Any wear Data Analyst Interview Questions and Answers

Artificial intelligence

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Q1. Basics of spark Discussion on case study approach SQL question on windows function
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Q1. SQL basics and coding questions
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Q1. Data Science mathematical concepts
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Q1. Why do you want to join
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Q1. Why our firm ?
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Q2. Why in office , why not work from home ?
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1 hour test. The test consists of 3 sections. First section is aptitude and second section is verbal and the third section is logical thinking.

Speak about a topic. 5 minutes of time will be given.. After 5 minutes start speaking about the given topic.

Q1. Why should last job in left
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Q1. Difference between Tuple and Dictionary
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Tuple is an ordered collection of elements, while a dictionary is an unordered collection of key-value pairs.
• Tuples are immutable, while dictionaries are mutable.

• Tuples are accessed using indexing, while dictionaries are accessed using keys.

• Tuples are typically used to store related pieces of data, while dictionaries are used for mapping and lookups.
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Q2. What is Adjusted R square
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Adjusted R square is a statistical measure that represents the proportion of variance in the dependent variable accounted for by the independent variables.
• Adjusted R square is an extension of R square, which measures the goodness of fit of a regression model.

• It takes into account the number of predictors in the model and adjusts for the degrees of freedom.

• Adjusted R square ranges from 0 to 1, where a higher value indic...read more
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Q3. Which is better Random forest or XG Boost
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Both Random Forest and XG Boost are powerful machine learning algorithms, but their performance depends on the specific problem and data.
• Random Forest is an ensemble learning method that combines multiple decision trees to make predictions.

• It is known for its ability to handle high-dimensional data and maintain good performance even with noisy or missing data.

• XG Boost, on the other hand, is a gradient boosting algorith...read more
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Q4. What is Max pooling
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Max pooling is a pooling operation that selects the maximum value from a region of the input data.
• Max pooling is commonly used in convolutional neural networks (CNNs) for feature extraction.

• It reduces the spatial dimensions of the input data while retaining the most important features.

• Max pooling helps in achieving translation invariance, making the model more robust to variations in input position.

• For example, in a 2x...read more
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Q1. Questions on cloud computing
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