Module: Data Analysis and Machine Learning I

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Data Analysis and Machine Learning I
Module Code	EEN1083 (ITS: EE474)
Faculty	Electronic Engineering	School	Engineering & Computing
NFQ level	9	Credit Rating	7.5

Description

This module will provide students with fundamental and advanced skills required for data analysis and machine learning. The module will cover topics related to data management, data summarisation, data pre-processing, visualisation and predictive analytics. Students will gain hands-on experience of dealing with different types of data sets and how to process these datasets. The Python programming language will be used to learn and perform data analysis and machine learning tasks. Students will use a wide variety of libraries and tools for data analysis.

Learning Outcomes

1. Describe several widely used methods for data storage, including various data formats. Understand a variety of data managements tools.
2. Apply data pre-processing tasks including data cleansing.
3. Explore datasets and generate summary statistics for a variety of datasets. Understand the benefits of summary statistics and analyse datasets.
4. Visualise characteristics of a dataset through various graph and apply advance data visualisation techniques.
5. Describe supervised machine learning. Understand various algorithms of supervised machine learning and apply machine learning tasks on different datasets.
6. Describe unsupervised machine learning. Understand various algorithms of unsupervised machine learning and apply machine learning tasks on different datasets.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	36	Classroom Lectures
Independent Study	24	Regular Homeworks
Assignment Completion	36	Assignment Work
Independent Study	92	Self-directed study of materials and study for the final exam.
Total Workload: 188

Section Breakdown
CRN	11926	Part of Term	Semester 1
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Muhammad Intizar Ali	Module Teacher

Section Breakdown
CRN	11953	Part of Term	Semester 1
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator		Module Teacher

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	Perform analysis of given set of datasets, generate summary statistics, visualisation and application of supervised and unsupervised machine learning tasks.	25%	n/a
Formal Examination	A written final exam at the end of the semester.	75%	End-of-Semester

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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

Indicative Reading List

Books:
None

Articles:
None

Other Resources

None