COMP30760 Data Science in Python - DS

Academic Year 2020/2021

This undergraduate module is aimed at students with some previous programming experience, but not necessarily in Python. The start of the module will provide a "crash course" in the Python language. The remainder of the module will cover a range of Data Science topics taught through Python, ranging from data collection and preparation, through to data analysis and visualisation. Assessment for the module will be based on indvidual project assignments which will require programming. COMP30760 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; 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 IPython Notebooks
- Introduction to Data Science
- Data Loading, Storage, and File Formats
- Online 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

60
Total

84
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
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
Assignment: Assignment 1 Unspecified n/a Graded No

50
Assignment: Assignment 2 Unspecified n/a Graded No

50

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