Kantar Data Scientist Interview Questions and Answers

Q1. Explain any ML model.
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Q2. Create Dataframe from two lists.
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Along with coding round..there's a communication test at the end

Q1. There's ntg to ask about technical for me..
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Q2. Behavioural questions
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Basic aptitude , tech aptitude

Q1. What is overfitting
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Q2. How to handle missing values
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Given 6 coding qns related to java and html and also ML.

Q1. Explain abt projects and qns related to ML.
Ans. 
Projects in machine learning involve developing algorithms to analyze and interpret data for various applications.
• Developing a recommendation system for an e-commerce website

• Predicting customer churn for a telecommunications company

• Classifying images in a computer vision project

• Anomaly detection in network traffic for cybersecurity

• Natural language processing for sentiment analysis
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Q1. Basic hr qns why straive?
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Q1. Past projects of machine learning
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Developed a predictive model for customer churn using machine learning algorithms.
• Used Python and scikit-learn library for data preprocessing and model building

• Performed feature engineering to improve model performance

• Evaluated model performance using metrics like accuracy, precision, and recall
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Q2. Distributed computing and spark questions
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I was asked to write SQL queries for 3rd highest salary of the employee, some name filtering, group by tasks.
Python code to find the index of the maximum number without using numpy.

Q1. Explain the Project undertaken during the research and follow-up questions
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Q1. Write pandas query to separate the names as first and last name from the full name. Drop the duplicate columns and also the missing values. Write output for the Python code. Write SQL query to retrieve t...read more
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Answering questions related to data science concepts and techniques.
• Recall is the ratio of correctly predicted positive observations to the total actual positives. Precision is the ratio of correctly predicted positive observations to the total predicted positives.

• To reduce variance in an ensemble model, techniques like bagging, boosting, and stacking can be used. Bagging involves training multiple models on different ...read more
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