What is data cleansing and why is it important?
It is imperative that a fintech data analyst understand how important it is that the data they work with be free of inconsistencies and errors, especially in the fintech field where bad data can result in lost clients and big costs to the company. The interviewer wants to know that you understand how to cleanse data and why it's necessary to do so.
"Throughout my college career and the internships I've completed, if there's one thing that I've learned it's that only good data will give you good results. Naturally, high-quality data is data that has had all errors and inconsistencies removed from it. One way I clean data is to segregate it by certain characteristics so I can then look at similar data grouped together. This is also helpful because it puts the data into smaller groups, which makes it easier to spot errors. I also tend to spend time analyzing the statistics of each data column, and many times I create scripts that automatically cleanse common errors from my dataset. I know how vital it is that the data be free of errors, and before I analyze any data I always cleanse it first."