Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| 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 | |||
|---|---|---|---|
| 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 | |
|---|---|
| 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 |
|---|
| 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 |
|---|
| 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 |
|---|---|---|
| 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 | |||||
|---|---|---|---|---|---|
| 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 | |
|---|---|
| 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 | |
|---|---|
| Prerequisites for Module | None, in addition to course entry requirements. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 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. |