COMP47670 Data Science in Python (MD)

Academic Year 2023/2024

The key objectives of this module are
1) to provide students with an initial crash course in Python programming;
2) 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.

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Curricular information is subject to change

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.

Student Effort Hours: 
Student Effort Type Hours
Autonomous Student Learning








Approaches to Teaching and Learning:
Learning by doing. 
Requirements, Exclusions and Recommendations
Learning Requirements:

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

Module Requisites and Incompatibles
COMP30760 - Data Science in Python - DS, COMP41680 - Data Science in Python, 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 Open Book Exam Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment: Practical Assignment 1 Unspecified n/a Alternative linear conversion grade scale 40% No


Assignment: Practical Assignment 2 Unspecified n/a Alternative linear conversion grade scale 40% No


Examination: 2 hour End of Trimester practical exam. Scheduled in Exam Period. 2 hour End of Trimester Exam Yes Alternative linear conversion grade scale 40% No



Carry forward of passed components
Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
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
Laura Dunne Tutor