Python Data Science Interview Questions and Answers, Pybrain

Basic python data science interview Questions

Q. Explain a univariate analysis in Data Science?

A. An Analysis that can be applied to one attribute at a time is called as a univariate analysis.
Boxplot is one of the widely used univariate model.
Scatter plot and cook’s distance are other methods used for bivariate and multivariate analysis.

What is overfitting in Data Science?

Any prediction rate which has high inconsistency between the training error and the test error leads to a high business problem, if the error rate in training set is low and the error rate is high, then we can conclude it as overfitting model.
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Q. In Data Science What is under fitting?
A. Any prediction rate which has provides low prediction in the training error and the test error leads to a high business problem, if the error rate in training set is high and the error rate in the test set is also high, then we can conclude it as Underfitting model.

Q. Name few methods for Missing Value Treatments?
A. Central Imputation – This method acts more like central tendencies. All the missing values will be filed with mean and median mode respective to numerical and categorical datatypes.
KNN – K Nearest Neighbour imputation.
Distance between two or multiple attributes are calculated using Euclidian’s distance and the same will be used to treat the missing values. Mean and mode will agaibe n used as in CI.

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Q. Tell me about the Pearson correlation?
A. Correlation between predicted and actual data can be examined and understood using this method.
The range is from -1 to +1.
-1 refers to negative 100% whereas +1 refers to positive 100%.
The formula is Sd(x)*m/Sd.(y).

Q. How Machine Learning Is Deployed In Real World Scenarios?
A. Here are some of the scenarios in which machine learning finds applications in real world:
Ecommerce: Understanding the customer churn, deploying targeted advertising, remarketing.
Search engine: Ranking pages depending on the personal preferences of the searcher
Finance: Evaluating investment opportunities & risks, detecting fraudulent transactions
Medicare: Designing drugs depending on the patient’s history and needs
Robotics: Machine learning for handling situations that are out of the ordinary
Social media: Understanding relationships and recommending connections
Extraction of information: framing questions for getting answers from databases over the web.

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