Eternal Limited Data Scientist Interview Questions and Answers

Q1. Coding and data structure
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Q1. ML Breadth and depth
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Q1. What do you know about Uber?
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Q2. Went over my current role
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Q1. What was the most interesting project you worked on
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Q2. Design and experiment to validate the new recommendation engine
Ans. 
Designing an experiment to validate a recommendation engine involves A/B testing, metrics, and user feedback for effectiveness.
• A/B Testing: Split users into two groups, one using the new engine and the other using the old one, to compare performance metrics.

• Key Metrics: Measure click-through rates, conversion rates, and user engagement to assess the effectiveness of the new engine.

• User Feedback: Collect qualitative fee...read more
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It been for 45 mins. question asked from python,ML,Deep learning and maths.

Q1. 1) explain correlation and convaraince 2) how logistic differ from linear regression
Ans. 
Correlation measures the strength and direction of a linear relationship between two variables, while covariance measures the extent to which two variables change together.
• Correlation ranges from -1 to 1, where 1 indicates a perfect positive relationship, -1 indicates a perfect negative relationship, and 0 indicates no relationship.

• Covariance can be positive, negative, or zero. A positive covariance indicates that as o...read more
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Test 45 mins 30 ques

Q1. What is Linearregression
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Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.
• Linear regression is used to predict the value of a dependent variable based on the value of one or more independent variables.

• It assumes a linear relationship between the independent and dependent variables.

• The goal of linear regression is to find the best-fitting line that minimi...read more
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Q2. What is random forest
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Random forest is an ensemble learning method used for classification and regression tasks.
• Random forest is a collection of decision trees that are trained on random subsets of the data.

• Each tree in the random forest independently predicts the target variable, and the final prediction is made by averaging the predictions of all trees.

• Random forest is robust to overfitting and noisy data, and it can handle large datasets...read more
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Q3. WHat is xgboost
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XGBoost is an optimized distributed gradient boosting library designed for efficient and accurate large-scale machine learning.
• XGBoost stands for eXtreme Gradient Boosting.

• It is a popular machine learning algorithm known for its speed and performance.

• XGBoost is used for regression, classification, ranking, and user-defined prediction problems.

• It is based on the gradient boosting framework and uses decision trees as bas...read more
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Q1. Describe LSTM and GRU
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LSTM and GRU are types of recurrent neural networks used for processing sequential data.
• LSTM (Long Short-Term Memory) networks are capable of learning long-term dependencies in data.

• GRU (Gated Recurrent Unit) networks are simpler than LSTM and have fewer parameters.

• LSTM has three gates (input, output, forget) while GRU has two gates (update, reset).

• LSTM is better at capturing long-term dependencies but is more complex,...read more
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Q2. Define Hypothesis Testing
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Hypothesis testing is a statistical method used to make inferences about a population based on sample data.
• Hypothesis testing involves formulating a null hypothesis and an alternative hypothesis.

• It aims to determine if there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis.

• Common methods of hypothesis testing include t-tests, chi-square tests, and ANOVA.

• The p-value is used to dete...read more
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Mcq based test on data science concepts

Q1. Explain precision,recall etc
Ans. 
Precision and recall are metrics used to evaluate the performance of classification models.
• Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

• Recall is the ratio of correctly predicted positive observations to the all observations in actual class.

• F1 score is the weighted average of precision and recall, where the best value is 1 and the worst is 0.

• Precision ...read more
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Q2. What is dropout in neural networks
Ans. 
Dropout is a regularization technique used in neural networks to prevent overfitting by randomly setting some neuron outputs to zero during training.
• Dropout is a regularization technique used in neural networks to prevent overfitting.

• During training, a fraction of neurons are randomly selected and their outputs are set to zero.

• This helps prevent complex co-adaptations in neurons and improves generalization.

• Dropout is t...read more
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Q1. Attention Mechanism
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Q2. Working of Decision Trees
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Decision Trees are a supervised learning algorithm used for classification and regression tasks, visualizing decisions as tree-like structures.
• Decision Trees split data into subsets based on feature values, creating branches for each decision point.

• Each node represents a feature, and each branch represents a decision rule, leading to a leaf node with the final prediction.

• They use metrics like Gini impurity or entropy t...read more
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Q3. Natural language processing
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