Foundational Computer Science (988G5)
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Foundational Computer Science (for Data Science)
Module 988G5
Module details for 2024/25.
15 credits
FHEQ Level 7 (Masters)
Module Outline
Apprentices will develop their applied Python programming skills, work with a variety of data sets including large data sets from real world applications, and investigate the impact on run-time of algorithmic choices.
Indicative Content
• Data structures and algorithms including algorithmic complexity
• System architectures including distributed computing, parallel computing, and cloud computing.
• Internet networking technologies.
• Basic principles of computer security technologies.
Module learning outcomes
Deploy originality in the application of knowledge of standard data structures to the formulation and decomposition of big data.
Comprehensively understand the fundamental issues and challenges of developing parallel distributed algorithms for big data.
Evaluate choice of computing model and data representation based on estimation and measurement of impact on space and time complexity and communication performance.
Systematically apply appropriate methods to store and retrieve structured big data.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 100.00% | |
| Coursework components. Weighted as shown below. | ||
| Test | T2 Week 4 (1 hour) | 20.00% |
| Report | T2 Week 11 | 80.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
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