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COMP30760

Academic Year 2025/2026

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 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.

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

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
Learning Recommendations:

Students should have a good level of prior programming experience, ideally including Python.


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.

Name Role
Suchana Datta Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Mon 09:00 - 09:50
Autumn Laboratory Offering 1 Week(s) - 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Mon 14:00 - 14:50
Autumn Exam Offering 1 Week(s) - 15 Wed 09:00 - 11:50
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