Decision science analyst Interview Questions | Glassdoor

Decision science analyst Interview Questions

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Difference between Logistic and linear regression

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Logistic Regression looks at outputting a binary choice (1 or 0, Yes or No, etc) by determining a probability or "confidence". The technique is popularly used for classification based problems (ex: Is the tumor malignant?) where the output choice is given a probability of being correct (ex: 85% likely that the answer is "yes" so output "yes"). Linear Regression has a virtually unlimited number of answers/outputs since it usually outputs a forecasted value (ex: What value will this building have in 2019?). Linear Regression outputs a likely value in a distribution of potential values. The outputs are continuous as opposed to discrete and it is often used for problems like forecasting.

2 samples of gender data of births at hospitals - one large, one small. Two different gender ratios - the larger sample is is 50% and the other is 70%. Question is, why this skew?

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What can you bring to the company?

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What is a continuous function?

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Probability questions, case study questions

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What is the unsupervised learning? What is supervised learning? What is the difference between them?

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