They haven't formulated clear questions yet, but here's what I've gathered: 1. Explaining dimensional modeling. 2. Understanding facts and dimensions and their functionality. 3. Exploring the necessity of surrogate keys. 4. Sharing experiences with unstructured or semi-structured data.
Why did you further your study? What are different medallion of Data Architecture? Why Credit Modeling?
Regarding the dynamic simulation of the different process related to plant operations
What would you look at in determining whether to extend credit
Going through CV and motivation
How do you do optimization modeling
How to determine a Palindrome String?
Several in-depth questions in theory.
1. Explain PD, LGD, EAD 2. Python Questions: use 'describe' to show the dataframe's info 3. what is p-value 4. confidence interval 5. How to find outlier 6. VaR
One of the Professional Learning Indicator test was Which number is lower: 1/6, 2/9, 1/2-2/5, 1/2-2/7
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