Sitaram Jewels Data Scientist Interview Questions and Answers

Q1. About your project and experience
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Core Python questions were asked

Q1. ML related questions and previous company's project related
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Q2. What is Regression?Prepare basci pandas related questions
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Q1. Tell me about yourself?
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I am a data scientist with a background in statistics and machine learning, passionate about solving complex problems using data-driven approaches.
• Background in statistics and machine learning

• Experience in solving complex problems using data-driven approaches

• Passionate about leveraging data to drive insights and decision-making
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Q2. Describe in detail about one of my main project.
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Developed a predictive model for customer churn in a telecom company.
• Collected and cleaned customer data including usage patterns and demographics.

• Used machine learning algorithms such as logistic regression and random forest to build the model.

• Evaluated model performance using metrics like accuracy, precision, and recall.

• Implemented the model into the company's CRM system for real-time predictions.
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Q3. Few questions related to my projects.
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Q1. Questions on Basics python(Since i am fresher)
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Q1. Explain NLP project life cycle for sentiment analysis in detail
Ans. 
NLP project life cycle for sentiment analysis involves data collection, preprocessing, model training, evaluation, and deployment.
• Data collection: Gather text data from various sources like social media, reviews, or surveys.

• Data preprocessing: Clean and preprocess the text data by removing stopwords, punctuation, and special characters.

• Model training: Use machine learning or deep learning algorithms to train a sentimen...read more
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Q2. Which models you use for sentiment analysis or summarisation
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I use models like LSTM, BERT, and Transformer for sentiment analysis and summarization.
• LSTM (Long Short-Term Memory) for sequence prediction tasks like sentiment analysis

• BERT (Bidirectional Encoder Representations from Transformers) for contextual word embeddings

• Transformer for attention-based sequence-to-sequence tasks like summarization
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