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Curricular information is subject to change
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 Type | Hours |
---|---|
Lectures | 12 |
Practical | 12 |
Autonomous Student Learning | 75 |
Total | 99 |
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
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment: Assignment 1 | Unspecified | n/a | Graded | No | 40 |
Assignment: Assignment 2 | Unspecified | n/a | Graded | No | 60 |
Resit In | Terminal Exam |
---|---|
Spring | No |
• Feedback individually to students, post-assessment
Not yet recorded.
Name | Role |
---|---|
Mr Eoghan Cunningham | Tutor |