Tiger Analytics Data Science Intern Interview Questions and Answers

In both aptitude and coding in the second round, aptitude mostly consists of basic problems and there are some data science problems like bias, stats and probability.

2 coding problems the ones I got are easier didn't take more than 15 minutes to solve both of them.

Q1. Pretty hard technical interview from formulae behind algorithms to math to algorithms touched the sight of all basic data science questions that are supposed to be asked on data science interview
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Q2. What is gradient descent, why does gradient descent follow tan angles and please explain and write down the formula of it.
Ans. 
Gradient descent is an optimization algorithm used to minimize the cost function of a machine learning model.
• Gradient descent is used to update the parameters of a model to minimize the cost function.

• It follows the direction of steepest descent, which is the negative gradient of the cost function.

• The learning rate determines the step size of the algorithm.

• The formula for gradient descent is: theta = theta - alpha * (1/...read more
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Q1. Managerial Technical round asked some basic level coding questions and data handling with lists, tuples, sets and dicts.
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Q2. Please write a dictionary and try to sort it.
Ans. 
A dictionary sorted in ascending order based on keys.
• Create a dictionary with key-value pairs

• Use the sorted() function to sort the dictionary based on keys

• Convert the sorted dictionary into a list of tuples

• Use the dict() constructor to create a new dictionary from the sorted list of tuples
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Q1. What is your family background?
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Q2. Why should we hire you?
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Q3. Where do you see yourself in 5 years?
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Q4. Why are you looking for a change?
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Q5. What are your strengths and weaknesses?
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Q6. Tell me about yourself.
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Q1. What are the key topics related to Generative AI, specifically focusing on Retrieval-Augmented Generation (RAG) and Large Language Models (LLM)?
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Q2. What is your understanding of the existing project and the technology stacks used?
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Q1. What has been your contribution to existing work and the technologies used in that context?
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Q2. ML, DL, Gen AI implementation and flow
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Q3. Python Coding logic and semantics
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Q1. What are the existing work challenges you face, and what solutions have you implemented to address them?
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Q2. What is your understanding of Generative AI and Natural Language Processing (NLP), and can you provide examples of their use cases?
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Q1. Questions around the previous work
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Q2. Questions around how re-rank work, and image segmentation,
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Q3. Questions with podman and docker
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AWS and Python, basic Machine learning questions

Python, project description, AWS, in build package

Basic Machine learning data science

Q1. Questions were asked related to ML modelling process
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Q2. Basic python coding
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Q1. What is Transformers and how they work
Ans. 
Transformers are models used in natural language processing tasks, known for their ability to handle long-range dependencies.
• Transformers use self-attention mechanism to weigh the importance of different words in a sentence.

• They consist of encoder and decoder layers, with each layer containing multi-head attention and feed-forward neural network.

• Examples of transformer models include BERT, GPT-3, and TransformerXL.
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Q2. How to train a model with imbalance data
Ans. 
Use techniques like oversampling, undersampling, SMOTE, or ensemble methods to train a model with imbalanced data.
• Use oversampling to increase the number of minority class samples.

• Use undersampling to decrease the number of majority class samples.

• Use Synthetic Minority Over-sampling Technique (SMOTE) to generate synthetic samples for the minority class.

• Utilize ensemble methods like Random Forest or Gradient Boosting to
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Write a code for Factorial series

Q1. Deep learning related
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Q2. Maximum GenAI Based question
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Test+2 interviews(technical and statistics)

Basic guestimates and case studies