Target Data Scientist Interview Questions and Answers

Q1. Heavy SQL solutions
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Retail case study, with soft skills is required for this round

Python and sql based questions

Q1. Intermediate level SQL questions using joins and case when
Ans. 
Intermediate SQL question focusing on joins and case when statements for data manipulation.
• Joins combine rows from two or more tables based on a related column. Example: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id.

• CASE WHEN statements allow conditional logic in SQL queries. Example: SELECT name, CASE WHEN age < 18 THEN 'Minor' ELSE 'Adult' END AS age_group FROM users.

• INNER JOIN returns only matching ro...read more
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Q2. String manipulation advanced level question in python
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Q1. Bias and variance with respect to model
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Bias and variance are two types of errors that can occur in a model.
• Bias refers to the error introduced by approximating a real-world problem, leading to underfitting.

• Variance refers to the error introduced by modeling the noise in the training data, leading to overfitting.

• Balancing bias and variance is crucial for creating a model that generalizes well to unseen data.
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Q2. Hypotheses test
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Round duration - 60 Minutes
Round difficulty - Medium

I was asked two questions in this round . More emphasis was given on the theoretical aspect of the subject in this round .

Q1. How can you tune the hyperparameters of the XGBoost algorithm?
Ans. 
Hyperparameters of XGBoost can be tuned using techniques like grid search, random search, and Bayesian optimization.
• Use grid search to exhaustively search through a specified parameter grid

• Utilize random search to randomly sample hyperparameters from a specified distribution

• Apply Bayesian optimization to sequentially choose hyperparameters based on the outcomes of previous iterations
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Q2. Can you explain the hyperparameters in the XGBoost algorithm?
Ans. 
Hyperparameters in XGBoost algorithm control the behavior of the model during training.
• Hyperparameters include parameters like learning rate, max depth, number of trees, etc.

• They are set before the training process and can greatly impact the model's performance.

• Example: 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100
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Round duration - 50 Minutes
Round difficulty - Medium

This round basically tested some fundamental concepts related to Machine Learning and proper ways to implement a model.

Q1. What is the difference between Ridge and LASSO regression?
Ans. 
Ridge and LASSO regression are both regularization techniques used in linear regression to prevent overfitting by adding penalty terms to the cost function.
• Ridge regression adds a penalty term equivalent to the square of the magnitude of coefficients (L2 regularization).

• LASSO regression adds a penalty term equivalent to the absolute value of the magnitude of coefficients (L1 regularization).

• Ridge regression tends to sh...read more
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Round duration - 50 Minutes
Round difficulty - Medium

This round was based on some basic concepts revolving around Deep Learning .

Q1. What are outlier values and how do you treat them?
Ans. 
Outlier values are data points that significantly differ from the rest of the data, potentially affecting the analysis.
• Outliers can be identified using statistical methods like Z-score or IQR.

• Treatment options include removing outliers, transforming the data, or using robust statistical methods.

• Example: In a dataset of salaries, a value much higher or lower than the rest may be considered an outlier.
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Round duration - 30 Minutes
Round difficulty - Easy

This is a cultural fitment testing round .HR was very frank and asked standard questions. Then we discussed about my role.

Q1. What do you know about the company?
Ans. 
The company is a leading innovator in technology solutions, focusing on data analytics and machine learning to drive business success.
• Founded in 2010, the company has rapidly grown to become a key player in the tech industry.

• They specialize in providing data-driven insights to help businesses optimize their operations.

• The company has a diverse portfolio, including products for predictive analytics and customer segmenta...read more
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Q2. Why should we hire you?
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Case Study was related to customer propensity to buy.

Q1. Linear Regression Assumptions
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Linear regression assumptions include linearity, independence, homoscedasticity, and normality.
• Assumption of linearity: The relationship between the independent and dependent variables is linear.

• Assumption of independence: The residuals are independent of each other.

• Assumption of homoscedasticity: The variance of the residuals is constant across all levels of the independent variables.

• Assumption of normality: The resid...read more
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Q2. ML Algorithms and Evaluation Metrics.
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Q1. What is VIF(variance inflation factor)
Ans. 
VIF is a measure of multicollinearity in regression analysis, indicating how much the variance of an estimated regression coefficient is increased due to collinearity.
• VIF values greater than 10 indicate high multicollinearity

• VIF is calculated for each predictor variable in a regression model

• VIF is calculated as 1 / (1 - R^2) where R^2 is the coefficient of determination from regressing a predictor on all other predicto...read more
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Q1. Why do you want to join?
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