2025/6, Trimester 1, ONLINE, 
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			   |    Occurrence:    |  001  |  
   |    Primary mode of delivery:    |   ONLINE   |  
   |    Location of delivery:    |   ONLINE   |  
   |    Partner:    |      |  
   |    Member of staff responsible for delivering module:    |   Sujoy Bhattacharya   |  
   |    Module Organiser:    |      |  
| Student Activity (Notional Equivalent  Study Hours (NESH)) | 
| Mode of activity | Learning & Teaching Activity | NESH (Study Hours) | NESH Description | 
 
  | Independent Learning |   Practical classes and workshops |   12 |   In weekly online live academic sessions, you will apply techniques to explore how machine learning and AI can be used in various businesses. You will use analytical software in an online environment, focusing on real-world business scenarios. |  
   | Independent Learning |   Guided independent study |   188 |   Independent reading: You will engage with materials on specific topics at your own pace. A variety of online resources (e.g., code samples, articles, textbooks, videos, etc.) will be provided to help you develop a deep understanding of the module content. |  
 | Total Study Hours | 200 |  | 
 | Expected Total Study Hours for Module | 200 |  | 
   |    Assessment    |  
   | Type of Assessment |   Weighting % |   LOs covered |   Week due |   Length in Hours/Words |   Description |  
   | Practical Skills Assessment |   30 |   3~4 |   Week 10 |   HOURS= 10 Minutes |   You will complete one practical task related to applying machine learning with R/Python to develop practical skills and find answers to business problems. You will create a presentation using the results obtained from the practical task. |  
   | Essay |   70 |   1~2~3 |   Week 13 |   , WORDS= 2000 words |   You will write a 2,000-word essay exploring how AI and machine learning enhance business analytics and organisational performance, while also addressing the ethical considerations and potential risks involved. |  
  | Component 1 subtotal: |  30 |   |  | 
 | Component 2 subtotal: |  70 |   |  |  |  | 
  | Module subtotal: |  100 |   |  |   |  | 
 
		 
			2025/6, Trimester 2, Online (fully o, 
VIEW FULL DETAILS 
		 
			   |    Occurrence:    |  001  |  
   |    Primary mode of delivery:    |   Online (fully o   |  
   |    Location of delivery:    |   ONLINE   |  
   |    Partner:    |      |  
   |    Member of staff responsible for delivering module:    |   Sujoy Bhattacharya   |  
   |    Module Organiser:    |      |  
| Student Activity (Notional Equivalent  Study Hours (NESH)) | 
| Mode of activity | Learning & Teaching Activity | NESH (Study Hours) | NESH Description | 
 
  | Independent Learning |   Practical classes and workshops |   12 |   In weekly online live academic sessions, you will apply techniques to explore how machine learning and AI can be used in various businesses. You will use analytical software in an online environment, focusing on real-world business scenarios. |  
   | Independent Learning |   Guided independent study |   188 |   Independent reading: You will engage with materials on specific topics at your own pace. A variety of online resources (e.g., code samples, articles, textbooks, videos, etc.) will be provided to help you develop a deep understanding of the module content. |  
 | Total Study Hours | 200 |  | 
 | Expected Total Study Hours for Module | 200 |  | 
   |    Assessment    |  
   | Type of Assessment |   Weighting % |   LOs covered |   Week due |   Length in Hours/Words |   Description |  
   | Practical Skills Assessment |   30 |   3~4 |   Week 10 |   HOURS= 10 Minutes |   You will complete one practical task related to applying machine learning with R/Python to develop practical skills and find answers to business problems. You will create a presentation using the results obtained from the practical task. |  
   | Essay |   70 |   1~2~3 |   Week 13 |   , WORDS= 2000 words |   You will write a 2,000-word essay exploring how AI and machine learning enhance business analytics and organisational performance, while also addressing the ethical considerations and potential risks involved. |  
  | Component 1 subtotal: |  30 |   |  | 
 | Component 2 subtotal: |  70 |   |  |  |  | 
  | Module subtotal: |  100 |   |  |   |  |