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COMP41680

Academic Year 2025/2026

Data Science in Python (COMP41680)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Assoc Professor Derek Greene
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

During this module, students will explore a range of key concepts and topics in Data Science, accompanied by practical implementations using Python. This ranges from the initial stages of data collection and preparation through to data analysis and modelling. Students will become familiar with widely-used Python packages for data analysis, including Pandas, Matplotlib, and scikit-learn. Assessment for this module comprises a combination of individual project assignments and a final practical exam.

NOTE: COMP41680 requires a reasonable level of prior programming experience (but not necessarily in Python).

About this Module

Learning Outcomes:

On completion of this module, students will be able to: 1) use a range of Python packages for data science; 2) collect, pre-process, and filter datasets; 3) apply common data analysis procedures and interpret their outputs.

Indicative Module Content:

The topics covered by this module may include:
- Introduction to Python
- Working with Jupyter Notebooks
- Introduction to Data Science
- Data Loading, Storage, and File Formats
- Web Data Collection
- Data Cleaning and Preparation
- Data Manipulation and Wrangling
- Plotting and Visualisation in Python
- Introduction to Modelling and Prediction
- Classification and Evaluation
- Text Mining
- Working with Time Series Data

Student Effort Hours:
Student Effort Type Hours
Lectures

12
Practical

12
Autonomous Student Learning

80
Total

104

Approaches to Teaching and Learning:
Practical Labs; Continuous assessment

Requirements, Exclusions and Recommendations
Learning Requirements:

Prior programming experience in a high level language (but not necessarily in Python).


Module Requisites and Incompatibles
Incompatibles:
COMP30760 - Data Science in Python - DS, COMP47350 - Data Analytics (Conv), COMP47670 - Data Science in Python (MD), FIN42330 - Python for Fin. Data Science, MEEN41330 - Data Analytics for Engineers, STAT40800 - Data Prog with Python (online)


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Practical Assignment 1 Week 8 Alternative linear conversion grade scale 40% No
20
 No
Assignment(Including Essay): Practical Assignment 2 Week 12 Alternative linear conversion grade scale 40% No
20
 No
Exam (Open Book): Two hour End of Trimester practical exam End of trimester
Duration:
2 hr(s)
 Alternative linear conversion grade scale 40% No
60
 No

Carry forward of passed components
No
 

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

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Professor Pádraig Cunningham Lecturer / Co-Lecturer

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Practical Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 09:00 - 09:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 09:00 - 09:50
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