Tata Motors Data Scientist Interview Questions and Answers

Q1. What is the difference between linear and classification?
Ans. 
Linear regression is used for predicting continuous values, while classification is used for predicting discrete values.
• Linear regression is used when the output variable is continuous, such as predicting house prices based on features like size and location.

• Classification is used when the output variable is categorical, such as predicting whether an email is spam or not based on its content.

• Linear regression aims to f...read more
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Q2. Explain example of outlier
Ans. 
An outlier is a data point that differs significantly from other data points in a dataset.
• Outliers can skew statistical analyses and machine learning models.

• Examples of outliers include a person's weight being recorded as 1000 lbs, when the average weight is around 150 lbs.

• Outliers can be detected using statistical methods like Z-score or IQR.
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Q3. Explain k mean algorithm
Ans. 
K-means algorithm is a clustering technique that partitions data into k clusters based on similarity.
• Divides data points into k clusters based on centroids

• Iteratively assigns data points to the nearest centroid and updates centroids

• Continues until centroids no longer change significantly

• Example: Grouping customers based on purchasing behavior
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Q4. Explain the metrics for classification
Ans. 
Classification metrics are used to evaluate the performance of a classification model.
• Accuracy: measures the proportion of correctly classified instances out of total instances

• Precision: measures the proportion of true positive predictions out of all positive predictions

• Recall: measures the proportion of true positive predictions out of all actual positive instances

• F1 Score: harmonic mean of precision and recall, balan...read more
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Q1. Previous experience, basic sql
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Q1. How did we solve business problems thru analytics stack
Ans. 
We utilized our analytics stack to identify patterns, trends, and insights in data to address business challenges.
• Utilized data visualization tools to identify trends and patterns in customer behavior

• Leveraged machine learning algorithms to predict customer churn and optimize marketing strategies

• Used statistical analysis to determine the impact of pricing changes on sales performance
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Q1. What is difference between lstm and rnn
Ans. 
LSTM is a type of RNN that addresses the vanishing gradient problem by using memory cells.
• RNN stands for Recurrent Neural Network, a type of neural network that processes sequential data.

• LSTM stands for Long Short-Term Memory, a type of RNN that includes memory cells to retain information over long sequences.

• LSTM is designed to overcome the vanishing gradient problem, which occurs when training RNNs on long sequences.

• L...read more
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Q1. What are different evaluation matrix ?
Ans. 
Evaluation matrices are used to assess the performance of models in data science.
• Confusion matrix: used to evaluate the performance of classification models.

• Precision, recall, and F1 score: measures for binary classification models.

• Mean squared error (MSE): evaluates the performance of regression models.

• R-squared: assesses the goodness of fit for regression models.

• Area under the ROC curve (AUC-ROC): evaluates the perfo...read more
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Q1. Introduction your self
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Q2. What are the language you learned
Ans. 
I have learned multiple programming languages including Python, R, SQL, and Java.
• Python

• R

• SQL

• Java
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Q1. Tell me about yourself
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Q2. Questions from projects which was mentioned on resume
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Q1. Fitment based questions
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Q2. Explain a project
Ans. 
Developed a machine learning model to predict customer churn for a telecom company.
• Collected and cleaned customer data including usage patterns and demographics

• Used classification algorithms like Random Forest and Logistic Regression to build the model

• Evaluated model performance using metrics like accuracy, precision, and recall
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Math, English, reasoning

Q1. Asked me about expected salary
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PYTHON,SQL,STATS,ML,DL

Q1. Strenght and weakness, long term and short term goal
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