Module: Data Science & Databases

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Data Science & Databases
Module Code	CA119 (ITS) / CSC1005 (Banner)
Faculty	Engineering & Computing	School	Computing
Module Co-ordinator	Mark Roantree
Module Teachers	-
NFQ level	6	Credit Rating	5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

None

Description

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.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	24	No Description
Laboratory	24	No Description
Directed learning	2	Enf of Year Exam
Directed learning	1	Lab Exam
Independent Study	74	No 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

SQL
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.

Normalisation
Applying functional dependencies to ensure normalised databases.

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

Assessment Breakdown
Continuous Assessment	20%	Examination Weight	80%

Type	Description	% of total	Assessment Date
Course Work Breakdown
In Class Test	SQL Lab Exam	20%	Week 8

Reassessment Requirement Type
Resit arrangements are explained by the following categories: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
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

None