Nielsen Executive Data Scientist Interview Questions and Answers

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
Ans. 
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. Gen AI projects
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Q2. Architecture of projects I worked
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DSA and ML, AI, Coding question

Q1. Case study which was easy
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Q1. In depth questions on ML
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Difficult To solve in 30 minutes

Q1. Python libraries pandas sql
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SQL, Python coding …

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
Ans. 
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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There was aptitude qusns and video synthesis qusn

Q1. They showed a video and asked to write about my understanding
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Q2. Case study related qusns
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Q3. She asked me about javascript
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Q4. Verified my speaking capabilities
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Q5. Firstly asked about my intro
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