Philips Data Scientist Interview Questions, Process, and Tips

Q1. Explain xgboost
Ans. 
XGBoost is a popular machine learning algorithm known for its speed and performance in gradient boosting.
• XGBoost stands for eXtreme Gradient Boosting.

• It is an implementation of gradient boosted decision trees designed for speed and performance.

• XGBoost is widely used in machine learning competitions and has become a popular choice for data scientists.

• It can handle missing data and is optimized for parallel processing.

• XG...read more
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Q2. Explain decision tree
Ans. 
Decision tree is a predictive modeling tool that uses a tree-like graph of decisions and their possible consequences.
• Decision tree is a supervised learning algorithm used for classification and regression tasks.

• It breaks down a dataset into smaller subsets based on different attributes.

• Each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome...read more
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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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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. 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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Topic was joining and calculating

Basic python questions

Q1. Interview questions were mostly based on basic SQL and python queries
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Q1. Question were asked on machine learning algorithms. There were few questions related to deep learning and image processing.Interviewer has asked me write live code on some moderate level DSA question.
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