Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Artificial Intelligence and Big Data Analytics | |||
| Reference | CM4705 | Version | 4 |
| Created | January 2023 | SCQF Level | SCQF 10 |
| Approved | June 2017 | SCQF Points | 30 |
| Amended | June 2023 | ECTS Points | 15 |
| Aims of Module | |||
|---|---|---|---|
| To introduce students to the state-of-the-art Artificial Intelligence and Big Data analytics techniques and tools, including intelligent systems technologies, machine learning, NoSQL storage and modern parallel computation methodologies for application in a business environment. Artificial Intelligence refers to the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Big Data Analytics are about analysing large amounts of data and extracting useful information, particularly as it relates to business and business analysis. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Explain the lifecycle, appropriate methods and tools for a data analytics project. |
| 2 | Pre-process, analyse and visualise data sets from different domains. |
| 3 | Evaluate and apply different machine learning algorithms for predictive analytics problems. |
| 4 | Appraise a variety of methods and technologies developed for artificial intelligence and Big Data that can be applied to real-world problems. |
| 5 | Design, implement and evaluate an application which makes significant use of artificial intelligence techniques taking in account relevant ethical issues. |
| Indicative Module Content |
|---|
| Big data Analytics introduction: lifecycle, methods and tools. Machine Learning Techniques. Visualisation techniques. Privacy and ethical issues related to Big Data Analytics in business. Artificial Intelligence (AI) and related technologies used to construct intelligent systems for use in business. Introduction to intelligent systems and the appropriate enabling technologies. |
| Module Delivery |
|---|
| This module uses the following delivery modes: Guided study (lectures, tutorials, and other learning materials delivered through VLE + bibliography), mentored practical work undertaken in the workplace, project work in the workplace including a design brief, personal study, group reflective sessions via VLE and at RGU Key concepts are introduced and illustrated through lectures (physical and virtual). Theory is put into practice in the workplace guided by a mentor. The understanding of students is tested and further enhanced through virtual interactive labs and tutorials. |
| 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, 5 |
| Description: | Written report. | ||||
| 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 | Successful completion of Stage 3 or equivalent. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | EMC Education Services, 2015, “Data Science and Big Data Analytics: Discovering, Analysing, Visualizing and Presenting Data |
| 2 | LESKOVEC, J., ANAND, R. and ULMAN, J. D., (2015), Mining of massive datasets, Cambridge University Press |
| 3 | CHANG, W., (2012), R Graphics Cookbook Practical Recipes for Visualizing Data, O’Reilly Media |
| 4 | LUCCI, S., KOPEC, D., 2015, Artificial Intelligence in the 21st Century, Mercury Learning and Information, Second Edition |
| 5 | PROVOST, F. and FAWCETT, T., 2013. Data science and its relationship to big data and datadriven decision making. Big Data, 1(1), pp.51-59. |
| 6 | MARZ, N. and WARREN, J., 2015. Big Data: Principles and best practices of scalable real-time data systems. Manning Publications Co. |
| 7 | WU, X., ZHU, X., WU, G.Q. and DING, W., 2014. Data mining with big data. ieee transactions on knowledge and data engineering, 26(1), pp.97-107. |
| 8 | MAYER-SCHÖNBERGER, V. and CUKIER, K., 2013. Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt. |