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Top + Data Science Interview Questions & Answers [UPDATED]
Is more data always better?
It depends on your model. Hence, always think about the cost of having more data.
What are advantages of plotting your data before performing analysis? Data sets have errors. That year old man. That 9 foot tall woman. Variables can have skewness, outliers etc. Then the arithmetic mean might not be useful. Variables can be multimodal!
If a variable is multimodal then anything based on its mean or median is going to be suspect. Proper exploratory data analysis. The exploratory phase will generate lots of possible hypotheses, and the exploitatory phase will let you really understand a few of them.
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What is the role of trial and error in data analysis? What is the role of making a hypothesisbefore diving in? The scientific method is eminently inductive: As a result, we come up with new hypotheses which are in turn tested and so on.
This is an iterative process, as science always is. Look at the variables added in forward variable selection Q How do you deal with some of your predictors being missing? Build another predictive model to predict the missing values — This could be a whole project in itself, so simple techniques are usually used here. Use a model that can incorporate missing data — Like a random forest, or any tree-based method.
You have several variables that are positively correlated with your response, and you thinkcombining all of the variables could give you a good prediction of your response. However, you see that in the multiple linear regression, one of the weights on the predictors is negative. What could be the issue?
Multicollinearity refers to a situation in which two or more explanatory variables in a multiple regression model are highly linearly related. But Zre will choose my life partner very carefully.
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