Anomalies Detection in Repayment Speed Background: Repayment speed is a term used to describe how quickly the customer(s) is making efforts trying to unlock the product. The lower the value the slower the customer trying to achieve the same. This is one of the metrics that are key in evaluating the performance of a customer across the unit proportional age of the product before it is unlocked.

Challenges: Different customers have different habits of making payments. For instance, some of the customers prefer payment once a month but cover the entire four weeks of payments while some prefer weekly. It is important to know when such customers deviate from their original course of action in terms of their repayment style. We need require a system that will be proactive enough and support credit managers to detect this peculiar issue.

Data: You have been provided with a data set. Develop a model that will assist in detecting these anomalies described above.

Expectations: ● Data preparation ● Model development ● Statistically, the justification of the model is in the report document. ● Submit the model deployment file. ● Model Deployment to production (optional) Weight: 40%

★ Microsoft Excel: You have been provided with a data set, develop a functional dashboard using the key functions such as Vlookup, Pivot table, Data Validation, Conditional functions, text functions, aggregate functions etc.

Expectations: ● Conditional functions ● Pivot table ● Text functions - Data cleaning, concat, etc ● Database aggregate functions (DSUM) etc

★ SQL - This will be tested during the live sessions: Expectations: ● CRUD functions ● Read Data - patterns match, clauses, etc ● Joins ● Group By, Having, etc.. ● Case When Then End ● Views Weight: 30%

We have provided you with an X dataset in .xls formats. Please utilize it for the exercise. a. The company would like us to randomly survey customers who bought a Boom product. Select a random sample, with a sample size determined by a confidence level of 95% and a margin error of 5%. This sample should be stratified by region such that it is balanced for each. Export the list of randomly selected observations to a separate excel file. b. The company would want to determine if the males have a higher repayment speed than the females. Using the sample data above, conduct a test statistic at an 88% confidence level and provide your conclusion. Weight: 15%

Requirement: Development of a simple animation platform using HTML and CSS. Content: Objects moving on an index page ● Python / R
● Excel / Google Sheet ● HTML, CSS, Javascripts, React ● Git - [version control] Experience Weight: 15%.