Today's Interview QnAs
Company Name - TCS
Role: Data Scientist
Q. What is a bias-variance trade-off?
A. If the algorithm is too basic (hypothesis with linear eq. ), it may be prone to errors due to strong bias and low variance. If the algorithms are too sophisticated (hypothesis with a high degree eq. ), the variance and bias may be considerable. The new entries will not fare well in the latter scenario. Trade-off, also known as Bias Variance Trade-off, is something that exists between these two situations. There is a tradeoff between bias and variance because of this tradeoff in complexity. It's impossible for an algorithm to be both more complex and less complex at the same time.
Q. What are the support vectors in SVM?
A. Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the classifier. Deleting the support vectors will change the position of the hyperplane. These are the points that help us build our SVM.
Q. Describe different regularization methods, such as L1 and L2 regularization?
A. L1 regularization, also known as L1 norm or Lasso (in regression problems), combats overfitting by shrinking the parameters towards 0. This makes some features obsolete. L2 regularization, or the L2 norm, or Ridge (in regression problems), combats overfitting by forcing weights to be small, but not making them exactly 0.
Q. What is correlation and covariance in statistics?
A. Both covariance and correlation measure the relationship and the dependency between two variables. Covariance indicates the direction of the linear relationship between variables. Correlation measures both the strength and direction of the linear relationship between two variables. Correlation values are standardized. Covariance values are not standardized.
..................
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Company Name - TCS
Role: Data Scientist
Q. What is a bias-variance trade-off?
A. If the algorithm is too basic (hypothesis with linear eq. ), it may be prone to errors due to strong bias and low variance. If the algorithms are too sophisticated (hypothesis with a high degree eq. ), the variance and bias may be considerable. The new entries will not fare well in the latter scenario. Trade-off, also known as Bias Variance Trade-off, is something that exists between these two situations. There is a tradeoff between bias and variance because of this tradeoff in complexity. It's impossible for an algorithm to be both more complex and less complex at the same time.
Q. What are the support vectors in SVM?
A. Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the classifier. Deleting the support vectors will change the position of the hyperplane. These are the points that help us build our SVM.
Q. Describe different regularization methods, such as L1 and L2 regularization?
A. L1 regularization, also known as L1 norm or Lasso (in regression problems), combats overfitting by shrinking the parameters towards 0. This makes some features obsolete. L2 regularization, or the L2 norm, or Ridge (in regression problems), combats overfitting by forcing weights to be small, but not making them exactly 0.
Q. What is correlation and covariance in statistics?
A. Both covariance and correlation measure the relationship and the dependency between two variables. Covariance indicates the direction of the linear relationship between variables. Correlation measures both the strength and direction of the linear relationship between two variables. Correlation values are standardized. Covariance values are not standardized.
..................
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Today's Interview QnAs
Company Name - Accenture
Role: Data Scientist
Q. What are the conditions for Overfitting and Underfitting?
A. The model has not overfitted if both the training and test accuracy are close. The model has overfitted if the training result is excellent but the test result is bad. The model is underfit if the training and test accuracy are both low.
Q. How do you clean raw data ?
A. Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. While the techniques used for data cleaning may vary according to the types of data your company stores, you can follow these basic steps to map out a framework for your organization. Step 1: Remove duplicate or irrelevant observations, Step 2: Fix structural errors, Step 3: Filter unwanted outliers and Step 4: Handle missing data.
Q. Explain any feature selection method?
A. The goal of feature selection in machine learning is to find the best set of features that allows one to build useful models of studied phenomena. Regularization consists of adding a penalty to the different parameters of the machine learning model to reduce the freedom of the model, i.e. to avoid over-fitting. In linear model regularization, the penalty is applied over the coefficients that multiply each of the predictors. From the different types of regularization, Lasso or L1 has the property that is able to shrink some of the coefficients to zero. Therefore, that feature can be removed from the model.
Q. Why random forest better than multiple decided trees?
A. Overfitting, error due to variation, and error due to bias are all important difficulties with decision trees. A Random Forest is a group of decision trees that produce a single, aggregated outcome. Overfitting is less likely when numerous trees are used in the random forest. They're also difficult to comprehend. A decision tree is simple to read and comprehend, whereas a random forest is more difficult to comprehend. A single decision tree isn't very good at forecasting outcomes, but it's simple to set up. More trees result in a more robust model that avoids overfitting. Each tree in the forest must be generated, processed, and analysed. As a result, this is a lengthy process that might take hours or even days.
..................
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Company Name - Accenture
Role: Data Scientist
Q. What are the conditions for Overfitting and Underfitting?
A. The model has not overfitted if both the training and test accuracy are close. The model has overfitted if the training result is excellent but the test result is bad. The model is underfit if the training and test accuracy are both low.
Q. How do you clean raw data ?
A. Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. While the techniques used for data cleaning may vary according to the types of data your company stores, you can follow these basic steps to map out a framework for your organization. Step 1: Remove duplicate or irrelevant observations, Step 2: Fix structural errors, Step 3: Filter unwanted outliers and Step 4: Handle missing data.
Q. Explain any feature selection method?
A. The goal of feature selection in machine learning is to find the best set of features that allows one to build useful models of studied phenomena. Regularization consists of adding a penalty to the different parameters of the machine learning model to reduce the freedom of the model, i.e. to avoid over-fitting. In linear model regularization, the penalty is applied over the coefficients that multiply each of the predictors. From the different types of regularization, Lasso or L1 has the property that is able to shrink some of the coefficients to zero. Therefore, that feature can be removed from the model.
Q. Why random forest better than multiple decided trees?
A. Overfitting, error due to variation, and error due to bias are all important difficulties with decision trees. A Random Forest is a group of decision trees that produce a single, aggregated outcome. Overfitting is less likely when numerous trees are used in the random forest. They're also difficult to comprehend. A decision tree is simple to read and comprehend, whereas a random forest is more difficult to comprehend. A single decision tree isn't very good at forecasting outcomes, but it's simple to set up. More trees result in a more robust model that avoids overfitting. Each tree in the forest must be generated, processed, and analysed. As a result, this is a lengthy process that might take hours or even days.
..................
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Today's Interview QnAs
Company Name - Cognizant
Role: Data Scientist
Q. How will you remove duplicates from dataframe based on particular column?
A. An important part of Data analysis is analyzing Duplicate Values and removing them. Pandas drop_duplicates() method helps in removing duplicates from the data frame. Subset parameter takes a column or list of column label. After passing columns, it will consider them only for duplicates.
Q. Explain ROC curve.
A. A Receiver Operator Characteristic (ROC) curve is a graphical plot used to show the diagnostic ability of binary classifiers.A ROC curve is constructed by plotting the true positive rate (TPR) against the false positive rate (FPR). The true positive rate is the proportion of observations that were correctly predicted to be positive out of all positive observations (TP/(TP + FN)). Similarly, the false positive rate is the proportion of observations that are incorrectly predicted to be positive out of all negative observations (FP/(TN + FP)).
Q. What is learning rate in gradient descent?
A. Gradient descent is the popular optimization algorithm used in machine learning to estimate the model parameters. Learning rate, generally represented by the symbol ‘α’, is a hyper-parameter used to control the rate at which an algorithm updates the parameter estimates or learns the values of the parameters.
Q. What do you understand by Recall and Precision?
A. Precision is defined as the fraction of relevant instances among all retrieved instances. Recall, sometimes referred to as ‘sensitivity, is the fraction of retrieved instances among all relevant instances. A perfect classifier has precision and recall both equal to 1.
..................
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Company Name - Cognizant
Role: Data Scientist
Q. How will you remove duplicates from dataframe based on particular column?
A. An important part of Data analysis is analyzing Duplicate Values and removing them. Pandas drop_duplicates() method helps in removing duplicates from the data frame. Subset parameter takes a column or list of column label. After passing columns, it will consider them only for duplicates.
Q. Explain ROC curve.
A. A Receiver Operator Characteristic (ROC) curve is a graphical plot used to show the diagnostic ability of binary classifiers.A ROC curve is constructed by plotting the true positive rate (TPR) against the false positive rate (FPR). The true positive rate is the proportion of observations that were correctly predicted to be positive out of all positive observations (TP/(TP + FN)). Similarly, the false positive rate is the proportion of observations that are incorrectly predicted to be positive out of all negative observations (FP/(TN + FP)).
Q. What is learning rate in gradient descent?
A. Gradient descent is the popular optimization algorithm used in machine learning to estimate the model parameters. Learning rate, generally represented by the symbol ‘α’, is a hyper-parameter used to control the rate at which an algorithm updates the parameter estimates or learns the values of the parameters.
Q. What do you understand by Recall and Precision?
A. Precision is defined as the fraction of relevant instances among all retrieved instances. Recall, sometimes referred to as ‘sensitivity, is the fraction of retrieved instances among all relevant instances. A perfect classifier has precision and recall both equal to 1.
..................
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Today's Interview QnAs
Company Name - Capgemini
Role: Data Scientist
Q. Conditions for Overfitting and Underfitting.
A. If both the training accuracy and test accuracy are close then the model has not overfit. If the training result is very good and the test result is poor then the model has overfitted. If the training accuracy and test accuracy is low then the model has underfit.
Q. What does it mean when the p-values are high and low?
A. The p-value is a number between 0 and 1 and interpreted in the following way: A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis.
A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis. p-values very close to the cutoff (0.05) are considered to be marginal (could go either way).
Q. What are the differences between correlation and covariance?
A. Covariance is a measure to indicate the extent to which two random variables change in tandem.Correlation is a measure used to represent how strongly two random variables are related to each other.Covariance is nothing but a measure of correlation.Correlation refers to the scaled form of covariance.
Q. How to create a sparse Matrix in Python?
A. Python’s SciPy gives tools for creating sparse matrices using multiple data structures, as well as tools for converting a dense matrix to a sparse matrix. The function csr_matrix() is used to create a sparse matrix of compressed sparse row format whereas csc_matrix() is used to create a sparse matrix of compressed sparse column format.
..................
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Company Name - Capgemini
Role: Data Scientist
Q. Conditions for Overfitting and Underfitting.
A. If both the training accuracy and test accuracy are close then the model has not overfit. If the training result is very good and the test result is poor then the model has overfitted. If the training accuracy and test accuracy is low then the model has underfit.
Q. What does it mean when the p-values are high and low?
A. The p-value is a number between 0 and 1 and interpreted in the following way: A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis.
A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis. p-values very close to the cutoff (0.05) are considered to be marginal (could go either way).
Q. What are the differences between correlation and covariance?
A. Covariance is a measure to indicate the extent to which two random variables change in tandem.Correlation is a measure used to represent how strongly two random variables are related to each other.Covariance is nothing but a measure of correlation.Correlation refers to the scaled form of covariance.
Q. How to create a sparse Matrix in Python?
A. Python’s SciPy gives tools for creating sparse matrices using multiple data structures, as well as tools for converting a dense matrix to a sparse matrix. The function csr_matrix() is used to create a sparse matrix of compressed sparse row format whereas csc_matrix() is used to create a sparse matrix of compressed sparse column format.
..................
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Today's Interview QnAs
Company Name - HP
Role: Data Scientist
Q. Difference between Xgboost and Random Forest?
A. In Random Forest, the decision trees are built independently so that if there are five trees in an algorithm, all the trees are built at a time but with different features and data present in the algorithm. This makes developers look into the trees and model them in parallel. XGBoost builds one tree at a time so that each data pertaining to the decision tree is taken into account and the data is filled if there are any missing data. This helps developers to work with gradient algorithms along with the decision tree algorithm for better results.
Q. What is Homoscedasticity?
A. Homoscedasticity, or homogeneity of variances, is an assumption of equal or similar variances in different groups being compared. This is an important assumption of parametric statistical tests because they are sensitive to any dissimilarities. Uneven variances in samples result in biased and skewed test results. In statistics, a sequence of random variables is homoscedastic if all its random variables have the same finite variance.
Q. What is DBSCAN Clustering?
A. DBSCAN is a density-based clustering algorithm that works on the assumption that clusters are dense regions in space separated by regions of lower density. It groups ‘densely grouped’ data points into a single cluster. It can identify clusters in large spatial datasets by looking at the local density of the data points. The most exciting feature of DBSCAN clustering is that it is robust to outliers. It also does not require the number of clusters to be told beforehand, unlike K-Means, where we have to specify the number of centroids.
Q. What is box cox transformation?
A. A Box Cox transformation is a transformation of non-normal dependent variables into a normal shape. Normality is an important assumption for many statistical techniques; if your data isn’t normal, applying a Box-Cox means that you are able to run a broader number of tests.
..................
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Company Name - HP
Role: Data Scientist
Q. Difference between Xgboost and Random Forest?
A. In Random Forest, the decision trees are built independently so that if there are five trees in an algorithm, all the trees are built at a time but with different features and data present in the algorithm. This makes developers look into the trees and model them in parallel. XGBoost builds one tree at a time so that each data pertaining to the decision tree is taken into account and the data is filled if there are any missing data. This helps developers to work with gradient algorithms along with the decision tree algorithm for better results.
Q. What is Homoscedasticity?
A. Homoscedasticity, or homogeneity of variances, is an assumption of equal or similar variances in different groups being compared. This is an important assumption of parametric statistical tests because they are sensitive to any dissimilarities. Uneven variances in samples result in biased and skewed test results. In statistics, a sequence of random variables is homoscedastic if all its random variables have the same finite variance.
Q. What is DBSCAN Clustering?
A. DBSCAN is a density-based clustering algorithm that works on the assumption that clusters are dense regions in space separated by regions of lower density. It groups ‘densely grouped’ data points into a single cluster. It can identify clusters in large spatial datasets by looking at the local density of the data points. The most exciting feature of DBSCAN clustering is that it is robust to outliers. It also does not require the number of clusters to be told beforehand, unlike K-Means, where we have to specify the number of centroids.
Q. What is box cox transformation?
A. A Box Cox transformation is a transformation of non-normal dependent variables into a normal shape. Normality is an important assumption for many statistical techniques; if your data isn’t normal, applying a Box-Cox means that you are able to run a broader number of tests.
..................
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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 - Larsen and Toubro
Role - Data Scientist
----------------------------------------
Q. Ways to avoid overfitting
A. Some steps that we can take to avoid it:
1. Data augmentation
2. L1/L2 Regularization
3. Remove layers / number of units per layer
4. Cross-validation
Q. Image classification algorithms
A. Image Classification algorithms are the algorithms which are used to classify labels for images using their characteristics. Example: Convolutional Neural Networks.
Q. args will return?
A. The special syntax *args in function is used to pass a variable number of arguments to a function. It is used to pass a non-key worded, variable-length argument list. The syntax is to use the symbol * to take in a variable number of arguments; by convention, it is often used with the word args.
Q. Difference between having and where clause in SQL.
A. WHERE Clause is used to filter the records from the table based on the specified condition. HAVING Clause is used to filter record from the groups based on the specified condition.
Q. How do you handle categorical data?
A. One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging (Value=1) or not (Value=0) to that group.
Q. What Is Interpolation And Extrapolation?
A. Interpolation is the process of calculating the unknown value from known given values whereas extrapolation is the process of calculating unknown values beyond the given data points.
Q. SQL joins and Groups
A. The SQL Joins clause is used to combine records from two or more tables in a database. The GROUP BY statement groups rows that have the same values into summary rows, like "find the number of customers in each country".
Q. How do you handle null values and which Imputation method is more favorable?
A. Ways to handle missing values in the dataset:
Deleting Rows with missing values.
Impute missing values for continuous variable.
Impute missing values for categorical variable.
Other Imputation Methods.
Using Algorithms that support missing values.
Prediction of missing values.
Multiple imputation is more advantageous than the single imputation because it uses several complete data sets and provides both the within-imputation and between-imputation variability.
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Company Name - Larsen and Toubro
Role - Data Scientist
----------------------------------------
Q. Ways to avoid overfitting
A. Some steps that we can take to avoid it:
1. Data augmentation
2. L1/L2 Regularization
3. Remove layers / number of units per layer
4. Cross-validation
Q. Image classification algorithms
A. Image Classification algorithms are the algorithms which are used to classify labels for images using their characteristics. Example: Convolutional Neural Networks.
Q. args will return?
A. The special syntax *args in function is used to pass a variable number of arguments to a function. It is used to pass a non-key worded, variable-length argument list. The syntax is to use the symbol * to take in a variable number of arguments; by convention, it is often used with the word args.
Q. Difference between having and where clause in SQL.
A. WHERE Clause is used to filter the records from the table based on the specified condition. HAVING Clause is used to filter record from the groups based on the specified condition.
Q. How do you handle categorical data?
A. One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging (Value=1) or not (Value=0) to that group.
Q. What Is Interpolation And Extrapolation?
A. Interpolation is the process of calculating the unknown value from known given values whereas extrapolation is the process of calculating unknown values beyond the given data points.
Q. SQL joins and Groups
A. The SQL Joins clause is used to combine records from two or more tables in a database. The GROUP BY statement groups rows that have the same values into summary rows, like "find the number of customers in each country".
Q. How do you handle null values and which Imputation method is more favorable?
A. Ways to handle missing values in the dataset:
Deleting Rows with missing values.
Impute missing values for continuous variable.
Impute missing values for categorical variable.
Other Imputation Methods.
Using Algorithms that support missing values.
Prediction of missing values.
Multiple imputation is more advantageous than the single imputation because it uses several complete data sets and provides both the within-imputation and between-imputation variability.
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We received a lot of requests for an interactive session with Mr. Akash Raj (Data Scientist, Founder: CloudyML ) for Data Science related queries, career guidqnce and mock interviews for jobs.
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We received a lot of requests for an interactive session with Mr. Akash Raj (Data Scientist, Founder: CloudyML ) for Data Science related queries, career guidqnce and mock interviews for jobs.
So CloudyML is here to give you 1-1 interaction opportunity with him.
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Topic to choose:
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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.
..................
P.S: If you wanna crack interviews of Data Science and become Job Ready, join our" Assignment based Data Science course" today!
Features
✅ Hands on Guided assignments
✅ Capstone End-to-end Projects
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Today's Interview QnA
Company Name - Accenture
Role: Data Scientist
Q. What is skewed Distribution & uniform distribution?
A. Uniform distribution refers to a condition when all the observations in a dataset are equally spread across the range of distribution. Skewed distribution refers to the condition when one side of the graph has more dataset in comparison to the other side.
Q. What is bias and variance in data science?
A. Bias is the amount that a model’s prediction differs from the target value, compared to the training data. Bias error results from simplifying the assumptions used in a model so the target functions are easier to approximate. Variance indicates how much the estimate of the target function will alter if different training data were used. In other words, variance describes how much a random variable differs from its expected value.
Q. Difference between RNN and CNN?
A. The main difference between a CNN and an RNN is the ability to process temporal information — data that comes in sequences, such as a sentence. Recurrent neural networks are designed for this very purpose, while convolutional neural networks are incapable of effectively interpreting temporal information. There are differences in the structures of the neural networks themselves to fit those different use cases. CNNs employ filters within convolutional layers to transform data (more on that later), whereas RNNs are predictive, reusing activation functions from other data points in the sequence to generate the next output in a series.
Q. What’s the Central Limit Theorem and why is it important?
A. The central limit theorem tells us that no matter what the distribution of the population is, the shape of the sampling distribution will approach normality as the sample size (N) increases. This is useful, as the research never knows which mean in the sampling distribution is the same as the population mean, but by selecting many random samples from a population the sample means will cluster together, allowing the research to make a very good estimate of the population mean. Thus, as the sample size (N) increases the sampling error will decrease.
Q. How will you define the number of clusters in a clustering algorithm?
A. In k-means clustering, the number of clusters that you want to divide your data points into i.e., the value of K has to be pre-determined whereas in Hierarchical clustering data is automatically formed into a tree shape form (dendrogram).
[1:11 PM, 3/1/2022] Rahul CloudyML: For multicollinearity u need atleast 2 variables. I checked online. It's wrong mentioned here that u need 3 variables
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Company Name - Accenture
Role: Data Scientist
Q. What is skewed Distribution & uniform distribution?
A. Uniform distribution refers to a condition when all the observations in a dataset are equally spread across the range of distribution. Skewed distribution refers to the condition when one side of the graph has more dataset in comparison to the other side.
Q. What is bias and variance in data science?
A. Bias is the amount that a model’s prediction differs from the target value, compared to the training data. Bias error results from simplifying the assumptions used in a model so the target functions are easier to approximate. Variance indicates how much the estimate of the target function will alter if different training data were used. In other words, variance describes how much a random variable differs from its expected value.
Q. Difference between RNN and CNN?
A. The main difference between a CNN and an RNN is the ability to process temporal information — data that comes in sequences, such as a sentence. Recurrent neural networks are designed for this very purpose, while convolutional neural networks are incapable of effectively interpreting temporal information. There are differences in the structures of the neural networks themselves to fit those different use cases. CNNs employ filters within convolutional layers to transform data (more on that later), whereas RNNs are predictive, reusing activation functions from other data points in the sequence to generate the next output in a series.
Q. What’s the Central Limit Theorem and why is it important?
A. The central limit theorem tells us that no matter what the distribution of the population is, the shape of the sampling distribution will approach normality as the sample size (N) increases. This is useful, as the research never knows which mean in the sampling distribution is the same as the population mean, but by selecting many random samples from a population the sample means will cluster together, allowing the research to make a very good estimate of the population mean. Thus, as the sample size (N) increases the sampling error will decrease.
Q. How will you define the number of clusters in a clustering algorithm?
A. In k-means clustering, the number of clusters that you want to divide your data points into i.e., the value of K has to be pre-determined whereas in Hierarchical clustering data is automatically formed into a tree shape form (dendrogram).
[1:11 PM, 3/1/2022] Rahul CloudyML: For multicollinearity u need atleast 2 variables. I checked online. It's wrong mentioned here that u need 3 variables
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🔴 Internship Alert🔴
CloudyML is hiring for Data Analyst Intern.
It will be Paid Internship.
Follow the link below, if interested
https://forms.gle/Ex8nCWtX7nCXQciT9
CloudyML is hiring for Data Analyst Intern.
It will be Paid Internship.
Follow the link below, if interested
https://forms.gle/Ex8nCWtX7nCXQciT9
Today's Interview QnAs
Company Name - Ford
Role: Data Scientist
Q. Difference between correlation and Multicollinearity.?
A. Correlation refers to the linear relationship between 2 variables. Multicollinearity is a special case of collinearity where a strong linear relationship exists between 2 or more independent variables even if no pair of variables has a high correlation. Number of variables involved is 2 in correlation while in multicollinearity is 2 or more.
Q. What is pruning in Decision Tree?
A. Pruning a decision tree helps to prevent overfitting the training data so that our model generalizes well to unseen data. Pruning a decision tree means to remove a subtree that is redundant and not a useful split and replace it with a leaf node. Decision tree pruning can be divided into two types: pre-pruning and post-pruning.
Q. What is loss and cost function?
A. Loss function is a method of evaluating how well your algorithm models your data set. If your predictions are totally off, your loss function will output a higher number. If they’re pretty good, it’ll output a lower number. A loss function/error function is for a single training example/input. A cost function, on the other hand, is the average loss over the entire training dataset.
Q. How does decision tree makes a decision?
A. Decision trees are upside down which means the root is at the top and then this root is split into various several nodes. Decision trees are nothing but a bunch of if-else statements in layman terms. It checks if the condition is true and if it is then it goes to the next node attached to that decision.
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Company Name - Ford
Role: Data Scientist
Q. Difference between correlation and Multicollinearity.?
A. Correlation refers to the linear relationship between 2 variables. Multicollinearity is a special case of collinearity where a strong linear relationship exists between 2 or more independent variables even if no pair of variables has a high correlation. Number of variables involved is 2 in correlation while in multicollinearity is 2 or more.
Q. What is pruning in Decision Tree?
A. Pruning a decision tree helps to prevent overfitting the training data so that our model generalizes well to unseen data. Pruning a decision tree means to remove a subtree that is redundant and not a useful split and replace it with a leaf node. Decision tree pruning can be divided into two types: pre-pruning and post-pruning.
Q. What is loss and cost function?
A. Loss function is a method of evaluating how well your algorithm models your data set. If your predictions are totally off, your loss function will output a higher number. If they’re pretty good, it’ll output a lower number. A loss function/error function is for a single training example/input. A cost function, on the other hand, is the average loss over the entire training dataset.
Q. How does decision tree makes a decision?
A. Decision trees are upside down which means the root is at the top and then this root is split into various several nodes. Decision trees are nothing but a bunch of if-else statements in layman terms. It checks if the condition is true and if it is then it goes to the next node attached to that decision.
P.S: If you wanna crack interviews of Data Science and become Job Ready, join our" Assignment based Data Science course" today!
Features
✅ Hands on Guided assignments
✅ Capstone End-to-end Projects
✅ 1-1 Skype Live Doubt Clearance
✅ Frequently Asked Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals after course completion
✅ Job Opening Mails, Quiz, Newsletters and more
✅ 1 Month Internship Opportunity
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
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