Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Big Data Analytics | |||
| Reference | CM3111 | Version | 4 |
| Created | June 2022 | SCQF Level | SCQF 9 |
| Approved | August 2017 | SCQF Points | 15 |
| Amended | July 2022 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| Provide students with the necessary technical skills and underlying knowledge that enable them to apply and evaluate different data analytics and machine learning algorithms. Enable students to understand the big data ecosystem and carry out different data analytics tasks on a large-volume datasets. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Understand, identify and contrast different components of the Big Data EcoSystem. |
| 2 | Understand, identify, apply and evaluate different machine learning algorithms. |
| 3 | Design, implement and evaluate solutions to mine data and extract knowledge. |
| 4 | Identify and apply different visualisation methods to communicate results. |
| Indicative Module Content |
|---|
| Three V’s, Apache Hadoop, MapReduce, Spark. Data Analytics: visualisation, pre-processing, text, categorical data, numerical, vision problem. Machine Learning: Classification, Regression, Decision Trees, Ensemble Learning , Kernel Methods. Results: Markdown, Reproducible Results, Interactive Documents. |
| Module Delivery |
|---|
| Key concepts are introduced and illustrated through lectures and directed reading. The understanding of students is tested and further enhanced through interactive tutorials. In the laboratories, the student will progress through a sequence of exercises to develop sufficient knowledge and skills in the subject. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 30 | N/A |
| Non-Contact Hours | 120 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | N/A |
| 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: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | This component consists of coursework assignment assessing the modules learning outcomes. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The calculation of the overall grade for this module is based on 100% weighing of C1. An overall minimum grade D is required to pass the module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student needs to achieve an A in C1. |
| B | The student needs to achieve an B in C1. |
| C | The student needs to achieve an C in C1. |
| D | The student needs to achieve an D in C1. |
| E | The student needs to achieve an E in C1. |
| F | The student needs to achieve an F in C1. |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | None - knowledge of programming and basics of database would however, be beneficial. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | BRETT, L., 2015. Machine Learning with R. Packt Publishing |
| 2 | Aurelien Geron 2019, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems |