Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Data Science Trends and Applications | |||
| Reference | CM4708 | Version | 1 |
| Created | February 2019 | SCQF Level | SCQF 10 |
| Approved | May 2019 | SCQF Points | 30 |
| Amended | ECTS Points | 15 | |
| Aims of Module | |||
|---|---|---|---|
| To enable students to keep abreast of the latest trends in techniques and applications of data science at the forefront of technology. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Analyse and critically evaluate the main challenges to data science posed by real-world applications. |
| 2 | Critically evaluate the latest developments in data science (techniques, platforms, software etc.). |
| 3 | Apply some of the latest data science techniques to a real-world application and analyse the results. |
| 4 | Compare and contrast state-of-the-art data science techniques and methodologies. |
| Indicative Module Content |
|---|
| State of the art research in data science. Research projects and case studies. Trends and applications, including mining of social media, virtual assistants, sentiment analysis, mining real-time data, intrusion detection systems, and recommender systems. |
| 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: | The coursework will consist of a report on a chosen state-of-the-art data science technique and application. | ||||
| 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 needs to achieve an A in C1. |
| B | The student needs to achieve a B in C1. |
| C | The student needs to achieve a C in C1. |
| D | The student needs to achieve a 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, in addition to course entry requirements. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | Data Mining and Knowledge Discovery. Springer Series. |
| 2 | SIGKDD Explorations. |
| 3 | ACM Transactions on Knowledge Discovery from Data. |
| 4 | Data Science Journal. Committee on Data for Science and Technology (CODATA), International Council for Science (ICSU). |