Hexaware Technologies Data Science Intern Interview Questions and Answers

Q1. What is your goal in life
Ans. 
My goal in life is to make a positive impact on others and continuously learn and grow.
• To make a positive impact on others through my work and actions

• To continuously learn and grow personally and professionally

• To achieve a work-life balance that allows me to pursue my passions and interests
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1. There were two coding problems from leetcode (1 easy and 1 mediam).
2. One was machine learning problem having hingh weightage. They given a data and we have to make a linear regression model on data and submit prediction.

Q1. It was a technical interview . Mostly focussed on data science machine learning alogorithms. Also do prepare case studies.
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Q2. Explain Random forest. What is gini impurity.
Ans. 
Random forest is an ensemble learning method that constructs a multitude of decision trees and outputs the mode of the classes. Gini impurity is a measure of impurity or randomness used in decision trees.
• Random forest is a collection of decision trees that are trained on different subsets of the data.

• Each decision tree in the forest is trained on a random subset of the features.

• The final prediction is made by taking th...read more
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Q3. One coding question (best time to buy and sell stock leetcode easy version).
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Q4. One case study data science senario based case study.
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Asked the question about ml and basic python questions

Q1. 1. Basics of Python 2. Basics of ML like supervised and unsupervised learning
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Bulb and switch puzzle

Rope burning and length question

Q1. Why you want to join optum
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Basic DP, Array Questions

Q1. Resume Walkthrough and Discussion, Medium level coding questions
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Q1. Discussion with Manager
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Q1. Normal HR round
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Easy array questions.

Q1. Explain projects from resume.
Ans. 
Developed a machine learning model to predict customer churn for a telecom company.
• Used Python and scikit-learn to preprocess data and build the model

• Performed feature engineering to improve model performance

• Evaluated model using metrics like accuracy, precision, and recall
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Q2. Questions on image processing and computer vision.
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