Kantar Data Scientist Interview Questions and Answers for Experienced

Q1. Explain the RAG pipeline?
Ans. 
RAG pipeline is a data processing pipeline used in data science to categorize data into Red, Amber, and Green based on certain criteria.
• RAG stands for Red, Amber, Green which are used to categorize data based on certain criteria

• Red category typically represents data that needs immediate attention or action

• Amber category represents data that requires monitoring or further investigation

• Green category represents data that...read more
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Q2. Explain Confusion metrics
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Confusion metrics are used to evaluate the performance of a classification model by comparing predicted values with actual values.
• Confusion matrix is a table that describes the performance of a classification model.

• It consists of four different metrics: True Positive, True Negative, False Positive, and False Negative.

• These metrics are used to calculate other evaluation metrics like accuracy, precision, recall, and F1 s...read more
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Q1. Explain any ML model.
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Random Forest is an ensemble learning method that builds multiple decision trees and merges them together to get a more accurate and stable prediction.
• Random Forest is a popular machine learning model used for classification and regression tasks.

• It works by creating multiple decision trees during training and outputs the mode of the classes for classification or the average prediction for regression.

• Random Forest is ro...read more
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Q2. Create Dataframe from two lists.
Ans. 
Creating a DataFrame from two lists in Python.
• Import the pandas library

• Create two lists of data

• Use pd.DataFrame() to create a DataFrame from the two lists
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Sql, python, Statistics mcq, Aptitude test. These were medium level questions.

Q1. SQL and python, time complexity
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Q2. Make 2 lists a=[1,2,3,4] b=[9,8,5,5,2,3,3,4,1,1,10,9,2,3,4,10,10,9,7,7,8] Write a program to remove duplicate of b and keep only those elements of b which are not present in a, and the final list should ...read more
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Remove duplicates from list b, keep elements not in list a, and sort in ascending order.
• Create a set from list b to remove duplicates

• Use list comprehension to keep elements not in list a

• Sort the final list in ascending order
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Q3. SQL question Remove duplicate from a table tab1
Ans. 
Use the DISTINCT keyword in SQL to remove duplicates from a table.
• Use the SELECT DISTINCT statement to retrieve unique rows from the table.

• Identify the columns that should be used to determine uniqueness.

• Example: SELECT DISTINCT column1, column2 FROM tab1;
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Given 2 case studies on data science and asked different possibilities to improve the models.

How to work with imbalance dataset.
How to remove null values, what is features engineering.
What is PCA
What is the working of XGBOOST

Q1. What was last project, tell me in detail. There were different technical questions related to my project
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Test consisted 7 sections which lasted for more than a hour. There were questions related to coding , sql , analytical questions etc.

1 python coding questions and 2 Sql question

2 case study question

Q1. This was the final round . Technical+HR .
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Q1. Questions related to python basics.
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Q2. Basic calculus to test math skills.
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Q3. Machine learning metrics.
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Q1. Policies, behavioral round.
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Q2. Work culture and the selection process.
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Q3. Discussion about previous employers and educational background.
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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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