Uber Senior Data Scientist Interview Questions and Answers

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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Q1. Explain past projects
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Q1. Explain resume points
Ans. 
Resume points are concise descriptions of your work experience, skills, and achievements listed on your resume.
• Resume points should be clear, specific, and quantifiable.

• Use action verbs to start each point, such as 'developed', 'implemented', 'analyzed'.

• Include relevant metrics or results to demonstrate impact, such as 'increased sales by 20%' or 'reduced processing time by 30%'.
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SQL - shared question on the screen and asked to code in SQL

Q1. Nth highes salary
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English, number system, grammar

Python , data science, machine learning

Python machine learning, natural language precossing

Q1. Python , Data Science, SQL
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Q2. What is python basics, libraries
Ans. 
Python basics include syntax, data types, and control structures. Libraries like NumPy, Pandas, and Matplotlib enhance data analysis and visualization.
• Python basics cover syntax, variables, data types, and control structures.

• NumPy is a library for numerical computing, providing powerful array operations.

• Pandas is a library for data manipulation and analysis, offering data structures like DataFrames.

• Matplotlib is a libr...read more
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Q3. Data science algorithms , and theory
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Q4. Sql querys, cluases
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Indian environment, village, college days

Q1. Python, data science
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Categories of the CAT exam include Quantitative Aptitude, Verbal Ability, Data Interpretation and Logical Reasoning, and Graphical questions.

Medium level. Focus on SQL subqueries application.

Q1. Statistical questions related to different hypothesis testing
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Q2. Questions related to different machine learning model
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2 Question and live coding was conducted on self platform

Q1. Taking about ML and statitics
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