Module Database Search
MODULE DESCRIPTOR | |||
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Module Title | |||
Big Data Systems | |||
Reference | CM3708 | Version | 2 |
Created | January 2023 | SCQF Level | SCQF 9 |
Approved | May 2019 | SCQF Points | 30 |
Amended | June 2023 | ECTS Points | 15 |
Aims of Module | |||
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To introduce students to the use of state-of-the-art Big Data analytics techniques and tools, including NoSQL data stores, and modern parallel computation methodologies. |
Learning Outcomes for Module | |
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On completion of this module, students are expected to be able to: | |
1 | Compare and contrast different types of NoSQL data stores. |
2 | Critically analyse the suitability of a NoSQL data store for a given problem. |
3 | Extract actionable knowledge from big data, using the parallel computation framework. |
4 | Design, implement and evaluate scalable program solutions using a big data computation framework. |
Indicative Module Content |
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NoSQL data stores (e.g., key-value, document, and graph). Case studies of NoSQL data stores. Properties of NoSQL data stores. Schema migration in NoSQL data stores. Modern parallel data processing techniques, e.g. MapReduce/Hadoop, Spark. Case studies on using parallel data processing for analysis and mining of Big Data. |
Module Delivery |
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The module is delivered in Blended Learning mode using structured online learning materials/activities and directed study, facilitated by regular online tutor support. Workplace Mentor support and work-based learning activities will allow students to contextualise this learning to their own workplace. Face-to-face engagement occurs through annual induction sessions, employer work-site visits, and modular on-campus workshops. |
Indicative Student Workload | Full Time | Part Time |
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Contact Hours | 30 | N/A |
Non-Contact Hours | 30 | N/A |
Placement/Work-Based Learning Experience [Notional] Hours | 240 | N/A |
TOTAL | 300 | N/A |
Actual Placement hours for professional, statutory or regulatory body | 240 |   |
ASSESSMENT PLAN | |||||
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If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
Component 1 | |||||
Type: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
Description: | This coursework will consist of a big data development exercise and analysis. |
MODULE PERFORMANCE DESCRIPTOR | |
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Explanatory Text | |
The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade D is required to pass the module. | |
Module Grade | Minimum Requirements to achieve Module Grade: |
A | The student must achieve an A in C1. |
B | The student must achieve a B in C1. |
C | The student must achieve a C in C1. |
D | The student must achieve a D in C1. |
E | The student must achieve an E in C1. |
F | The student must achieve an F in C1. |
NS | Non-submission of work by published deadline or non-attendance for examination |
Module Requirements | |
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Prerequisites for Module | None, in addition to course entry requirements. |
Corequisites for module | None. |
Precluded Modules | None. |
INDICATIVE BIBLIOGRAPHY | |
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1 | HARRISON, G., 2015. Next Generation Databases: NoSQL, NewSQL, and Big Data. Apress. |
2 | LESKOVEC, J., ANAND, R. and ULLMAN, J.D., 2015. Mining of massive datasets. Cambridge University Press. |
3 | BERMAN, J., 2018. Principles and practice of big data: preparing, sharing, and analyzing complex information. 2nd ed. London: Academic Press. |
4 | MISHRA, R., 2018. PySpark recipes: a problem-solution approach with PySpark2. United States: Apress. |
5 | WIKTORSKI, T., 2019. Data-intensive systems principles and fundamentals using Hadoop and Spark. Cham: Springer. |