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"When you apply for a job in the field of analytics, you can expect to be asked technical questions and given math problems that will test your knowledge of statistical math and data management and analysis. The interviewer may test your critical thinking and decision making skills by having you solve word problems on the spot, as you will be expected to know how to gather insights into and draw conclusions from the data you collect on the job. Analytics is a broad field, and the technical questions you are asked will depend on the position you are applying for."

14,518 analytics interview questions shared by candidates

 1). What is overfitting?  / Please briefly describe what is bias vs. variance.       2). How do you overcome overfitting? Please list 3-5 practical experience.    / What is 'Dimension Curse'? How to prevent?       3). Please briefly describe the Random Forest classifier. How did it work? Any pros and cons in practical implementation?       4). Please describe the difference between GBM tree model and Random Forest.       5). What is SVM? what parameters you will need to tune during model training? How is different kernel changing the classification result?       6). Briefly rephrase PCA in your own way. How does it work? And tell some goods and bads about it.       7). Why doesn't logistic regression use R^2?       8). When will you use L1 regularization compared to L2?
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Data Analytics

Interviewed at FirstEnergy

2.6★
Jun 21, 2021

 1). What is overfitting?  / Please briefly describe what is bias vs. variance.       2). How do you overcome overfitting? Please list 3-5 practical experience.    / What is 'Dimension Curse'? How to prevent?       3). Please briefly describe the Random Forest classifier. How did it work? Any pros and cons in practical implementation?       4). Please describe the difference between GBM tree model and Random Forest.       5). What is SVM? what parameters you will need to tune during model training? How is different kernel changing the classification result?       6). Briefly rephrase PCA in your own way. How does it work? And tell some goods and bads about it.       7). Why doesn't logistic regression use R^2?       8). When will you use L1 regularization compared to L2?

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