Module: Data Literacy & Analytics for the 21st Century

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Data Literacy & Analytics for the 21st Century
Module Code	CSC1015 (ITS: CA179)
Faculty	Computing	School	Engineering & Computing
NFQ level	8	Credit Rating	5

Description

This module equips students with the knowledge and skillset of Data Literacy and Analytics required in the 21st century. It enables students to gain the capability to collect, process, critique, analyse, visualise, and interpret data in an unbiased, responsible, actionable and ethical manner. It further prepares students with the ability to use tools and techniques to efficiently understand, interpret, and use data in their discipline. The delivery mode will be online and asynchronous. There will be no synchronous face-to-face or online lecture/lab taking place throughout the semester. All resources will be available to students on the module Loop page and students will complete the topics at their convenient time in a self-paced manner. For attaining the best out of this module, students are highly advised to engage with the content every week over the semester. There are seven online assessments corresponding to the seven topics. Students are encouraged to take the assessment immediately after they complete the content covered in that topic. Students are expected to pass all the seven topics to pass this module. The passing mark for each topic will be posted on the module loop page. Students are also allowed to try as many times until they pass the assessment during the term. However, failing to achieve the passing mark for three consecutive attempts may require them to revisit the content covered before trying again.

Learning Outcomes

1. Describe the fundamental concepts of Data Literacy and Analytics, the key steps in the analytics process, and the applications and implications of data analytics in their specialism.
2. Demonstrate knowledge of big data analytics and its steps, key statistical concepts underlying data analytics techniques, including descriptive statistics.
3. Discuss the ethical and legal requirements involved when gathering, storing, analysing and reporting data and the societal impact of data analytics.
4. Be able to source and import data and apply basic operations for cleaning, and processing this data in preparation for data analysis using basic functionalities of analytics tools such as Spreadsheet, Python, or R.
5. Identify and use basic data analysis and visualisation tools to describe and interpret data.
6. Identify the relevant insights extracted from a dataset and effectively and appropriately communicate (interpreter, critique, and present) them.
7. Differentiate between the different data types in analytics and have the ability to explain basic database design concepts, including conceptual, logical, and physical data models.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Assessment Feedback	12	Asynchronous, online lectures covering key concepts of the course. The lecture hours will introduce major concepts and direct students to relevant practical activities.
Laboratory	24	Asynchronous, online laboratory hands-on experience working on the selected tasks. Students will focus on the practical tasks given to them to gain hands-on experience.
Independent Study	89	Reading, practising and reflections
Total Workload: 125

Section Breakdown
CRN	10187	Part of Term	Semester 1
Coursework	0%	Examination Weight	0%
Grade Scale	PASS/FAIL	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Yalemisew Abgaz	Module Teacher

Section Breakdown
CRN	21121	Part of Term	Semester 2
Coursework	0%	Examination Weight	0%
Grade Scale	PASS/FAIL	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Yalemisew Abgaz	Module Teacher

Section Breakdown
CRN	12301	Part of Term	Semester 1
Coursework	0%	Examination Weight	0%
Grade Scale	PASS/FAIL	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Yalemisew Abgaz	Module Teacher

Type	Description	% of total	Assessment Date
Assessment Breakdown
Loop Exam	An online exam assessing the learner's understanding of the major concepts in data literacy and analysis.	15%	n/a
Loop Exam	An online exam assessing the learner's knowledge of big data analytics and its steps, key statistical concepts underlying data analytics techniques, including descriptive statistics.	15%	n/a
Loop Exam	An online exam that assesses the learner's awareness of the ethical and legal requirements involved when gathering, storing, analysing and reporting data and the societal impact of data analytics.	10%	n/a
Loop Exam	An online exam that assesses the learner's ability to source and import data and apply basic functions for cleaning, and processing this data in preparation for data analysis using basic functionalities of analytics tools such as Spreadsheet, Python, or R.	15%	n/a
Loop Exam	Online practical questions that assess the learner's ability to identify and use basic data analysis and visualisation tools to describe and interpret data.	15%	n/a
Loop Exam	Online practical questions that assess the learner's ability to dentify the relevant insights extracted from a dataset and effectively and appropriately communicate (interpreter, critique, and present) them.	15%	n/a
Loop Exam	Online questions that assess the learner's ability to differentiate between the different data types in analytics and have the ability to explain basic database design concepts, including conceptual, logical, and physical data models.	15%	n/a

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

Introduction to Data Literacy and Analytics
Data Literacy, Data, Data Types, Types of Data, Metadata, Data Sources, and Data Collection.

Introduction to Big Data Analytics
Big Data, Data Analytics Pipeline, Application Areas, Tools and Technologies, and Trends.

Introduction to Data Protection and Ethics
Introduction to Data Protection, Organisational and Individual Responsibilities, Ethical Data Collection, Ethical Data Analysis and Storage, and FAIR Principles.

Introduction to Spreadsheet
Introduction to Spreadsheet, Creating New Books and Sheets, Entering Data, Formatting, Sorting, Filtering, Aggregation, and Formulas.

Intermediate Spreadsheet
Importing/Exporting data, Data Cleaning, Advanced Formulas and References, Useful Built-in Functions, and Basic Regression and Correlation.

Data Visualisation and Communication Using Spreadsheet
Data Visualisation Fundamentals, Selecting Appropriate Graph Types, Data Visualisation in Spreadsheets, Data Critique, and Data Misrepresentation, Data Reporting and Presentation, Arguing with Data (Support Narrative).

Introduction to Database Modelling
Capturing and Modelling Data, Conceptual Modelling, Logical Modelling, Physical Modelling, Data Storage Technologies, Data Retrieval Languages.

Indicative Reading List

Books:

Stephen Few: 2012, Show me the Numbers, 2nd, Analytics Press, 978-097060197

Articles:
None

Other Resources

1: Additonal Resources for each topics will be provided during the module delivery.,