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.
..................
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
✅ Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals and Opening Mails
✅ 1 Month Internship Opportunity
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
Enroll Now 👉🏻https://www.cloudyml.com/cloudyml-data-science-course/
Happy Learning With CloudyML 💙
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.
..................
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
✅ Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals and Opening Mails
✅ 1 Month Internship Opportunity
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
Enroll Now 👉🏻https://www.cloudyml.com/cloudyml-data-science-course/
Happy Learning With CloudyML 💙
Dear All, we are here with a super discount offer for you.
AI for All - Data Science Course = ₹1249
Demystify Deep Learning = ₹3499
SQL For Data Science = ₹749
All of our courses are on discount and if you buy this combo package, you can get all these courses total worth price Rs. 5497 at extra discounted Combo Price of Rs. 3999.
Enroll Now and get this super deadly discount.
Combo Link👉🏻 https://ai.cloudyml.com/combo-package
This Offer is only valid for tonight.
AI for All - Data Science Course = ₹1249
Demystify Deep Learning = ₹3499
SQL For Data Science = ₹749
All of our courses are on discount and if you buy this combo package, you can get all these courses total worth price Rs. 5497 at extra discounted Combo Price of Rs. 3999.
Enroll Now and get this super deadly discount.
Combo Link👉🏻 https://ai.cloudyml.com/combo-package
This Offer is only valid for tonight.
❤1
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.
..................
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
✅ Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals and Opening Mails
✅ 1 Month Internship Opportunity
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
Enroll Now 👉🏻https://www.cloudyml.com/cloudyml-data-science-course/
Happy Learning With CloudyML 💙
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.
..................
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
✅ Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals and Opening Mails
✅ 1 Month Internship Opportunity
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
Enroll Now 👉🏻https://www.cloudyml.com/cloudyml-data-science-course/
Happy Learning With CloudyML 💙
Job Openings
🔴Role - Fresher / Data Scientist / Data Analyst / Python / Power BI - Interns
Company - Excel Ptp Solutions
Exp - 0-2yrs
Link - https://ai.cloudyml.com/3v56HXC
🔴Role - Business Analyst/ Business Intelligence Analyst
Company - paytm
Exp - 0-3yr
Link - https://ai.cloudyml.com/3gZKPF5
🔴Role- Data Analyst / Data Scientist / Business Analytics / Fresher - MNC
Company - Gaba Consultancy Services
Exp - 0 yr
Link - https://ai.cloudyml.com/35d2iqG
🔴Role - Computer Vision/Image Processing & Machine Learning/Deep Learning
Company - Siddhi Softech
Exp - 0-5yr
Link - https://ai.cloudyml.com/3BE44gE
🔴Role - Fresher / Data Scientist / Data Analyst / Python / Power BI - Interns
Company - Excel Ptp Solutions
Exp - 0-2yrs
Link - https://ai.cloudyml.com/3v56HXC
🔴Role - Business Analyst/ Business Intelligence Analyst
Company - paytm
Exp - 0-3yr
Link - https://ai.cloudyml.com/3gZKPF5
🔴Role- Data Analyst / Data Scientist / Business Analytics / Fresher - MNC
Company - Gaba Consultancy Services
Exp - 0 yr
Link - https://ai.cloudyml.com/35d2iqG
🔴Role - Computer Vision/Image Processing & Machine Learning/Deep Learning
Company - Siddhi Softech
Exp - 0-5yr
Link - https://ai.cloudyml.com/3BE44gE
Check Data Science interview questions at a glance in our Blog.
Blog Link 👉🏻 https://www.cloudyml.com/blog/data-science-interviews-with-questions-at-a-glance/
Blog Link 👉🏻 https://www.cloudyml.com/blog/data-science-interviews-with-questions-at-a-glance/
CloudyML
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.
---------------------------------------
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
✅ Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals and Opening Mails
✅ 1 Month Internship Opportunity
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
Enroll Now 👉🏻https://www.cloudyml.com/cloudyml-data-science-course/
Happy Learning With CloudyML 💙
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.
---------------------------------------
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
✅ Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals and Opening Mails
✅ 1 Month Internship Opportunity
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
Enroll Now 👉🏻https://www.cloudyml.com/cloudyml-data-science-course/
Happy Learning With CloudyML 💙
Hey Guys
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.
This will be 1 Hour of session.
Topic to choose:
✅Data Science Mock Interview
✅Career Consultancy
6 Slot Available
1. 26-02-2022 (7PM, 8PM, 9PM)
2. 27-02-2022 (7PM, 8PM, 9PM)
Fee: INR 2999/-
Register Now (Limited slot available)
https://cloudyml.dayschedule.com/register-yourself
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.
This will be 1 Hour of session.
Topic to choose:
✅Data Science Mock Interview
✅Career Consultancy
6 Slot Available
1. 26-02-2022 (7PM, 8PM, 9PM)
2. 27-02-2022 (7PM, 8PM, 9PM)
Fee: INR 2999/-
Register Now (Limited slot available)
https://cloudyml.dayschedule.com/register-yourself
HURRY UP! Only 4 Slots are available.
Which slot you want to attending??
Which slot you want to attending??
Anonymous Poll
19%
26 Feb 08:00PM (Today)
20%
27 Feb 07:00PM (Tomorrow)
11%
27 Feb 08:00PM (Tomorrow)
11%
27 Feb 09:00PM (Tomorrow)
39%
I want to attend next week
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.
..................
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
Enroll Now 👉🏻https://www.cloudyml.com/cloudyml-data-science-course/
Happy Learning With CloudyML 💙
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
✅ 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
Enroll Now 👉🏻https://www.cloudyml.com/cloudyml-data-science-course/
Happy Learning With CloudyML 💙
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
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
Enroll Now 👉🏻 https://www.cloudyml.com/cloudyml-data-science-course/
Happy Learning With CloudyML 💙
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
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
Enroll Now 👉🏻 https://www.cloudyml.com/cloudyml-data-science-course/
Happy Learning With CloudyML 💙
🔴 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.
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
Enroll Now 👉🏻 https://www.cloudyml.com/cloudyml-data-science-course/
Happy Learning With CloudyML 💙
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
Enroll Now 👉🏻 https://www.cloudyml.com/cloudyml-data-science-course/
Happy Learning With CloudyML 💙
👍1🔥1
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.
..................
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
Enroll Now 👉🏻https://www.cloudyml.com/cloudyml-data-science-course/
Happy Learning With CloudyML 💙
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.
..................
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
Enroll Now 👉🏻https://www.cloudyml.com/cloudyml-data-science-course/
Happy Learning With CloudyML 💙
👇🏻Combo Offer Link👇🏻
https://www.cloudyml.com/course-combo-package/
https://www.cloudyml.com/course-combo-package/
Today's Interview QnAs
Company Name - L&T
Role: Data Scientist
Q. How can you assess a good logistic model?
A. An approach to determining the goodness of fit is through the Homer-Lemeshow statistics, which is computed on data after the observations have been segmented into groups based on having similar predicted probabilities. It examines whether the observed proportions of events are similar to the predicted probabilities of occurrence in subgroups of the data set using a Pearson chi-square test. Small values with large p-values indicate a good fit to the data while large values with p-values below 0.05 indicate a poor fit. The null hypothesis holds that the model fits the data and in the below example we would reject H0.
Q. What is bias, variance trade off ?
A. Bias is the difference between the average prediction of our model and the correct value which we are trying to predict. Variance is the variability of model prediction for a given data point or a value which tells us spread of our data. If our model is too simple and has very few parameters then it may have high bias and low variance. On the other hand if our model has large number of parameters then it’s going to have high variance and low bias. So we need to find the right/good balance without overfitting and underfitting the data. This tradeoff in complexity is why there is a tradeoff between bias and variance. An algorithm can’t be more complex and less complex at the same time.
Q. Why is mean square error a bad measure of model performance?
A. A disadvantage of the mean-squared error is that it is not very interpretable because MSEs vary depending on the prediction task and thus cannot be compared across different tasks. Assume, for example, that one prediction task is concerned with estimating the weight of trucks and another is concerned with estimating the weight of apples. Then, in the first task, a good model may have an RMSE of 100 kg, while a good model for the second task may have an RMSE of 0.5 kg. Therefore, while RMSE is viable for model selection, it is rarely reported and R2 is used instead.
Q. How can the outlier values be treated
A. Below are some of the methods of treating the outliers
Trimming/removing the outlier: In this technique, we remove the outliers from the dataset.
Quantile based flooring and capping : In this technique, the outlier is capped at a certain value above the 90th percentile value or floored at a factor below the 10th percentile value.
Mean/Median imputation : As the mean value is highly influenced by the outliers, it is advised to replace the outliers with the median value.
.....................................
🔴P.S: Join our Beginner to Advanced Data Science Combo Course and get our 4 courses at extra discounted price.
✅Data Science + ML Course = ₹1249
✅Deep Learning = ₹3499
✅SQL For Data Science = ₹999
✅Maths and Statistic For Data Science = ₹999
👉🏻Total Price:₹6746 ₹3999
Course 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
Start Your Data Science Journey Today!
Enroll Now 👉🏻 https://www.cloudyml.com/course-combo-package/
Company Name - L&T
Role: Data Scientist
Q. How can you assess a good logistic model?
A. An approach to determining the goodness of fit is through the Homer-Lemeshow statistics, which is computed on data after the observations have been segmented into groups based on having similar predicted probabilities. It examines whether the observed proportions of events are similar to the predicted probabilities of occurrence in subgroups of the data set using a Pearson chi-square test. Small values with large p-values indicate a good fit to the data while large values with p-values below 0.05 indicate a poor fit. The null hypothesis holds that the model fits the data and in the below example we would reject H0.
Q. What is bias, variance trade off ?
A. Bias is the difference between the average prediction of our model and the correct value which we are trying to predict. Variance is the variability of model prediction for a given data point or a value which tells us spread of our data. If our model is too simple and has very few parameters then it may have high bias and low variance. On the other hand if our model has large number of parameters then it’s going to have high variance and low bias. So we need to find the right/good balance without overfitting and underfitting the data. This tradeoff in complexity is why there is a tradeoff between bias and variance. An algorithm can’t be more complex and less complex at the same time.
Q. Why is mean square error a bad measure of model performance?
A. A disadvantage of the mean-squared error is that it is not very interpretable because MSEs vary depending on the prediction task and thus cannot be compared across different tasks. Assume, for example, that one prediction task is concerned with estimating the weight of trucks and another is concerned with estimating the weight of apples. Then, in the first task, a good model may have an RMSE of 100 kg, while a good model for the second task may have an RMSE of 0.5 kg. Therefore, while RMSE is viable for model selection, it is rarely reported and R2 is used instead.
Q. How can the outlier values be treated
A. Below are some of the methods of treating the outliers
Trimming/removing the outlier: In this technique, we remove the outliers from the dataset.
Quantile based flooring and capping : In this technique, the outlier is capped at a certain value above the 90th percentile value or floored at a factor below the 10th percentile value.
Mean/Median imputation : As the mean value is highly influenced by the outliers, it is advised to replace the outliers with the median value.
.....................................
🔴P.S: Join our Beginner to Advanced Data Science Combo Course and get our 4 courses at extra discounted price.
✅Data Science + ML Course = ₹1249
✅Deep Learning = ₹3499
✅SQL For Data Science = ₹999
✅Maths and Statistic For Data Science = ₹999
👉🏻Total Price:
Course 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
Start Your Data Science Journey Today!
Enroll Now 👉🏻 https://www.cloudyml.com/course-combo-package/
Hello Data Science Enthusiasts
Mr. Akash Raj ( Data Scientist, Founder- CloudyML) coming 🔴 LIVE Today on Instagram.
Time: 07:00 PM
Topic: Interaction Session on Career in Data Science Field and more.
➡️Instagram Profile: https://www.instagram.com/cloudyml.akash/
Follow on Instagram to get notification.
Mr. Akash Raj ( Data Scientist, Founder- CloudyML) coming 🔴 LIVE Today on Instagram.
Time: 07:00 PM
Topic: Interaction Session on Career in Data Science Field and more.
➡️Instagram Profile: https://www.instagram.com/cloudyml.akash/
Follow on Instagram to get notification.
Drop Your Questions here for Today's Live session 👇🏻
https://app.sli.do/event/5A7yiZs8sgxbf4aQa6sqAG
https://app.sli.do/event/5A7yiZs8sgxbf4aQa6sqAG