Do you perform data wrangling and data cleaning before applying machine learning algorithms to your data analysis?

How to Answer
Answer Example

This is an operational question. The Doordash interviewer will ask operational questions to learn more about how you go about doing your job. One of the key responsibilities of a data scientist is to ensure that the data sets they are using are appropriate for the analysis they are performing. Data wrangling and cleaning are two processes used to accomplish this. You should be familiar with these and able to explain them. As with any operational question, keep your answer direct and to the point and anticipate a follow-up question or two.

"I believe that it is important to perform both data wrangling and cleaning before applying any machine learning algorithms. This will ensure that the data set is appropriate. They are the data sets I intended to work with for my analysis. The standard deviations meet the study guidelines, the relationships between the data are valid, and the data is normalized and standardized. This eliminates any outliers or variables that would potentially skew the results I obtain."

Next Question

How to Answer: Do you perform data wrangling and data cleaning before applying machine learning algorithms to your data analysis?

Advice and answer examples written specifically for a DoorDash job interview.

3. Do you perform data wrangling and data cleaning before applying machine learning algorithms to your data analysis?

How to Answer

This is an operational question. The Doordash interviewer will ask operational questions to learn more about how you go about doing your job. One of the key responsibilities of a data scientist is to ensure that the data sets they are using are appropriate for the analysis they are performing. Data wrangling and cleaning are two processes used to accomplish this. You should be familiar with these and able to explain them. As with any operational question, keep your answer direct and to the point and anticipate a follow-up question or two.

Answer Example

"I believe that it is important to perform both data wrangling and cleaning before applying any machine learning algorithms. This will ensure that the data set is appropriate. They are the data sets I intended to work with for my analysis. The standard deviations meet the study guidelines, the relationships between the data are valid, and the data is normalized and standardized. This eliminates any outliers or variables that would potentially skew the results I obtain."

Next Question