Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Big Data Analytics and Visualisation | |||
| Reference | CMM534 | Version | 8 |
| Created | February 2023 | SCQF Level | SCQF 11 |
| Approved | April 2015 | SCQF Points | 15 |
| Amended | June 2023 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| To introduce students to the use of state-of-the-art Big Data analytics and visualisation techniques and tools, and modern parallel computation methodologies. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Critically compare and evaluate methodologies and software frameworks for big data analysis tasks. |
| 2 | Explore, visualise and evaluate big data, using a parallel computation framework. |
| 3 | Extract and interpret actionable knowledge from big data, using the parallel computation framework. |
| 4 | Compare and contrast data visualisation techniques. |
| Indicative Module Content |
|---|
| Modern parallel data processing techniques, e.g. MapReduce/Hadoop, Spark. Machine learning libraries applicable to big data e.g MLlib, Mahout. Case studies on using parallel data processing for analysis and mining of Big Data. Data visualisation techniques. |
| Module Delivery |
|---|
| This is a lecture based module, supplemented with practical sessions, where a number of Big Data technologies will be used to teach students how to store, analyse and visualise Big Data. Online materials and online sessions will be used to support DL students. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 30 | 30 |
| Non-Contact Hours | 120 | 120 |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | 150 |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Practical Exam | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | A practical assessment covering knowledge of, and practical skills in, big data technologies and visualisation techniques and principles. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The calculation of the overall grade for this module is based on 100% weighting of C1. To pass the module students should achieve grade D or better. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | Grade A in Assessment Component 1 |
| B | Grade B in Assessment Component 1 |
| C | Grade C in Assessment Component 1 |
| D | Grade D in Assessment Component 1 |
| E | Grade E in Assessment Component 1 |
| F | Grade F in Assessment Component 1 |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | GULLER, M., 2015. Big data analytics with Spark : a practitioner's guide to using Spark for large-scale data processing, machine learning, and graph analytics, and high-velocity data stream processing. Apress. |
| 2 | LESKOVEC, J., ANAND, R. and ULLMAN, J.D., 2019. Mining of massive datasets. (3rd Edition) Cambridge University Press. |
| 3 | ZIKOPOULOS, P. and EATON, C., 2011. Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media. |
| 4 | CHIVUKULA, A.S. et al, 2019. Big Data Analytics. Springer |