MobileFirst Applications Senior Data Scientist Interview Questions and Answers

Q1. 1. difference between list & tuple 2. describe your day-to-day work 3. describe your favourite project
Ans. 
List is mutable, tuple is immutable. Day-to-day work involves data analysis and modeling. Favorite project involved developing a predictive analytics model.
• List can be modified after creation, tuple cannot

• List uses square brackets [], tuple uses parentheses ()

• Day-to-day work includes data cleaning, exploratory data analysis, model building, and communication of results

• Favorite project involved collecting and analyzing ...read more
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Q2. How does your day look like
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Q3. Your favourite project
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Q4. Difference between regression & classification based algorithms
Ans. 
Regression predicts continuous values, while classification predicts discrete values.
• Regression algorithms predict continuous values, such as predicting house prices based on features like size and location.

• Classification algorithms predict discrete values, such as classifying emails as spam or not spam based on content.

• Regression algorithms include linear regression, polynomial regression, and support vector regressio...read more
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Q5. Difference between RNN & CNN
Ans. 
RNN is used for sequential data like time series, while CNN is used for spatial data like images.
• RNN processes sequential data by maintaining memory of past inputs, suitable for time series forecasting.

• CNN is designed for spatial data like images, using filters to extract features and patterns.

• RNN is good for text data analysis, language translation, and speech recognition.

• CNN is commonly used in image recognition, obj
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Q1. No question was asked
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Q2. Same as above, zero questions were asked
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Q1. Intro about yourself
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Q2. About the project
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1. No one has joined the interview in the first time
2. One week later HR called and scheduled another interview, I communicated HR saying no panelist joined and no communication also. She simply said this time they well join.
Second time also no one joined.
3. Again after 10days HR called me and scheduled another interview. This time he joined.
4. Tell me about my self - I have introduced my self for 10 min
No introduction from his end and he didn’t turn on video
5. Directly he asked me to share screen entire window and asked me to fit a classification model by loading insurance day (mailed by them)

What I don’t understand is do we need to by-hart the entire code or what ? In this GPT age do we need to remember the complete syntax. I told him the steps what to do but he want me to code only.

Q1. 1. Based on my resume, asked to explain ML projects and questions related to the algorithms used were asked. 2. SQL questions 3. Monty-Hall problem 4. Probability and Permutation & Combination questions 5....read more
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Q1. Basic HR questions 1. How early you will be able to join? 2. Are you willing to relocate?
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Python, SQL questions were in the initial round of hiring process.

Q1. ML, DL & knowledge about Cloud were main focus of the interview.
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Q2. Questions around your resume
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Q1. Machine Learning
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Q1. Machine Learning
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Approach check for multiple case studies

Q1. Give a brief about yourself.
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Q2. Salary Expectation and earliest joining date
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Q1. Brief about a project worked in the company.
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Q2. What is Data Leakage?
Ans. 
Data leakage occurs when information from outside the training dataset is used to create a model, leading to unrealistic performance.
• Occurs when information that would not be available in a real-world scenario is used in the model training process

• Can result in overly optimistic performance metrics for the model

• Examples include using future data, target leakage, and data preprocessing errors
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Q3. What is Encoder Decoder? What is a Transformer model and explain its architecture?
Ans. 
Encoder Decoder is a neural network architecture used for sequence-to-sequence tasks. Transformer model is a type of neural network architecture that relies entirely on self-attention mechanisms.
• Encoder Decoder is commonly used in machine translation tasks where the input sequence is encoded into a fixed-length vector representation by the encoder and then decoded into the target sequence by the decoder.

• Transformer mod...read more
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Q4. Name some Deep learning models?
Ans. 
Deep learning models include CNN, RNN, LSTM, GAN, and Transformer.
• Convolutional Neural Networks (CNN) - used for image recognition tasks

• Recurrent Neural Networks (RNN) - used for sequential data like time series

• Long Short-Term Memory (LSTM) - a type of RNN with memory cells

• Generative Adversarial Networks (GAN) - used for generating new data samples

• Transformer - used for natural language processing tasks
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Q5. What is Regularization in machine learning?
Ans. 
Regularization is a technique used in machine learning to prevent overfitting by adding a penalty term to the model's loss function.
• Regularization helps to reduce the complexity of the model by penalizing large coefficients.

• It adds a penalty term to the loss function, which discourages the model from fitting the training data too closely.

• Common types of regularization include L1 (Lasso) and L2 (Ridge) regularization.

• Re...read more
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Q6. What is Model Quantization?
Ans. 
Model quantization is the process of reducing the precision of the weights and activations of a neural network model to improve efficiency.
• Reduces memory usage and speeds up inference by using fewer bits to represent numbers

• Can be applied to both weights and activations in a neural network model

• Examples include converting 32-bit floating point numbers to 8-bit integers
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Q1. Why do we hire you?
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Q2. What contributions should you make?
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