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COMP41680

Academic Year 2024/2025

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.

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. The evaluation for this module involves a combination of individual project assignments and a final practical class test.

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) 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
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, 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
25
 No
Assignment(Including Essay): Practical Assignment 2 Week 12 Alternative linear conversion grade scale 40% No
25
 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
50
 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
Dr Timilehin Aderinola Lecturer / Co-Lecturer
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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