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COMP30760

Academic Year 2026/2027

Data Science in Python - DS (COMP30760)

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

Curricular information is subject to change.

During this undergraduate 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: Some prior experience with Python is recommended, though a brief refresher will be provided in Week 1.

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:
- Python Essentials
- Introduction to Data Science
- Data Storage, and File Formats
- Web Data Collection
- Data Cleaning and Preparation
- Plotting and Visualisation in Python
- Working with Time Series Data
- Introduction to Modelling and Prediction
- Classification and Evaluation

UNESCO highlights a set of key competencies that support individuals in their development and support society in achieving the UN Sustainable Development Goals (SDGs). UCD has adapted these competencies and is combining them with others to form a general framework of learning competencies. This module will help you develop the competencies below to the levels specified. 
Learning Competency Additional Information Level

AI Literacy & Agency

 The ability to understand, critically evaluate and responsibly engage with artificial intelligence; to recognise how AI systems are shaped by human values; to assess their ethical, social and environmental implications; and to exercise human judgement, agency and accountability in AI-related contexts. Competent

The United Nations identified seventeen Sustainable Development Goals (SDGs) as core to the 2030 Agenda for Sustainable Development, and UCD contributes in general to SDG 4 Quality Education. Further SDGs explored within this module if relevant are listed below. A scale of 1 - 5 indicates the extent to which the SDG is covered.


Goal Name Description Coverage

Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all 1
 

Student Effort Hours:
Student Effort Type Hours
Lectures

12
Practical

12
Autonomous Student Learning

80
Total

104

Approaches to Teaching and Learning:
Teaching and learning approaches: Practical labs; continuous assessment in the form of individual project assignments.

If students are expected to use generative AI tools in assignments, this will be indicated in the assignment specification.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Incompatibles:
COMP41680 - Data Science in Python, COMP47670 - Data Science in Python (MD), 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): 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
Spring 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.

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