Qualifi Level 5 DAI501 Visualisation (M/651/0605) Assignment Brief 2026
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DAI501 Assignment Brief
| Qualification | Qualifi Level 5 Diploma in Artificial Intelligence (610/3935/4) |
|---|---|
| Unit Code | DAI501 |
| Unit Title | Visualisation |
| Unit Reference | M/651/0605 |
| Credits | 20 |
| TQT | 200 |
| GLH | 120 |
Assignment Aim
This unit aims to provide students with fundamental knowledge of and hands-on practical skills in performing data visualization using graphic user-interface design and effective visual communication. Learners will cover the principles and theories of visualization and appreciate the purpose and benefits of the different types of data visualization and their respective tools and technologies. Students will practise applying user-experience design principles and communication skills using popular visualization tools and software to create interactive visualization on given datasets.
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 the importance of data visualisation in business intelligence.
|
1.1 Discuss data visualisation and its benefits in business intelligence. |
| 1.2 Explain the different types of data visualization and when best to optimize them. | |
| 1.3 Compare the various tools and technologies used in data visualization. | |
| 2. Be able to carry out data preparation for visualization.
|
2.1 Organise data collection, cleansing and preprocessing for effective data visualization |
| 2.2 Perform data quality validation and data preparation. | |
| 3. Be able to use different visual representations for decision-making. | 3.1 Identify and select appropriate visualization tools to perform data visualization |
| 3.2 Carry out visualization on a given dataset using appropriate visualization tools | |
| 4. Be able to present visualizations using appropriate visualization tools. | 4.1 Perform data preparation for visualizations. |
| 4.2 Present visualization on a given real-life business scenario. | |
| 4.3 Analyse visualization results to interpret business insights. |
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