COMP41680 Data Science in Python

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 individual coursework assignments, each involving implementing a Python solution to a data analytics task.

NOTE: COMP41680 requires a reasonable level of prior programming experience (but not necessarily in Python).

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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
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), STAT40800 - Data Prog with Python (online)


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Assignment 1 Unspecified n/a Alternative linear conversion grade scale 40% No

30
Assignment: Assignment 2 Unspecified n/a Alternative linear conversion grade scale 40% No

45
Class Test: Practical Test Week 12 n/a Alternative linear conversion grade scale 40% No

25

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
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
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 09:00 - 09:50
Spring