
Assignment outline
This individual 3,000 words (excl. title page, contents page, appendix, and bibliography) assessment is worth 100% of the module marks. You are required to submit a report.
Once your assignment has been marked, you will receive written feedback from ML.
Assignment aim
The assignment aims to show your understanding of several key ideas and concepts covered in the module and your ability to apply those concepts in a meaningful way. The assignment will involve a combination of critical analysis and interpretation.
Assignment background
You have been recently started to work as an Artificial Intelligence and Data Scientists at a large organisation (of your choice). The company’s top management is not familiar with AI/DS techniques and would like to understand these techniques better. Therefore, top management asked you to prepare a two-part report showing how AI/DS techniques (i.e. machine learning, deep learning) can be used in organisational decision-making.
• The first part of the report is the literature review part. You should critically review the literature and highlight the opportunities and challenges of adopting AI/DS techniques within an organisation. You are expected to justify your arguments through the use of literature and should have a good balance of academic peer-reviewed journals, textbooks, other reports, and websites. In addition, you are expected to support your arguments with case study examples. (1,500 words) (50%)
• The second part of the report is the data analysis part. By selecting a company of your interest, you must download the data1 (at least 1,000 rows) of the chosen company. By investigating the company’s financial and industry performance, you are expected to provide recommendations to your manager on how to increase sales and profits in the company. (1,500 words) (50%)
o Students are required to use R software to analyse the data.
1 Data can be downloaded from Kaggle.
Please make sure that Harvard referencing is applied throughout your coursework.
The coursework must be submitted via the Canvas “Assignments” section.
The deadline date for this assignment will be released on Canvas in due course.