GeoIQ Senior Data Scientist Interview Questions and Answers

DSA question, probability, statistical test

Q1. Tell me about your projects?
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Q2. How do you approach the project if you are using logistic regression model?
Ans. 
Approach involves data preprocessing, model training, evaluation, and interpretation.
• Perform data preprocessing such as handling missing values, encoding categorical variables, and scaling features.

• Split the data into training and testing sets.

• Train the logistic regression model on the training data.

• Evaluate the model using metrics like accuracy, precision, recall, and F1 score.

• Interpret the model coefficients to under...read more
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Q1. What are you future goals?
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Q2. What would you do if your interested field doesnt have any work in the company?
Ans. 
I would seek opportunities to apply my skills in related fields within the company.
• Explore other departments or teams within the company that may have projects related to my field of interest

• Offer to collaborate with colleagues in different departments to bring a new perspective to their projects

• Seek out professional development opportunities to expand my skills and knowledge in related areas
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Q1. Basic Recommendation System Questions
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Q2. A B Testing Questions
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Assignment containing the python coding and the pandas data analysis and statistics questions

Q1. Given the tabular data and asked to find the issues in the data and how to solve them.
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Q2. Check if the sentence is palindrome or not.
Ans. 
Check if a sentence is a palindrome or not.
• Remove all non-alphanumeric characters and convert to lowercase.

• Compare the original sentence with its reverse to check for palindrome.

• Example: 'A man, a plan, a canal, Panama' is a palindrome sentence.
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Q1. Classical data science questions
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Q2. Given the large dataset and asked to find the answers using pandas
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Q1. Design a recommendation system
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Designing a recommendation system involves creating algorithms to suggest items to users based on their preferences and behavior.
• Collect user data such as past purchases, ratings, and browsing history

• Use collaborative filtering or content-based filtering techniques to make recommendations

• Implement machine learning models like matrix factorization or neural networks for personalized recommendations

• Evaluate the system's ...read more
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Q2. ML theoretical questions
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Q1. ML coding round
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Q2. Project discussion
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Questions related to basic coding were asked, and some background on projects and discussions alongside maths and statistics concepts

Q1. Questions related to projects done at my previous company
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Q1. Questions related to my work at previous company
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Q2. ML system design use case type of discussion
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