Times Internet Data Scientist Interview Questions and Answers

Leetcode hard level coding with 4-5 questions

Q1. Machine Learning algorithms, Statistics, NLP
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Q1. Probability ,deep learning basics ,machine learning ,simple python programming questions.
Ans. It will be multilpe choice questions .Duration - 40 minutes.
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Q1. Why this company ,work related to your project,some technical questions on deep learning.
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Q1. Explain all of decision tree and random forest?
Ans. 
Decision tree is a tree-like model of decisions and their possible consequences, while random forest is an ensemble learning method that builds multiple decision trees and merges them together.
• Decision tree is a flowchart-like structure where each internal node represents a decision based on an attribute, each branch represents the outcome of the decision, and each leaf node represents a class label.

• Random forest is a ...read more
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Q2. Some basic probability questions
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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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Q1. Basic ML/DL and statistics questions
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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. Describe LSTM and GRU
Ans. 
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
Ans. 
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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