DAI404 Big Data Management (J/651/0602) Assignment Brief 2026
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DAI404 Assignment Brief
| Qualification | Level 4 Diploma in Artificial Intelligence (610/3934/2) |
|---|---|
| Unit Code | DAI404 |
| Unit Title | Big Data Management |
| Unit Reference | J/651/0602 |
| Credits | 20 |
| TQT | 200 |
| GLH | 120 |
Assignment Aim
This unit introduces students to the key concepts and characteristics of big data technologies, data mining, visualization, real-world applications, and ethical implications in the field of big data analytics. Students will explore big data and its crucial role in today’s data-driven landscape and the structured to provide understanding and practical expertise in managing and analyzing extensive datasets. Students will develop an understanding of big data fundamentals and gain hands-on experience of using cutting-edge technologies. The unit aims to equip students with the necessary skills and knowledge to excel in the rapidly evolving field of big data analytics. Students will apply their knowledge and skills to a comprehensive dataset and present their data analysis.
Learning Outcomes and Assignment Criteria
| Learning Outcomes
When awarded credit for this unit, a learner will: |
Assessment Criteria
Assessment of this learning outcome will require a learner to demonstrate that they can: |
| 1. Understand what constitutes big data and its importance.
|
1.1 Explain the Big Data concepts and characteristics, and basic understanding of. |
| 1.2 Describe the basic aspects of Hadoop and
NoSQL databases |
|
| 1.3 Assess the evolution of Big Data technologies and their impact on modern businesses. | |
| 2. Be able to use technologies and tools in big data analytics | 2.1 Select and use appropriate technologies and tools to carry out practical coding or a process a dataset. |
| 2.2 Interpret a given dataset using basic data mining techniques and present findings. | |
| 3. Be able to apply techniques for mining, processing, and analyzing large datasets.
|
3.1 Describe the algorithms for classification, clustering, and association rule mining in processing datasets. |
| 3.2 Use tools to summarize data visualizationbased on a given dataset. | |
| 3.3 Interpret results from data visualization for specific industry to highlight business insights. | |
| 4. Be able to review applications of big data in different key industries.
|
4.1 Compare the ethical implications and privacy concerns in Big Data in different industries |
| 4.2 Carry out data analysis on a comprehensive dataset including ethical considerations | |
| 4.3 Present the results of the data to highlight business insights, predictions, or strategies. |
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