Amantya Technologies Data Scientist Interview Questions and Answers

Q1. Proficiency in cloud technology
Ans. 
Proficiency in cloud technology is essential for data scientists to efficiently store, manage, and analyze large datasets.
• Experience with cloud platforms like AWS, Azure, or Google Cloud

• Knowledge of cloud storage solutions like S3, Blob Storage, or Cloud Storage

• Understanding of cloud computing concepts like virtual machines, containers, and serverless computing

• Ability to work with big data technologies like Hadoop, Spa
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Q1. What is r squared value
Ans. 
R-squared value is a statistical measure that represents the proportion of the variance in the dependent variable that is predictable from the independent variable(s).
• R-squared value ranges from 0 to 1, with 1 indicating a perfect fit.

• It is used to evaluate the goodness of fit of a regression model.

• A higher R-squared value indicates that the model explains a larger proportion of the variance in the dependent variable.

• F...read more
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Q1. Proficiency in cloud technology
Ans. 
Proficiency in cloud technology is essential for data scientists to efficiently store, manage, and analyze large datasets.
• Experience with cloud platforms like AWS, Azure, or Google Cloud

• Knowledge of cloud storage solutions like S3, Blob Storage, or Cloud Storage

• Understanding of cloud computing concepts like virtual machines, containers, and serverless computing

• Ability to work with big data technologies like Hadoop, Spa
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Q1. Machine learning
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Python , pandas, sql

Test had a mix of questions on Statistics, Probability, Machine Learning, SQL and Python.

Q1. How to retain special characters (that pandas discards by default) in the data while reading it?
Ans. 
To retain special characters in pandas data, use encoding parameter while reading the data.
• Use encoding parameter while reading the data in pandas

• Specify the encoding type of the data file

• Example: pd.read_csv('filename.csv', encoding='utf-8')
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Q2. How to read large .csv files in pandas quickly?
Ans. 
Use pandas' read_csv() method with appropriate parameters to read large .csv files quickly.
• Use the chunksize parameter to read the file in smaller chunks

• Use the low_memory parameter to optimize memory usage

• Use the dtype parameter to specify data types for columns

• Use the usecols parameter to read only necessary columns

• Use the skiprows parameter to skip unnecessary rows

• Use the nrows parameter to read only a specific numb...read more
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Q3. How do perform the manipulations quicker in pandas?
Ans. 
Use vectorized operations, avoid loops, and optimize memory usage.
• Use vectorized operations like apply(), map(), and applymap() instead of loops.

• Avoid using iterrows() and itertuples() as they are slower than vectorized operations.

• Optimize memory usage by using appropriate data types and dropping unnecessary columns.

• Use inplace=True parameter to modify the DataFrame in place instead of creating a copy.

• Use the pd.eval()...read more
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Q4. Explain generators and decorators in python
Ans. 
Generators are functions that allow you to iterate over a sequence of values without creating the entire sequence in memory. Decorators are functions that modify the behavior of other functions.
• Generators use the yield keyword to return values one at a time

• Generators are memory efficient and can handle large datasets

• Decorators are functions that take another function as input and return a modified version of that funct...read more
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Q5. You have a pandas dataframe with three columns, filled with state names, city names and arbitrary numbers respectively. How to retrieve top 2 cities per state. (top according to the max number in the third...read more
Ans. 
Retrieve top 2 cities per state based on max number in third column of pandas dataframe.
• Group the dataframe by state column

• Sort each group by the third column in descending order

• Retrieve the top 2 rows of each group using head(2) function

• Concatenate the resulting dataframes using pd.concat() function
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Q6. How does look up happens in a list when you do my_list[5]?
Ans. 
my_list[5] retrieves the 6th element of the list.
• Indexing starts from 0 in Python.

• The integer inside the square brackets is the index of the element to retrieve.

• If the index is out of range, an IndexError is raised.
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Q7. How to create dictionaries in python with repeated keys?
Ans. 
To create dictionaries in Python with repeated keys, use defaultdict from the collections module.
• Import the collections module

• Create a defaultdict object

• Add key-value pairs to the dictionary using the same key multiple times

• Access the values using the key

• Example: from collections import defaultdict; d = defaultdict(list); d['key'].append('value1'); d['key'].append('value2')
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Q8. What is the purpose of lambda function when regural functions(of def) exist? how are they different?
Ans. 
Lambda functions are anonymous functions used for short and simple operations. They are different from regular functions in their syntax and usage.
• Lambda functions are defined without a name and keyword 'lambda' is used to define them.

• They can take any number of arguments but can only have one expression.

• They are commonly used in functional programming and as arguments to higher-order functions.

• Lambda functions are oft...read more
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Q9. Merge vs join in pandas
Ans. 
Merge and join are used to combine dataframes in pandas.
• Merge is used to combine dataframes based on a common column or index.

• Join is used to combine dataframes based on their index.

• Merge can handle different column names, while join cannot.

• Merge can handle different types of joins (inner, outer, left, right), while join only does inner join by default.
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Q10. How will the resultant table be, when you "merge" two tables that match at a column. and the second table has many of keys repeated.
Ans. 
The resultant table will have all the columns from both tables and the rows will be a combination of matching rows.
• The resultant table will have all the columns from both tables

• The rows in the resultant table will be a combination of matching rows

• If the second table has repeated keys, there will be multiple rows with the same key in the resultant table
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Q11. Some questions on spacy and NLP models and my project.
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Q1. Explain eign vectors and eign values? what purpose do they serve in ML?
Ans. 
Eigenvalues and eigenvectors are linear algebra concepts used in machine learning for dimensionality reduction and feature extraction.
• Eigenvalues represent the scaling factor of the eigenvectors.

• Eigenvectors are the directions along which a linear transformation acts by stretching or compressing.

• In machine learning, eigenvectors are used for principal component analysis (PCA) to reduce the dimensionality of data.

• Eigenv...read more
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Q2. Explain PCA briefly? what can it be used for and what can it not be used for?
Ans. 
PCA is a dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional space.
• PCA can be used for feature extraction, data visualization, and noise reduction.

• PCA cannot be used for causal inference or to handle missing data.

• PCA assumes linear relationships between variables and may not work well with non-linear data.

• PCA can be applied to various fields such as finance, image process
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Q3. What is VIF and how is it calculated?
Ans. 
VIF stands for Variance Inflation Factor, a measure of multicollinearity in regression analysis.
• VIF is calculated for each predictor variable in a regression model.

• It measures how much the variance of the estimated regression coefficient is increased due to multicollinearity.

• A VIF of 1 indicates no multicollinearity, while a VIF greater than 1 indicates increasing levels of multicollinearity.

• VIF is calculated as 1 / (1...read more
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Q4. What is AIC & BIC in linear regression?
Ans. 
AIC & BIC are statistical measures used to evaluate the goodness of fit of a linear regression model.
• AIC stands for Akaike Information Criterion and BIC stands for Bayesian Information Criterion.

• Both AIC and BIC are used to compare different models and select the best one.

• AIC penalizes complex models less severely than BIC.

• Lower AIC/BIC values indicate a better fit of the model to the data.

• AIC and BIC can be calculated...read more
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Q5. Do we minimize or maximize the loss in logistic regression?
Ans. 
We minimize the loss in logistic regression.
• The goal of logistic regression is to minimize the loss function.

• The loss function measures the difference between predicted and actual values.

• The optimization algorithm tries to find the values of coefficients that minimize the loss function.

• Minimizing the loss function leads to better model performance.

• Examples of loss functions used in logistic regression are cross-entropy
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Q6. How does one vs rest work for logistic regression?
Ans. 
One vs Rest is a technique used to extend binary classification to multi-class problems in logistic regression.
• It involves training multiple binary classifiers, one for each class.

• In each classifier, one class is treated as the positive class and the rest as negative.

• The class with the highest probability is predicted as the final output.

• It is also known as one vs all or one vs others.

• Example: In a 3-class problem, we ...read more
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Q7. What is one vs one classification?
Ans. 
One vs one classification is a binary classification method where multiple models are trained to classify each pair of classes.
• It is used when there are more than two classes in the dataset.

• It involves training multiple binary classifiers for each pair of classes.

• The final prediction is made by combining the results of all the binary classifiers.

• Example: In a dataset with 5 classes, 10 binary classifiers will be traine
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Q8. How to find the number of white cars in a city? (interviewer wanted my approach and had given me 5 minutes to come up with an apporach)
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Q1. Machine Learning, Python, SQL, Basic Stats The difficulty level of questions was average.
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Q1. Resume and project related. One or two questions around probability and stats.
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Q1. Questions based on my work experience, basic level of python coding (return counts for duplicate integers in a list), theory questions on basic DL (activation layers, time series)
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Q1. What is r squared value
Ans. 
R-squared value is a statistical measure that represents the proportion of the variance in the dependent variable that is predictable from the independent variable(s).
• R-squared value ranges from 0 to 1, with 1 indicating a perfect fit.

• It is used to evaluate the goodness of fit of a regression model.

• A higher R-squared value indicates that the model explains a larger proportion of the variance in the dependent variable.

• F...read more
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