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COMP10320

Academic Year 2025/2026

Intro to Data Science and AI (COMP10320)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
1 (Introductory)
Credits:
5
Module Coordinator:
Assoc Professor David Coyle
Trimester:
Spring
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module introduces students to the world of data science, an important sub-discipline of computer science, which focuses on learning about the world by analysing data.

This module will give students an introduction to the Python Data Science Ecosystem. Students will learn the fundamentals of Python programming and important Python tools and technologies for data science, including Pandas for data manipulation and analysis and Matplotlib for data visualisation.

Students will gain valuable practical experience in many important aspects of data science including data collection and cleaning, data exploration and analysis, visualisation and prediction.

The module will also provide an overview of important trends in and current topics in Data Science and AI, for example:
- Social Network Analysis
- Generative AI and Large Language Models
- Machine Learning & Health
- Bias & Fairness in Machine Learning
- Sport Analytics

Note: If students are permitted to use generative AI tools in assignments, that will be indicated in the assignment specification.

About this Module

Learning Outcomes:

This module will enable you to:

1. Apply practical data analytics using Python notebooks.
2. Manipulate datasets in Pandas data-frames.
3. Produce data visualisations using Python libraries.
4. Better understand the role of Data Science and AI in wider society.
5. Recognise and reflect on the power and limitations of Data Science and AI.

Note: If students are permitted to use generative AI tools in assignments, that will be indicated in the assignment specification.

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Practical

24
Autonomous Student Learning

52
Total

100

Approaches to Teaching and Learning:
This module will combine face-to-face lectures, code demonstrations, and hands-on practical sessions.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): Quiz 1: Python fundamentals Week 5 Alternative linear conversion grade scale 40% No
30
 No
Exam (In-person): Quiz 2 - Data Analysis and AI Week 12 Alternative linear conversion grade scale 40% No
70
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Autumn No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Saugat Aryal Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 10:00 - 10:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 12:00 - 12:50
Spring Practical Offering 1 Week(s) - 21, 22, 23, 24, 25, 26, 29, 31, 32, 33 Fri 10:00 - 10:50
Spring Practical Offering 2 Week(s) - 21, 22, 23, 24, 25, 26, 29, 31, 32, 33 Fri 11:00 - 11:50
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