COMP30760 Data Science in Python - DS

Academic Year 2024/2025

During the rest of the module, students will explore various 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, NumPy, and scikit-learn. The evaluation for this module involves a combination of individual project assignments and a final practical class test.

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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; 2) Use a range of Python packages for data science; 3) Collect, pre-process and filter datasets; 4) 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
- Numerical Computing
- Working with Time Series Data
- Plotting and Visualisation in Python
- 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:
Practical Labs; Continuous assessment; Group Work 
Requirements, Exclusions and Recommendations
Learning Requirements:

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

Learning Recommendations:

Students should have a reasonable level of prior programming experience, but not necessarily in Python


Module Requisites and Incompatibles
Pre-requisite:
COMP20250 - Introduction to Java, COMP20280 - Data Structures, COMP20290 - Algorithms, COMP20350 - Object-Oriented Programming

Incompatibles:
COMP41680 - Data Science in Python, 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
Exam (Open Book): Two hour End of Trimester practical exam n/a Alternative linear conversion grade scale 40% No

50
Assignment(Including Essay): Practical Assignment 1 n/a Alternative linear conversion grade scale 40% No

25
Assignment(Including Essay): Practical Assignment 2 n/a Alternative linear conversion grade scale 40% No

25

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
Mr Eoghan Cunningham Tutor