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Curricular information is subject to change
⦁ Have a comprehensive appreciation of the key issues involved in predictive analytics in banking.
⦁ Understand fundamental ideas which underpin the methodologies introduced.
⦁ Demonstrate a knowledge of the institutional and regulatory contexts of the illustrated application areas in banking.
⦁ Be able to explain in detail and model in practice classification related problems in banking.
⦁ Have an appreciation of the role of economic policy and regulation in the predictive analytics in banking field.
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Tutorial | 12 |
Autonomous Student Learning | 90 |
Total | 126 |
One Regression Analysis module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Class Test: Mid-term exam | Week 8 | n/a | Graded | No | 60 |
Group Project: Assignments | Varies over the Trimester | n/a | Graded | No | 40 |
Not yet recorded |
Name | Role |
---|---|
Mr Shivam Agarwal | Tutor |
Parvati Neelakantan | Tutor |