Times Internet Data Scientist Interview Questions and Answers

Q1. 1. First question was, from my project asked me that explain one project.
Ans. I have done three project, out of this three project one project was movie recommendation syatem. That i was explained. Then some counter question from project like how did you convert text to numerical, which technique you used here. I able to give all questions answer from project.
Answered by adventurouseggnog
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Q2. 2. After they asked me, what is your subject preference?
Ans. My subject preference was NLP, Deep Learning, ML. Afrer that asked some question from NLP like what is term document matrix, tf-idf, word embedding, Gradient descent, why we have to change learning rate after every epoch, how lr change, precision recall.
Answered by adventurouseggnog
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Q3. 3. My one projcet was sentimental analysis that was by LSTM model. So, next question was explain how LSTM model work and architecture .
Ans. Then I was explained workin flow of LST model and architecture. Then asked me did you know bidireectional LSTM. I said yes, then explained.
Answered by adventurouseggnog
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Q4. 4. One question was from sql. what is the condition for primary key? last question one case business case study. this is all about my interview. My interview was 50 min and selected for HR round.
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Q1. Phone call round, just asked me are you interested to come here.
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Leetcode hard level coding with 4-5 questions

Q1. Machine Learning algorithms, Statistics, NLP
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Q1. Basic ML/DL and statistics questions
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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. 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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Test 45 mins 30 ques

Q1. What is Linearregression
Ans. 
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
Ans. 
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
Ans. 
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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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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