Moody's Senior Business Intelligence Analyst Interview Questions and Answers

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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SQL knowledge is must.
Any visualisation tool is preferred.
Python basic required.

Q1. Concepts of SQL Optimisation techniques CTE
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Excel data analytics case

Q1. Data analytics, data visualisation tools
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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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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
Ans. 
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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Aptitude test consists of 40 questions.

Q1. Introduce yourself with ur projects
Ans. 
I am a data scientist with experience in developing predictive models and analyzing large datasets.
• Developed a predictive model for customer churn prediction using machine learning algorithms

• Analyzed sales data to identify key trends and patterns for business optimization

• Implemented natural language processing techniques for sentiment analysis of customer reviews
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Q2. Problem statement of a case that needs solutions.
Ans. 
Develop a predictive model to identify potential customers for a new product launch.
• Define the target variable and features to be used in the model

• Collect and preprocess relevant data for training the model

• Select an appropriate machine learning algorithm and train the model

• Evaluate the model's performance using metrics like accuracy, precision, and recall

• Use the model to predict potential customers for the new product
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