Ignitarium Technology Solutions Data Scientist Interview Questions and Answers

Q1. When do we use PCA?
Ans. 
PCA is used to reduce the dimensionality of data by finding the most important features.
• PCA is used when dealing with high-dimensional data to reduce the number of features and avoid multicollinearity.

• It helps in visualizing data in lower dimensions while retaining as much variance as possible.

• PCA is commonly used in image processing, genetics, finance, and other fields where dimensionality reduction is needed.
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Q2. PCA is a dimensionality reduction technique and is used to reduce the less useful features from the dataset
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Q1. LTTS data science interview
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Q1. Difference between .py and .pyc
Ans. 
The .py files contain Python code, while the .pyc files are compiled bytecode files generated by Python when a .py file is imported.
• The .py files are human-readable text files containing Python code.

• The .pyc files are compiled bytecode files created by Python to improve execution speed.

• The .pyc files are automatically generated by Python when a .py file is imported.

• The .pyc files are platform-independent and can be dis
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Q2. Why we use bias in neural networks? Why do we scale our data in neural networks?
Ans. 
Bias in neural networks helps in capturing the underlying patterns in data. Scaling data helps in improving convergence and performance.
• Bias in neural networks helps in shifting the activation function to better fit the data.

• It allows the model to capture the underlying patterns in the data by providing flexibility in the decision boundary.

• Scaling data helps in improving convergence by ensuring that the gradients are o...read more
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Q3. Explain RNN and LSTM
Ans. 
RNN is a type of neural network that processes sequential data. LSTM is a type of RNN that can learn long-term dependencies.
• RNN stands for Recurrent Neural Network and is designed to handle sequential data by maintaining a hidden state that captures information about previous inputs.

• LSTM stands for Long Short-Term Memory and is a type of RNN that addresses the vanishing gradient problem by introducing a memory cell, in...read more
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Case study was given to test coding and ML skills

Q1. Based on Case Study
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Q2. Based on Case Study
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Q1. Overall interview feedback
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Q2. Salary expectations
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Q1. Discussion on projects mentioned in the Resume
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Q2. Two Python live coding questions
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Q3. Few DL, ML, Transformers, and NLP questions Few questions on deployment tools mentioned in the resume
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Q1. General HR discussion
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