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Top 10 AI Trends you canβt miss in 2025ππ» https://youtube.com/shorts/791s7lG_c1o?si=pzlOTDq_Tr9_4Ro7
Date: 21-06-2025
Company name: Tiger Analytics
Role: Data Scientist
01. How to fetch unique records from a table in SQL?
Answer- SQL DISTINCT clause is used to remove the duplicated columns from the result set.
The distinct keyword is used with the select keyword in conjunction. It is helpful when we avoid duplicate values present in the specific columns/tables. The unique values are fetched when we use the distinct keyword.
02. What is overfitting?
Answer- Overfitting is a concept in machine learning where a model performs extremely well on the training data but fails to generalize well to new, unseen data. In other words, the model "memorizes" the training examples rather than learning the underlying patterns or relationships. When a machine learning algorithm overfits, it fits the training data too closely and captures both the desired patterns and the random noise or outliers present in the data. As a result, the model becomes overly complex, overly specialized, and less capable of making accurate predictions on new, unseen data. Overfitting can occur in various types of machine learning models, such as decision trees, neural networks, support vector machines, and regression models. It is typically characterized by excessively low training error and significantly higher error rates on test or validation data.
03. What is regularization?
Answer- Regularization is a technique used to prevent overfitting by adding a penalty term to the model's loss function. This penalty term discourages the model from learning complex relationships that are not supported by the data.
04. You have a dataset with a single continuous variable, and you want to visualize its distribution. Would you choose a histogram or a box plot, and why?
Answer- In this scenario, I would choose a histogram to visualize the distribution of the continuous variable. A histogram provides information about the frequency distribution and the shape of the data. It shows the spread of values and allows us to identify any outliers or patterns. On the other hand, a box plot provides a summary of the distribution, including measures like the median, quartiles, and potential outliers, but it doesn't show the detailed distribution of the data.
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Company name: Tiger Analytics
Role: Data Scientist
01. How to fetch unique records from a table in SQL?
Answer- SQL DISTINCT clause is used to remove the duplicated columns from the result set.
The distinct keyword is used with the select keyword in conjunction. It is helpful when we avoid duplicate values present in the specific columns/tables. The unique values are fetched when we use the distinct keyword.
02. What is overfitting?
Answer- Overfitting is a concept in machine learning where a model performs extremely well on the training data but fails to generalize well to new, unseen data. In other words, the model "memorizes" the training examples rather than learning the underlying patterns or relationships. When a machine learning algorithm overfits, it fits the training data too closely and captures both the desired patterns and the random noise or outliers present in the data. As a result, the model becomes overly complex, overly specialized, and less capable of making accurate predictions on new, unseen data. Overfitting can occur in various types of machine learning models, such as decision trees, neural networks, support vector machines, and regression models. It is typically characterized by excessively low training error and significantly higher error rates on test or validation data.
03. What is regularization?
Answer- Regularization is a technique used to prevent overfitting by adding a penalty term to the model's loss function. This penalty term discourages the model from learning complex relationships that are not supported by the data.
04. You have a dataset with a single continuous variable, and you want to visualize its distribution. Would you choose a histogram or a box plot, and why?
Answer- In this scenario, I would choose a histogram to visualize the distribution of the continuous variable. A histogram provides information about the frequency distribution and the shape of the data. It shows the spread of values and allows us to identify any outliers or patterns. On the other hand, a box plot provides a summary of the distribution, including measures like the median, quartiles, and potential outliers, but it doesn't show the detailed distribution of the data.
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Date: 28-06-2025
Company name: MUsigma
Role: Data Scientist
01. Whatβs a Fourier transform?
Answer- A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. The Fourier transform finds the set of cycle speeds, amplitudes, and phases to match any time signal. A Fourier transform converts a signal from time to frequency domainβitβs a very common way to extract features from audio signals or other time series such as sensor data.
02. What in Excel is a macro?
Answer- An Excel macro is an algorithm or a group of steps that helps automate an operation by capturing and replaying the steps needed to finish it. Once the steps have been saved, you may construct a Macro that the user can alter and replay as often as they like.
03. Explain the intuition behind the Gradient Descent algorithm.
Answer- Gradient descent is an optimization algorithm thatβs used when training a machine learning model and is based on a convex function and tweaks its parameters iteratively to minimize a given function to its local minimum (that is, slope = 0).
For a start, we have to select a random bias and weights, and then iterate over the slope function to get a slope of 0.
The way we change update the value of the bias and weights is through a variable called the learning rate. We have to be wise on the learning rate because choosing:
A small leaning rate may lead to the model to take some time to learn
A large learning rate will make the model converge as our pointer will shoot and weβll not be able to get to minima.
04. What are some disadvantages of using Naive Bayes Algorithm?
Answer- Some disadvantages of using Naive Bayes Algorithm are:
It relies on a very big assumption that the independent variables are not related to each other.
It is generally not suitable for datasets with large numbers of numerical attributes.
It has been observed that if a rare case is not in the training dataset but is in the testing dataset, then it will most definitely be wrong.
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Company name: MUsigma
Role: Data Scientist
01. Whatβs a Fourier transform?
Answer- A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. The Fourier transform finds the set of cycle speeds, amplitudes, and phases to match any time signal. A Fourier transform converts a signal from time to frequency domainβitβs a very common way to extract features from audio signals or other time series such as sensor data.
02. What in Excel is a macro?
Answer- An Excel macro is an algorithm or a group of steps that helps automate an operation by capturing and replaying the steps needed to finish it. Once the steps have been saved, you may construct a Macro that the user can alter and replay as often as they like.
03. Explain the intuition behind the Gradient Descent algorithm.
Answer- Gradient descent is an optimization algorithm thatβs used when training a machine learning model and is based on a convex function and tweaks its parameters iteratively to minimize a given function to its local minimum (that is, slope = 0).
For a start, we have to select a random bias and weights, and then iterate over the slope function to get a slope of 0.
The way we change update the value of the bias and weights is through a variable called the learning rate. We have to be wise on the learning rate because choosing:
A small leaning rate may lead to the model to take some time to learn
A large learning rate will make the model converge as our pointer will shoot and weβll not be able to get to minima.
04. What are some disadvantages of using Naive Bayes Algorithm?
Answer- Some disadvantages of using Naive Bayes Algorithm are:
It relies on a very big assumption that the independent variables are not related to each other.
It is generally not suitable for datasets with large numbers of numerical attributes.
It has been observed that if a rare case is not in the training dataset but is in the testing dataset, then it will most definitely be wrong.
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β€1
Your dataset has 100,000 rows. You find 10% duplicate entries. What would you do first?
Anonymous Quiz
29%
1οΈβ£ Drop all duplicates immediately
51%
2οΈβ£ Check which columns are duplicated and why
16%
3οΈβ£ Replace duplicates with averages
4%
4οΈβ£ Ignore them β 10% isnβt a big deal
β€3
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Beco hiring Data Analytics Internship in Mumbai in Mumbai Metropolitan Region | LinkedIn
Posted 12:06:38 AM. Job DescriptionWe are looking for a motivated and detail-oriented Data Analyst Intern to join ourβ¦See this and similar jobs on LinkedIn.
β€5
Which of the following is the primary purpose of SQL in Data Analytics?
Anonymous Quiz
13%
To design and create data visualizations
18%
To clean and preprocess data
67%
To query and manage relational databases
2%
To implement machine learning models
β€1
Date: 19-07-2025
Company name: Blinkit
Role: Data Scientist
01. What are Superkey and candidate key?
Answer- A super key may be a single or a combination of keys that help to identify a record in a table. Know that Super keys can have one or more attributes, even though all the attributes are not necessary to identify the records.
A candidate key is the subset of Superkey, which can have one or more than one attributes to identify records in a table. Unlike Superkey, all the attributes of the candidate key must be helpful to identify the records.
02. What is Vectorization in Python pandas?
Answer- The procedure of executing operations on the full array is known as vectorization. This is intended to limit the number of iterations that the methods do. Pandas have a series of vectorized methods, such as string functions and aggregations, that are optimized for use with series and dataframes.
03. Define SQL Order by the statement?
Answer- The ORDER BY statement in SQL is used to sort the fetched data in either ascending or descending according to one or more columns.
By default ORDER BY sorts the data in ascending order.
We can use the keyword DESC to sort the data in descending order and the keyword ASC to sort in ascending order.
04. Explain some cases where k-Means clustering fails to give good results
Answer- k-means has trouble clustering data where clusters are of various sizes and densities.
Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.
If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.
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Company name: Blinkit
Role: Data Scientist
01. What are Superkey and candidate key?
Answer- A super key may be a single or a combination of keys that help to identify a record in a table. Know that Super keys can have one or more attributes, even though all the attributes are not necessary to identify the records.
A candidate key is the subset of Superkey, which can have one or more than one attributes to identify records in a table. Unlike Superkey, all the attributes of the candidate key must be helpful to identify the records.
02. What is Vectorization in Python pandas?
Answer- The procedure of executing operations on the full array is known as vectorization. This is intended to limit the number of iterations that the methods do. Pandas have a series of vectorized methods, such as string functions and aggregations, that are optimized for use with series and dataframes.
03. Define SQL Order by the statement?
Answer- The ORDER BY statement in SQL is used to sort the fetched data in either ascending or descending according to one or more columns.
By default ORDER BY sorts the data in ascending order.
We can use the keyword DESC to sort the data in descending order and the keyword ASC to sort in ascending order.
04. Explain some cases where k-Means clustering fails to give good results
Answer- k-means has trouble clustering data where clusters are of various sizes and densities.
Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.
If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.
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Apply now: https://www.linkedin.com/jobs/view/4279556493
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Exp: Fresher
Apply now: https://www.linkedin.com/jobs/view/4274698451
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Role: Analyst, Forecasting
Exp: 1 year
Apply now: https://www.linkedin.com/jobs/view/4125952134
Company: Eclectique F&B concepts
Role: Data Analyst
Exp: 1+ Years
Apply now: https://www.linkedin.com/jobs/view/4278480041
Company: Magna International
Role: Jr. Analyst
Exp: 1+ Years
Apply now: https://www.linkedin.com/jobs/view/4211740627
Company: Accenture Services Pvt Ltd
Role: Measurement and reporting associate
Exp: 1+ Years
Apply now: https://www.linkedin.com/jobs/view/4278444641
Company: Darwinbox
Role: Data Analyst
Exp: 2 Years
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Company: HackerRank
Role: BI and analytics engineer
Exp: 2+ years
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Company: Roblox
Role: Compensation Business Analyst
Exp: 2+ Years
Apply now: https://www.linkedin.com/jobs/view/4278459313
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Role: Data Analyst Intern
Exp: Fresher
Apply now: https://www.linkedin.com/jobs/view/4279568103
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Role: SQL Developer intern
Exp: Fresher
Apply now: https://www.linkedin.com/jobs/view/4279556493
Company: CryptoChakra
Role: Data Analyst Intern
Exp: Fresher
Apply now: https://www.linkedin.com/jobs/view/4274698451
Company: Concentrix
Role: Analyst, Forecasting
Exp: 1 year
Apply now: https://www.linkedin.com/jobs/view/4125952134
Company: Eclectique F&B concepts
Role: Data Analyst
Exp: 1+ Years
Apply now: https://www.linkedin.com/jobs/view/4278480041
Company: Magna International
Role: Jr. Analyst
Exp: 1+ Years
Apply now: https://www.linkedin.com/jobs/view/4211740627
Company: Accenture Services Pvt Ltd
Role: Measurement and reporting associate
Exp: 1+ Years
Apply now: https://www.linkedin.com/jobs/view/4278444641
Company: Darwinbox
Role: Data Analyst
Exp: 2 Years
Apply now: https://www.linkedin.com/jobs/view/4279591279
Company: HackerRank
Role: BI and analytics engineer
Exp: 2+ years
Apply now: https://www.linkedin.com/jobs/view/4251371904
Company: Roblox
Role: Compensation Business Analyst
Exp: 2+ Years
Apply now: https://www.linkedin.com/jobs/view/4278459313
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