Navi Technologies Data Scientist 2 Interview Questions and Answers

Q1. SQL Joins and remove duplicates
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Q2. Function to create a transaction bin column
Ans. 
Create a function to generate a transaction bin column based on transaction amounts.
• Create bins based on transaction amounts (e.g. $0-$100, $101-$200, etc.)

• Use pandas cut() function in Python to create bins

• Assign bin labels to the transactions based on the bin ranges
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Q1. Implement lag() and lead() without using lag() or lead()
Ans. 
Implement lag() and lead() functions without using lag() or lead()
• To implement lag(), shift the values in the array by one position to the right

• To implement lead(), shift the values in the array by one position to the left

• For example, to implement lag() on [1, 2, 3, 4], you can shift the values to get [null, 1, 2, 3]

• For lead(), you can shift the values to get [2, 3, 4, null]
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Q2. How will you go about optimising a query?
Ans. 
Optimising a query involves identifying bottlenecks and improving efficiency.
• Identify slow-performing queries using tools like query execution plans.

• Optimize database indexes to speed up query performance.

• Limit the number of columns and rows returned in the query.

• Avoid using SELECT * and instead specify only the necessary columns.

• Use appropriate data types and avoid unnecessary data conversions.

• Consider denormalizing d...read more
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Q3. If the 3rd position in a string contains 'b' replace it with 'v'
Ans. 
Replace 'b' with 'v' in the 3rd position of each string in an array
• Iterate through each string in the array

• Check if the 3rd position contains 'b'

• If yes, replace 'b' with 'v'
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Q1. 1. How to handle model overfitting and model underfitting situations ? 2. In which situations should we standardize the data and where it is not required ? 3. How does Decision Trees work ? 4. How to selec...read more
Ans. 
Answering common questions related to data science concepts and techniques.
• To handle model overfitting, one can use techniques like cross-validation, regularization, and early stopping. For model underfitting, consider using more complex models or adding more features.

• Standardizing data is important for algorithms like K-Nearest Neighbors and Support Vector Machines. It is not required for tree-based models like Decisi...read more
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Q2. 1. If you already have a lot of features and you also have categorical column what strategy will you use to encode the categorical column so that the overall feature count should not increase or should not...read more
Ans. 
Use target encoding or frequency encoding to encode categorical columns without increasing feature count.
• Use target encoding: Encode categorical column with the mean of the target variable for each category.

• Use frequency encoding: Encode categorical column with the frequency of each category in the dataset.

• Both methods preserve the information of the categorical column without increasing feature count.
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Q1. 1. What are the assumptions of Linear Regression ? 2. What is the formula for Euclidean distance in K-Means ? 3. How does SVM work ? 4. How does SVM work on non linearly separable data ?
Ans. 
Answers to questions related to Linear Regression, K-Means, and SVM in data science.
• Assumptions of Linear Regression include linearity, independence, homoscedasticity, and normality of errors.

• Euclidean distance formula in K-Means is the square root of the sum of squared differences between two points.

• SVM works by finding the hyperplane that best separates the classes in the feature space.

• SVM on non-linearly separable d...read more
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Q2. What is the difference between Decision Trees and Random Forest. Why do we grow a lot of Decision Trees in a Random Forest ?
Ans. 
Decision Trees are single trees while Random Forest is a collection of trees. Random Forest grows multiple trees to improve accuracy and reduce overfitting.
• Decision Trees are individual trees that make decisions based on features of the data.

• Random Forest is an ensemble method that combines multiple Decision Trees to improve accuracy and reduce overfitting.

• Random Forest grows a lot of Decision Trees to increase diversi...read more
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Q1. SQL case study for data
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Q2. Find nth salary
Ans. 
To find the nth salary in a dataset
• Sort the dataset in descending order based on salary

• Remove duplicates if necessary

• Return the salary at index n-1
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Q1. Tell me about yourself and the current projects you are working on.
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Q1. Sql Questions on regex, json , window functions were there.
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Q1. Questions on current project, Tableau related question were there.
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It was a basic Aptitude test, Needed to learn Probability, Permutation , Cobination and SQL.
Total Duration 1 Hour.

Q1. Basic questions about resume
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Q1. Workplace adjustment questions
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Q2. Are you willing to work extra hours
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