Samsung Data Scientist Interview Questions and Answers

Q1. Exptected salary
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Q2. What do you expect for work
Ans. 
I expect challenging projects, opportunities for growth, collaborative team environment, and work-life balance.
• Challenging projects that allow me to apply my data science skills and learn new techniques

• Opportunities for growth and advancement within the company

• Collaborative team environment where I can share ideas and work together towards common goals

• Work-life balance to ensure I can perform at my best both profession
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Basic python questions

Q1. Data science basics fundamental questions
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Q1. Data science project based
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Q1. Behavioural project based and technical
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Q1. Motivation, behavioral and informative
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I was asked to solve various problems (your typical algorithm and data structure subjects), as well as explain the various projects I worked on in my most recent position.

Divide candidates in a group of around 15 people, and put you through different activities such as role play exercises to measure your communication and team working skills.

Q1. Questions related to Python , ML, Deep Learning and resume based questions
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Q1. Project related
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Q1. Usual HR questions
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Q1. 1. diff between acf pacf
Ans. 
ACF measures the linear relationship between an observation and its lagged values. PACF measures the direct relationship.
• ACF (Autocorrelation Function) measures the correlation between an observation and its lagged values.

• PACF (Partial Autocorrelation Function) measures the correlation between an observation and its lagged values, while removing the indirect effects of intermediate lags.

• ACF is used to identify the orde...read more
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Q1. Describe a decision tree
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A decision tree is a flowchart-like model that shows the possible outcomes of a decision based on certain conditions.
• It is a tree-like structure with nodes representing decisions and branches representing outcomes

• Each node has a condition that determines which branch to follow

• It is commonly used in machine learning for classification and regression tasks

• Example: A decision tree for predicting whether a customer will bu...read more
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Q2. What is random forest
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Random forest is an ensemble learning method for classification, regression and other tasks.
• Random forest builds multiple decision trees and combines their outputs to improve accuracy.

• It is a popular machine learning algorithm due to its high accuracy and ability to handle large datasets.

• Random forest can be used for both classification and regression tasks.

• It is resistant to overfitting and can handle missing data.

• Exa...read more
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Q3. What is neural network
Ans. 
Neural network is a type of machine learning algorithm inspired by the structure and function of the human brain.
• Consists of layers of interconnected nodes that process information

• Used for tasks such as image recognition, natural language processing, and prediction

• Can be trained using supervised, unsupervised, or reinforcement learning

• Examples include convolutional neural networks, recurrent neural networks, and deep n
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Leetcode style coding