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COMP47670

Academic Year 2026/2027

Data Science in Python (MD) (COMP47670)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Wanling Cai
Trimester:
Autumn and Spring (separate)
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

The key objectives of this module is to familiarise students with a range of key topics in the emerging field of Data Science through the medium of Python.

Students will start by exploring methods for collecting, storing, filtering, and analysing datasets. From there, the module will introduce core concepts from numerical computing, statistics, and machine learning, and demonstrate how these can be applied in practice using popular open source packages and tools. Additional topics that will be covered include data visualisation and working with textual data. This module has a strong practical programming focus and students will be expected to complete two detailed coursework assignments, each involving implementing a Python solution to a data analytics task. COMP47670 requires a reasonable level of mathematical ability, and students should have prior programming experience (but not necessarily in Python).

This is a Mixed Delivery module with online lectures and face to face practicals/tutorials.

About this Module

Learning Outcomes:

On completion of this module, students will be able to:
1) Program competently using Python and be familiar with a range of Python packages for data science;
2) Collect, pre-process and filter datasets;
3) Apply and evaluate machine learning algorithms in Python;
4) Visualise and interpret the results of data analysis procedures.

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
- Introduction to Modelling and Prediction
- Classification and Evaluation
- Text Mining
- Working with Time Series Data

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. Proficient

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.


 

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

80
Practical

4
Online Learning

24
Total

108

Approaches to Teaching and Learning:

Teaching and learning approaches: Learning by doing - practical labs; continuous assessment in the form of individual project assignments.

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

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, COMP41680 - Data Science in Python, COMP41980 - Data Analytics and AI (Conv), COMP47350 - Data Analytics (Conv), STAT40800 - Data Prog with Python (online)

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


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade Component repeat (in-module) 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. Scheduled in Exam Period. End of trimester
Duration:
2 hr(s)
 Alternative linear conversion grade scale 40% No
60
 No

As part of UCD's student support, under the Additional Consideration policy, extensions may be available for the following assessments in the module: Assignment (including essay/poster), Portfolio, Reflective Assignment, Report(s), and Individual Project.


Carry forward of passed components
No
 

Remediation Type Remediation Timing
Repeat Within Two Trimesters
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
Priscilla Adong Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Practical Offering 1 Week(s) - 1, 4, 7, 10 Thurs 12:00 - 12:50
Spring Practical Offering 1 Week(s) - 20, 23, 26, 29 Thurs 12:00 - 12:50
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