Apar Technologies Data Science Intern Interview Questions and Answers

Q1. Basic questions in python
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Q2. Questions in Ml
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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.
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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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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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They gave a span of 3 days to build an AI-powered webapp

Q1. How would you go about learning a new skill
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Q2. Experience in cloud technologies
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I have experience working with cloud technologies such as AWS, Azure, and Google Cloud Platform.
• Experience in setting up and managing virtual machines, storage, and networking in cloud environments

• Knowledge of cloud services like EC2, S3, RDS, and Lambda

• Experience with cloud-based data processing and analytics tools like AWS Glue and Google BigQuery
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Q1. Tell me about yourself
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Q2. Project details and challenges faced in the project
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Developed a predictive model for customer churn in a telecom company
• Collected and cleaned customer data from various sources

• Performed exploratory data analysis to identify key factors influencing churn

• Built and fine-tuned machine learning models to predict customer churn

• Challenges included imbalanced data, feature engineering, and model interpretability
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Asked the question about ml and basic python questions

Q1. Questions on Python datatypes, class, object, inheritance,
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Q2. Machine learning algorithms
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Machine learning algorithms are used to train models on data to make predictions or decisions.
• Supervised learning algorithms include linear regression, decision trees, and neural networks.

• Unsupervised learning algorithms include clustering and dimensionality reduction.

• Reinforcement learning algorithms involve learning through trial and error.

• Examples of machine learning applications include image recognition, natural l
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Q3. Sql queries and python coding questions
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Q1. Related to project
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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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