directed learning2Enf of Year Exam
Directed learning1Lab Exam
Independent Study74No Description
Total Workload: 125

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Database Environment
Overview: Architecture, Components & Functions, Data Models.

Relational Model
Schemas, constraints, violations.

Relational Algebra
Relational Algebra Operators

Programming in SQL

Data Integrity
Capturing and maintaining integrity in relational databases.

Entity-Relationship Modelling
Using the E-R model to capture system requirements and database deployment.

Functional Dependency
Understanding functional dependency theory and rules.

Applying functional dependencies to ensure normalised databases.

Data Preprocessing for Data Science
Summarisation, cleaning and transformation of data.

Assessment Breakdown
Continuous Assessment20% Examination Weight80%
Course Work Breakdown
TypeDescription% of totalAssessment Date
In Class TestSQL Lab Exam20%Week 8
Reassessment Requirement Type
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
This module is category 1
Indicative Reading List

  • Thomas Connolly & Carolyn Begg: 0, Database Systems: A Practical Approach to Design, Implementation and Management, Addison Wesley,
Other Resources

Programme or List of Programmes
APBSc in Applied Physics
DSBSc in Data Science
ECSAOStudy Abroad (Engineering & Computing)

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