Today's Interview QnAs
Company Name - Accenture
Role: Data Scientist
Q. Why do we use Cross validation?
A. Cross-validation is a technique used in applied machine learning to estimate a machine learning model's skill on unknown data. That is, to use a small sample to assess how the model will perform in general when used to generate predictions on data that was not utilised during the model's training.
Q. How will you work in a machine learning project if there is a huge imbalance in the data?
A. Machine Learning algorithms tend to produce unsatisfactory classifiers when faced with imbalanced datasets. To overcome this, methods like randome oversampling and random undersampling can be used. Synthetic Minority Over-sampling Technique, Modified synthetic minority oversampling technique (MSMOTE), Algorithmic Ensemble Techniques can also be used.
Q. Formula of sigmoid function?
A. A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve. Sigmoid functions most often show a return value (y axis) in the range 0 to 1. A common example of a sigmoid function is the logistic function shown in the first figure and defined by the formula
S(x) = 1/(1+exp(-x))
Q. Can we use sigmoid function in case of multiple classification?
A. You must use Softmax if your task is a type of classification where the labels are mutually exclusive and each input only has one label. If you have numerous labels for each input in your classification work, your classes are not mutually exclusive, and you can utilise Sigmoid for each output. In the first scenario, the output should be the output entry with the highest value. In the latter scenario, you have an activation value for each class that belongs to the last sigmoid. You can assert that an entry exists in the input if each activation is greater than 0.5.
..................
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Company Name - Accenture
Role: Data Scientist
Q. Why do we use Cross validation?
A. Cross-validation is a technique used in applied machine learning to estimate a machine learning model's skill on unknown data. That is, to use a small sample to assess how the model will perform in general when used to generate predictions on data that was not utilised during the model's training.
Q. How will you work in a machine learning project if there is a huge imbalance in the data?
A. Machine Learning algorithms tend to produce unsatisfactory classifiers when faced with imbalanced datasets. To overcome this, methods like randome oversampling and random undersampling can be used. Synthetic Minority Over-sampling Technique, Modified synthetic minority oversampling technique (MSMOTE), Algorithmic Ensemble Techniques can also be used.
Q. Formula of sigmoid function?
A. A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve. Sigmoid functions most often show a return value (y axis) in the range 0 to 1. A common example of a sigmoid function is the logistic function shown in the first figure and defined by the formula
S(x) = 1/(1+exp(-x))
Q. Can we use sigmoid function in case of multiple classification?
A. You must use Softmax if your task is a type of classification where the labels are mutually exclusive and each input only has one label. If you have numerous labels for each input in your classification work, your classes are not mutually exclusive, and you can utilise Sigmoid for each output. In the first scenario, the output should be the output entry with the highest value. In the latter scenario, you have an activation value for each class that belongs to the last sigmoid. You can assert that an entry exists in the input if each activation is greater than 0.5.
..................
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Today's Interview QnAs
Company Name - Mindtree
Role: Data Scientist
Q. What is central tendency ?
A. The central tendency is a descriptive description of a dataset represented by a single value that represents the data distribution's center. It does not provide information on individual values in the dataset, but it does provide a thorough overview of the entire dataset. The following measurements can be used to describe the central tendency of a dataset in general: Mean: Represents the sum of all values in a dataset divided by total number of values in the dataset. The median is the midpoint value in an ascending-ordered dataset. The most often occurring value in a dataset is defined by the mode.
Q. Which central tendency method is used If there exists any outliers ?
A. Because the value of the mean might be skewed by outliers, the median is frequently recommended in these instances. It will, however, depend on how powerful the outliers are. Using the mean as the measure of central tendency is usually preferred if they do not significantly alter the mean.
Q. Explain Central limit theorem ?
A. The central limit theorem (CLT) asserts that as the sample size grows higher, the distribution of a sample variable approaches a normal distribution (i.e., a "bell curve"), assuming that all samples are identical in size and independent of the population's actual distribution shape.
Q. What is Chi-Square test ?
A. A hypothesis testing method is the Chi-square test. Checking if observed frequencies in one or more categories match expected frequencies is one of two frequent Chi-square tests. The Chi-square test is used to determine whether your data is as expected. The test's core premise is to compare the observed values in your data to the expected values that you would see if the null hypothesis is true. The Chi-square goodness of fit test and the Chi-square test of independence are two regularly used Chi-square tests. Both tests use variables to categorize your data into groups.
..................
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Company Name - Mindtree
Role: Data Scientist
Q. What is central tendency ?
A. The central tendency is a descriptive description of a dataset represented by a single value that represents the data distribution's center. It does not provide information on individual values in the dataset, but it does provide a thorough overview of the entire dataset. The following measurements can be used to describe the central tendency of a dataset in general: Mean: Represents the sum of all values in a dataset divided by total number of values in the dataset. The median is the midpoint value in an ascending-ordered dataset. The most often occurring value in a dataset is defined by the mode.
Q. Which central tendency method is used If there exists any outliers ?
A. Because the value of the mean might be skewed by outliers, the median is frequently recommended in these instances. It will, however, depend on how powerful the outliers are. Using the mean as the measure of central tendency is usually preferred if they do not significantly alter the mean.
Q. Explain Central limit theorem ?
A. The central limit theorem (CLT) asserts that as the sample size grows higher, the distribution of a sample variable approaches a normal distribution (i.e., a "bell curve"), assuming that all samples are identical in size and independent of the population's actual distribution shape.
Q. What is Chi-Square test ?
A. A hypothesis testing method is the Chi-square test. Checking if observed frequencies in one or more categories match expected frequencies is one of two frequent Chi-square tests. The Chi-square test is used to determine whether your data is as expected. The test's core premise is to compare the observed values in your data to the expected values that you would see if the null hypothesis is true. The Chi-square goodness of fit test and the Chi-square test of independence are two regularly used Chi-square tests. Both tests use variables to categorize your data into groups.
..................
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Today's Interview QnAs
Company Name - Cognizant
Role: Data Scientist
Q. When underfitting occurs in a static model?
A. When a mathematical model or a machine learning algorithm fails to capture the primary trend of the data, this is known as underfitting. When a model is too simplistic — informed by too few features or overly regularised — it becomes inflexible in learning from the dataset, resulting in underfitting.
Q. While working on a data set,how can you select important variables?
A. 1. Variance: You can remove characteristics with low variance because they don't provide any insight into the forecast and overfit the model. 2. Correlated Features: There may be features with significant correlations among them; we can keep a few and discard the rest. 3. From the Model: This approach of feature selection takes longer but provides us with the most important features supplied by the models themselves. In this case, we can employ 2-3 models and place the train set on top of them, instructing the model to output a certain amount of relevant functions while ignoring the rest. In the Sklearn RFE and RFECV feature selection modules, there are two functions that we can use to pick features.
Q. What is Autoencoder?
A. Autoencoder is an unsupervised artificial neural network that learns how to compress and encode data effectively before reconstructing it back to a representation that is as similar to the original input as feasible. By definition, an autoencoder decreases data dimensionality by learning to ignore noise in the data. Encoder, Bottleneck, Decoder, and Reconstruction Loss: Autoencoders are made up of four primary parts: encoder, bottleneck, decoder, and reconstruction loss.
Q. Difference between violin plot and box plot?
A. Although violin plots are similar to box plots, they provide additional information such as the sample data distribution (density trace). Box plots show data points outside 1.5 times the inter-quartile range as outliers above or below the whiskers by default, whereas violin plots show the entire range of the data.
Q. What is statistical power of sensitivity and how do you calculate it?
A. The statistical power of an A/B test refers to the test's sensitivity to certain magnitudes of effect sizes. More precisely, it is the probability of observing a statistically significant result at level alpha (α) if a true effect of a certain magnitude (MEI) is in fact present.
..................
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Company Name - Cognizant
Role: Data Scientist
Q. When underfitting occurs in a static model?
A. When a mathematical model or a machine learning algorithm fails to capture the primary trend of the data, this is known as underfitting. When a model is too simplistic — informed by too few features or overly regularised — it becomes inflexible in learning from the dataset, resulting in underfitting.
Q. While working on a data set,how can you select important variables?
A. 1. Variance: You can remove characteristics with low variance because they don't provide any insight into the forecast and overfit the model. 2. Correlated Features: There may be features with significant correlations among them; we can keep a few and discard the rest. 3. From the Model: This approach of feature selection takes longer but provides us with the most important features supplied by the models themselves. In this case, we can employ 2-3 models and place the train set on top of them, instructing the model to output a certain amount of relevant functions while ignoring the rest. In the Sklearn RFE and RFECV feature selection modules, there are two functions that we can use to pick features.
Q. What is Autoencoder?
A. Autoencoder is an unsupervised artificial neural network that learns how to compress and encode data effectively before reconstructing it back to a representation that is as similar to the original input as feasible. By definition, an autoencoder decreases data dimensionality by learning to ignore noise in the data. Encoder, Bottleneck, Decoder, and Reconstruction Loss: Autoencoders are made up of four primary parts: encoder, bottleneck, decoder, and reconstruction loss.
Q. Difference between violin plot and box plot?
A. Although violin plots are similar to box plots, they provide additional information such as the sample data distribution (density trace). Box plots show data points outside 1.5 times the inter-quartile range as outliers above or below the whiskers by default, whereas violin plots show the entire range of the data.
Q. What is statistical power of sensitivity and how do you calculate it?
A. The statistical power of an A/B test refers to the test's sensitivity to certain magnitudes of effect sizes. More precisely, it is the probability of observing a statistically significant result at level alpha (α) if a true effect of a certain magnitude (MEI) is in fact present.
..................
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Anonymous Quiz
4%
10 % 2
13%
10 * 2
17%
10 ** 10
66%
10 ** 2
Job Openings
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Comp - Global Talent Track Private Limited
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Anonymous Quiz
10%
modulus
79%
comment
5%
variable
6%
Boolean
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#quiz
To solve a problem that requires the user to enter 10 numbers would use what type of iteration ?
To solve a problem that requires the user to enter 10 numbers would use what type of iteration ?
Anonymous Quiz
26%
While loop
61%
For loop
9%
Variable
4%
Selection
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Today's Interview QnAs
Company Name - Fractal Analytics
Role- Data Scientist
Q. What is residual error?
A. The difference between a group of values observed and their arithmetical mean. The difference between what was expected and what was predicted is called the residual error.
Q. Select an appropriate value of k in k-means?
A. The elbow approach is a popular way for determining the ideal value of K when using the K-Means Clustering Algorithm. The essential concept behind this method is that it plots various cost values as k changes. There will be fewer elements in the cluster as the value of K grows.
Q. How do you deal missing values in datasets?
A. Deleting Rows with missing values, Impute missing values for continuous variable, Impute missing values for categorical variable, Using Algorithms that support missing values, Prediction of missing values, Imputation using Deep Learning Library — Datawig, Imputation using Machine Learning libraries like SimpleImputer, KNNImputer,etc.
Q. What is a confusion matrix?
A. A confusion matrix is a method of summarising a classification algorithm's performance. Calculating a confusion matrix can help you understand what your classification model is getting right and where it is going wrong. It gives us: “true positive” for correctly predicted event values, “false positive” for incorrectly predicted event values, “true negative” for correctly predicted no-event values, “false negative” for incorrectly predicted no-event values.
..................
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Company Name - Fractal Analytics
Role- Data Scientist
Q. What is residual error?
A. The difference between a group of values observed and their arithmetical mean. The difference between what was expected and what was predicted is called the residual error.
Q. Select an appropriate value of k in k-means?
A. The elbow approach is a popular way for determining the ideal value of K when using the K-Means Clustering Algorithm. The essential concept behind this method is that it plots various cost values as k changes. There will be fewer elements in the cluster as the value of K grows.
Q. How do you deal missing values in datasets?
A. Deleting Rows with missing values, Impute missing values for continuous variable, Impute missing values for categorical variable, Using Algorithms that support missing values, Prediction of missing values, Imputation using Deep Learning Library — Datawig, Imputation using Machine Learning libraries like SimpleImputer, KNNImputer,etc.
Q. What is a confusion matrix?
A. A confusion matrix is a method of summarising a classification algorithm's performance. Calculating a confusion matrix can help you understand what your classification model is getting right and where it is going wrong. It gives us: “true positive” for correctly predicted event values, “false positive” for incorrectly predicted event values, “true negative” for correctly predicted no-event values, “false negative” for incorrectly predicted no-event values.
..................
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Check our blog which contains Data Science Interview Questions collection with companies name that you can go for.
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Data Science Interviews with questions at a glance
We are sharing Data Science Interviews with expected questions to enhance the chance of cracking the interview. It will also enhance confidence on this area.
Today's Interview QnAs
Company Name - Accenture
Role: Data Scientist
Q. What is skewed Distribution & uniform distribution?
A. The term "uniform distribution" refers to a situation in which all of the observations in a dataset are evenly distributed over the distribution range. When one side of a graph has more dataset than the other, the circumstance is known as skewed distribution. A uniform distribution, as opposed to a skewed distribution, contains relatively consistent data, with the frequency of each class being similar to the others.
Q. Find the positions of numbers that are multiples of 4 from a series?
A. import pandas as pd
import numpy as np
num_series = pd.Series(np.random.randint(1, 10, 9))
result = np.argwhere(num_series % 4==0)
print(result)
Q. Solve curse of dimensionality?
A. The curse of dimensionality states that as the number of characteristics grows, the error grows as well. It refers to the fact that high-dimensional algorithms are more difficult to build and often have a running duration that is proportional to the dimensions. Dimensionality reduction is a technique for transforming high-dimensional variables into lower-dimensional variables while preserving the variables' particular information. The curse of dimensionality is caused by unstable parameter estimates; thus, regularising these estimates will assist the parameters in making accurate estimations. One of the most common tools for dimension reduction is PCA (Principal Component Analysis).
Q. What are lambda functions? Give example?
A. In Python programming, an anonymous function or a function with no name is known as a Lambda Function. It's a simple and limited function with only one line of code. A Lambda function, like any other function, can have numerous arguments in a single statement. Lambda expressions (or lambda forms) are used to create anonymous functions in Python. You'll achieve this by using the lambda keyword (just as you use def to define normal functions).
Example:
adder = lambda x, y: x + y
print (adder (1, 2))
Q. Get third highest scored student in class table sql?
A. SELECT * FROM
Q. Difference between type I and type II errors?
A. In statistics, a Type I error is a false positive conclusion, while a Type II error is a false negative conclusion. Type I vs. Type II Error as an Example Based on your modest symptoms, you decide to get tested for COVID-19. There are two types of errors that could occur: Type I error (false positive): the test result indicates that you have coronavirus when you don't. Type II error (false negative): the test result indicates that you are free of coronavirus, while you are in fact infected.
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Company Name - Accenture
Role: Data Scientist
Q. What is skewed Distribution & uniform distribution?
A. The term "uniform distribution" refers to a situation in which all of the observations in a dataset are evenly distributed over the distribution range. When one side of a graph has more dataset than the other, the circumstance is known as skewed distribution. A uniform distribution, as opposed to a skewed distribution, contains relatively consistent data, with the frequency of each class being similar to the others.
Q. Find the positions of numbers that are multiples of 4 from a series?
A. import pandas as pd
import numpy as np
num_series = pd.Series(np.random.randint(1, 10, 9))
result = np.argwhere(num_series % 4==0)
print(result)
Q. Solve curse of dimensionality?
A. The curse of dimensionality states that as the number of characteristics grows, the error grows as well. It refers to the fact that high-dimensional algorithms are more difficult to build and often have a running duration that is proportional to the dimensions. Dimensionality reduction is a technique for transforming high-dimensional variables into lower-dimensional variables while preserving the variables' particular information. The curse of dimensionality is caused by unstable parameter estimates; thus, regularising these estimates will assist the parameters in making accurate estimations. One of the most common tools for dimension reduction is PCA (Principal Component Analysis).
Q. What are lambda functions? Give example?
A. In Python programming, an anonymous function or a function with no name is known as a Lambda Function. It's a simple and limited function with only one line of code. A Lambda function, like any other function, can have numerous arguments in a single statement. Lambda expressions (or lambda forms) are used to create anonymous functions in Python. You'll achieve this by using the lambda keyword (just as you use def to define normal functions).
Example:
adder = lambda x, y: x + y
print (adder (1, 2))
Q. Get third highest scored student in class table sql?
A. SELECT * FROM
student_table ORDER BY marks DESC LIMIT 2,1Q. Difference between type I and type II errors?
A. In statistics, a Type I error is a false positive conclusion, while a Type II error is a false negative conclusion. Type I vs. Type II Error as an Example Based on your modest symptoms, you decide to get tested for COVID-19. There are two types of errors that could occur: Type I error (false positive): the test result indicates that you have coronavirus when you don't. Type II error (false negative): the test result indicates that you are free of coronavirus, while you are in fact infected.
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#Quiz #FillTheBlank
A confusion matrix is used for classification tasks where the output of the algorithm is in ……... class/classes
A confusion matrix is used for classification tasks where the output of the algorithm is in ……... class/classes
Anonymous Quiz
11%
one
31%
More than 3
28%
Two
31%
2 or more
Today's Interview QnAs
Company Name - Ford
Role: Data Scientist
Q. What is the the difference between normal distribution, standard normal distribution and uniform distribution?
A. A uniform distribution is one in which all values are equally likely within a range (and impossible beyond that range). A Normal distribution is one in which values cluster around the mean, or average, a outlying values are very unlikely. The standard normal distribution, also called the z-distribution, is a special normal distribution where the mean is 0 and the standard deviation is 1.
Q. Which should be preferred among Gini impurity and Entropy?
A. When using sklearn, the Gini impurity is a good default because it is slightly faster to compute. When they work in opposite directions, Gini impurity isolates the most common class in its own branch of the Tree, whereas entropy produces slightly more balanced Trees.
Q. Explain the time and space complexity of training and testing in the case of a Decision Tree.
A. In the training stage for features (dimensions) in the dataset, we sort the data which takes O(n log n) time following which we traverse the data points to find the right threshold which takes O(n) time. Subsequently, for d dimensions, the total time complexity would be: O(nlogn*d). Moreover, the testing time complexity is O(depth) as we have to traverse from the root to a leaf node of the decision tree i.e., testing space complexity is O(nodes) .
Q. What do you mean by Bootstrap Sample in random forest?
A. Each tree in a random forest learns from a random selection of data points during training. Because the samples are drawn via replacement, or bootstrapping, certain samples will be utilised numerous times in a single tree. The theory is that by training each tree on various samples, even if each tree has a lot of variance with regard to a specific set of training data, the overall variance of the forest will be minimised, but not at the expense of bias.
Q. What are the Limitations of Bagging Trees?
A. The loss of interpretability of a model is one of the drawbacks of bagging. When the right technique is ignored, the resulting model can have a lot of bias. Despite its great accuracy, bagging can be computationally expensive, which may deter its adoption in some situations.
..................
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Company Name - Ford
Role: Data Scientist
Q. What is the the difference between normal distribution, standard normal distribution and uniform distribution?
A. A uniform distribution is one in which all values are equally likely within a range (and impossible beyond that range). A Normal distribution is one in which values cluster around the mean, or average, a outlying values are very unlikely. The standard normal distribution, also called the z-distribution, is a special normal distribution where the mean is 0 and the standard deviation is 1.
Q. Which should be preferred among Gini impurity and Entropy?
A. When using sklearn, the Gini impurity is a good default because it is slightly faster to compute. When they work in opposite directions, Gini impurity isolates the most common class in its own branch of the Tree, whereas entropy produces slightly more balanced Trees.
Q. Explain the time and space complexity of training and testing in the case of a Decision Tree.
A. In the training stage for features (dimensions) in the dataset, we sort the data which takes O(n log n) time following which we traverse the data points to find the right threshold which takes O(n) time. Subsequently, for d dimensions, the total time complexity would be: O(nlogn*d). Moreover, the testing time complexity is O(depth) as we have to traverse from the root to a leaf node of the decision tree i.e., testing space complexity is O(nodes) .
Q. What do you mean by Bootstrap Sample in random forest?
A. Each tree in a random forest learns from a random selection of data points during training. Because the samples are drawn via replacement, or bootstrapping, certain samples will be utilised numerous times in a single tree. The theory is that by training each tree on various samples, even if each tree has a lot of variance with regard to a specific set of training data, the overall variance of the forest will be minimised, but not at the expense of bias.
Q. What are the Limitations of Bagging Trees?
A. The loss of interpretability of a model is one of the drawbacks of bagging. When the right technique is ignored, the resulting model can have a lot of bias. Despite its great accuracy, bagging can be computationally expensive, which may deter its adoption in some situations.
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