We all know the importance of Cloud Computing now a days and having certification in Azure can give you so many benefits in your career.
CloudyML is launching a new course on Microsoft AZ-900 Azure Fundamental Certification Exam Preparation Course.
This course will give you a complete hands-on understanding of Azure Cloud Fundamentals with Guided tutorial Videos, Practice Test Modules, Quizzes and 1-1 Live Doubt Clearance Support over Skype Chat Everyday so you can crack AZ-900 Certification Exam.
So what are you waiting for, ENROLL TODAY and become Azure Certified.
Follow the link below and visit our website now and check the course syllabus and more details.
ππ»https://www.cloudyml.com/microsoft-azure-course/
CloudyML is launching a new course on Microsoft AZ-900 Azure Fundamental Certification Exam Preparation Course.
This course will give you a complete hands-on understanding of Azure Cloud Fundamentals with Guided tutorial Videos, Practice Test Modules, Quizzes and 1-1 Live Doubt Clearance Support over Skype Chat Everyday so you can crack AZ-900 Certification Exam.
So what are you waiting for, ENROLL TODAY and become Azure Certified.
Follow the link below and visit our website now and check the course syllabus and more details.
ππ»https://www.cloudyml.com/microsoft-azure-course/
How to become a DATA ANALYST in 3 months from Scratch π€π€π€ ???
Check out our latest YouTube video in which we have explained a complete roadmap to Data Analytics from any background. Link given belowπ€©ππ»
ππ» https://youtu.be/jd7R-jbqID8
Check out our latest YouTube video in which we have explained a complete roadmap to Data Analytics from any background. Link given belowπ€©ππ»
ππ» https://youtu.be/jd7R-jbqID8
Today's Interview QnA
Company- Essar technology
Role- Data Analyst
1.What are the different types of functions generally used in Tableau?
In Tableau, we have a lot of processing and analytical freedom with the virtue of functions available.
With the help of different types of functions, we can perform a lot of analytical operations on the data.
The main categories of Tableau function are:
String function: These functions like ASCII, CHAR, FIND, ISDATE, LOWER, etc, are known as string functions because they work on the string values or characters to manipulate them.
Date function: We use date functions to apply logical as well as arithmetic operations on date values present at the data source.Using the date functions we can manipulate the date values by changing the old values, creating new ones or searching data on the basis of specific dates.
Some commonly used date functions in Tableau are DATEADD, MAKEDATE, ISDATE, MAKETIME, MONTH, MIN/MAX, TODAY, NOW, etc.
Logical function: We use logical functions to perform logical or relational operations on data in Tableau.
Some commonly used logical functions in Tableau are, CASE, IF, IFNULL, ISNULL, ZN, etc.
Aggregate function: We use aggregate functions to apply aggregation on data values in different ways.
Some important aggregation functions used in Tableau are; AVG, ATTR, MAX, MEDIAN, MIN, PERCENTILE, SUM, STDDEV, etc.
User function: We use functions to manage the users registered on Tableau Server or Tableau Online.
Commonly used user functions are, FULLNAME, ISFULLNAME, ISUSERNAME, USERDOMAIN, USERNAME, etc.
2.What do you understand by context filters?
Context filters are used to apply context on the data under analysis.
By applying a context we set a perspective according to which we can see the charts and graphs.
For example, we have sales data of an electronic store and we want to conduct our analysis only for the corporate sector or segment.
To do this, we have to apply a context filter on our Tableau sheet. Once we add the context for the Corporate segment from the Add to context option, all the charts present on the sheet will only show data relevant to the Corporate segment.
In this way, we can apply a context to our analysis in Tableau.
3. How do you add a Note to a cell?
To add a Note, select the cell and right-click on the same. then select the New Note option and type in any note that you wish to. In case you want to delete the Note, follow the same procedure and select the Delete Note option. Notes are indicated by a red triangle at the top-right corner of the cell.
4. What is DAX?
DAX stands for Data Analysis Expressions. It's a collection of functions, operators, and constants used in formulas to calculate and return values. In other words, it helps you create new info from data you already have.
ββββββββββββββββββββ-
π΄If you wanna switch your career from any domain to Data Science OR Data Analytics OR Business Analytics then check our MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!
Course Linkππ» https://ai.cloudyml.com/Mega-Combo
Company- Essar technology
Role- Data Analyst
1.What are the different types of functions generally used in Tableau?
In Tableau, we have a lot of processing and analytical freedom with the virtue of functions available.
With the help of different types of functions, we can perform a lot of analytical operations on the data.
The main categories of Tableau function are:
String function: These functions like ASCII, CHAR, FIND, ISDATE, LOWER, etc, are known as string functions because they work on the string values or characters to manipulate them.
Date function: We use date functions to apply logical as well as arithmetic operations on date values present at the data source.Using the date functions we can manipulate the date values by changing the old values, creating new ones or searching data on the basis of specific dates.
Some commonly used date functions in Tableau are DATEADD, MAKEDATE, ISDATE, MAKETIME, MONTH, MIN/MAX, TODAY, NOW, etc.
Logical function: We use logical functions to perform logical or relational operations on data in Tableau.
Some commonly used logical functions in Tableau are, CASE, IF, IFNULL, ISNULL, ZN, etc.
Aggregate function: We use aggregate functions to apply aggregation on data values in different ways.
Some important aggregation functions used in Tableau are; AVG, ATTR, MAX, MEDIAN, MIN, PERCENTILE, SUM, STDDEV, etc.
User function: We use functions to manage the users registered on Tableau Server or Tableau Online.
Commonly used user functions are, FULLNAME, ISFULLNAME, ISUSERNAME, USERDOMAIN, USERNAME, etc.
2.What do you understand by context filters?
Context filters are used to apply context on the data under analysis.
By applying a context we set a perspective according to which we can see the charts and graphs.
For example, we have sales data of an electronic store and we want to conduct our analysis only for the corporate sector or segment.
To do this, we have to apply a context filter on our Tableau sheet. Once we add the context for the Corporate segment from the Add to context option, all the charts present on the sheet will only show data relevant to the Corporate segment.
In this way, we can apply a context to our analysis in Tableau.
3. How do you add a Note to a cell?
To add a Note, select the cell and right-click on the same. then select the New Note option and type in any note that you wish to. In case you want to delete the Note, follow the same procedure and select the Delete Note option. Notes are indicated by a red triangle at the top-right corner of the cell.
4. What is DAX?
DAX stands for Data Analysis Expressions. It's a collection of functions, operators, and constants used in formulas to calculate and return values. In other words, it helps you create new info from data you already have.
ββββββββββββββββββββ-
π΄If you wanna switch your career from any domain to Data Science OR Data Analytics OR Business Analytics then check our MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!
Course Linkππ» https://ai.cloudyml.com/Mega-Combo
Do you wanna know how you can crack interview of ACCENTURE to secure a job of ML ENGINEER ?
Check out our latest YouTube video in which we have solved the interview questions asked by Accenture of the role of ML Engineer with complete explanation. Link given below.
ππ» https://youtu.be/EdzvIaM_VMw
Check out our latest YouTube video in which we have solved the interview questions asked by Accenture of the role of ML Engineer with complete explanation. Link given below.
ππ» https://youtu.be/EdzvIaM_VMw
Today's Interview QnAs
Company - PWC
Role- Data Scientist
1. What is Power of Test?
Power of Test: The Power of the test is defined as the probability of rejecting the null hypothesis when the null hypothesis is false. Since Ξ² is the probability of a Type II error, the power of the test is defined as 1- Ξ². In advanced statistics, we compare various types of tests based on their size and power, where the size denotes the actual proportion of rejections when the null is true and the power denotes the actual proportion of rejections when the null is false.
2. Which are the important steps of Data Cleaning?
Different types of data require different types of cleaning, the most important steps of Data Cleaning are:
Data Quality
Removing Duplicate Data (also irrelevant data)
Structural errors
Outliers
Treatment for Missing Data
Data Cleaning is an important step before analysing data, it helps to increase the accuracy of the model. This helps organisations to make an informed decision.
Data Scientists usually spends 80% of their time cleaning data.
3.What is correlation and covariance in statistics?
Correlation is defined as the measure of the relationship between two variables. If two variables are directly proportional to each other, then its positive correlation. If the variables are indirectly proportional to each other, it is known as a negative correlation. Covariance is the measure of how much two random variables vary together.
4.What is the Confusion Matrix?
A confusion matrix is a tabular summary of the number of correct and incorrect predictions made by a classifier. It is used to measure the performance of a classification model. It can be used to evaluate the performance of a classification model through the calculation of performance metrics like accuracy, precision, recall, and F1-score.
For example, in a case of binary classifier, it predicts all data instances of a test dataset as either positive or negative. This produces four outcomes-
True positive(TP) β Correct positive prediction
False-positive(FP) β Incorrect positive prediction
True negative(TN) β Correct negative prediction
False-negative(FN) β Incorrect negative prediction
It helps in calculating various measures including error rate (FP+FN)/(P+N), specificity(TN/N), accuracy(TP+TN)/(P+N), sensitivity (TP/P), and precision( TP/(TP+FP) ).
A confusion matrix is essentially used to evaluate the performance of a machine learning model when the truth values of the experiments are already known and the target class has two or more than two categories of data. It helps in visualisation and evaluation of the results of the statistical process.
ββββββββββββββββββββ-
π΄If you wanna switch your career from any domain to Data Science OR Data Analytics OR Business Analytics then check our MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!
Course Linkππ» https://ai.cloudyml.com/Mega-Combo
Company - PWC
Role- Data Scientist
1. What is Power of Test?
Power of Test: The Power of the test is defined as the probability of rejecting the null hypothesis when the null hypothesis is false. Since Ξ² is the probability of a Type II error, the power of the test is defined as 1- Ξ². In advanced statistics, we compare various types of tests based on their size and power, where the size denotes the actual proportion of rejections when the null is true and the power denotes the actual proportion of rejections when the null is false.
2. Which are the important steps of Data Cleaning?
Different types of data require different types of cleaning, the most important steps of Data Cleaning are:
Data Quality
Removing Duplicate Data (also irrelevant data)
Structural errors
Outliers
Treatment for Missing Data
Data Cleaning is an important step before analysing data, it helps to increase the accuracy of the model. This helps organisations to make an informed decision.
Data Scientists usually spends 80% of their time cleaning data.
3.What is correlation and covariance in statistics?
Correlation is defined as the measure of the relationship between two variables. If two variables are directly proportional to each other, then its positive correlation. If the variables are indirectly proportional to each other, it is known as a negative correlation. Covariance is the measure of how much two random variables vary together.
4.What is the Confusion Matrix?
A confusion matrix is a tabular summary of the number of correct and incorrect predictions made by a classifier. It is used to measure the performance of a classification model. It can be used to evaluate the performance of a classification model through the calculation of performance metrics like accuracy, precision, recall, and F1-score.
For example, in a case of binary classifier, it predicts all data instances of a test dataset as either positive or negative. This produces four outcomes-
True positive(TP) β Correct positive prediction
False-positive(FP) β Incorrect positive prediction
True negative(TN) β Correct negative prediction
False-negative(FN) β Incorrect negative prediction
It helps in calculating various measures including error rate (FP+FN)/(P+N), specificity(TN/N), accuracy(TP+TN)/(P+N), sensitivity (TP/P), and precision( TP/(TP+FP) ).
A confusion matrix is essentially used to evaluate the performance of a machine learning model when the truth values of the experiments are already known and the target class has two or more than two categories of data. It helps in visualisation and evaluation of the results of the statistical process.
ββββββββββββββββββββ-
π΄If you wanna switch your career from any domain to Data Science OR Data Analytics OR Business Analytics then check our MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!
Course Linkππ» https://ai.cloudyml.com/Mega-Combo
π1
#Quiz
Which of the following evaluation metrics can not be applied in case of logistic regression output to compare with target?
Which of the following evaluation metrics can not be applied in case of logistic regression output to compare with target?
Anonymous Quiz
19%
AUC-ROC
21%
Accuracy
19%
Log Loss
41%
Mean-Squared-Error
π1
How to crack DELOITTE APTITUDE ROUND as a FRESHERπ§?
Check out our latest YouTube video in which we have covered aptitude questions (with explained solutions) asked by Deloitte for the role of Data Analyst. Link Below ππ»
β‘οΈ https://youtu.be/gjCl3VtOoNk
Check out our latest YouTube video in which we have covered aptitude questions (with explained solutions) asked by Deloitte for the role of Data Analyst. Link Below ππ»
β‘οΈ https://youtu.be/gjCl3VtOoNk
Today's Interview QnAs
Company - The Math Company
Role- ML Engineer
1.Explain Normal Distribution.
Normal Distribution is also called the Gaussian Distribution. It is a type of probability distribution such that most of the values lie near the mean. It has the following characteristics:
-The mean, median, and mode of the distribution coincide
-The distribution has a bell-shaped curve
-The total area under the curve is 1
-Exactly half of the values are to the right of the centre, and the other half to the left of the centre
2. Mention some drawbacks of the Linear Model.
Here a few drawbacks of the linear model:
-The assumption regarding the linearity of the errors
-It is not usable for binary outcomes or count outcome
-It canβt solve certain overfitting problem
-It also assumes that there is no multicollinearity in the data.
3.What are exploding gradients?
Exploding Gradients is the problematic scenario where large error gradients accumulate to result in very large updates to the weights of neural network models in the training stage. In an extreme case, the value of weights can overflow and result in NaN values. Hence the model becomes unstable and is unable to learn from the training data.
4.Explain the Law of Large Numbers.
The βLaw of Large Numbersβ states that if an experiment is repeated independently a large number of times, the average of the individual results is close to the expected value. It also states that the sample variance and standard deviation also converge towards the expected value.
ββββββββββββββββββββ-
π΄If you wanna switch your career from any domain to Data Science OR Data Analytics OR Business Analytics then check our MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!
Course Linkππ» https://ai.cloudyml.com/Mega-Combo
Company - The Math Company
Role- ML Engineer
1.Explain Normal Distribution.
Normal Distribution is also called the Gaussian Distribution. It is a type of probability distribution such that most of the values lie near the mean. It has the following characteristics:
-The mean, median, and mode of the distribution coincide
-The distribution has a bell-shaped curve
-The total area under the curve is 1
-Exactly half of the values are to the right of the centre, and the other half to the left of the centre
2. Mention some drawbacks of the Linear Model.
Here a few drawbacks of the linear model:
-The assumption regarding the linearity of the errors
-It is not usable for binary outcomes or count outcome
-It canβt solve certain overfitting problem
-It also assumes that there is no multicollinearity in the data.
3.What are exploding gradients?
Exploding Gradients is the problematic scenario where large error gradients accumulate to result in very large updates to the weights of neural network models in the training stage. In an extreme case, the value of weights can overflow and result in NaN values. Hence the model becomes unstable and is unable to learn from the training data.
4.Explain the Law of Large Numbers.
The βLaw of Large Numbersβ states that if an experiment is repeated independently a large number of times, the average of the individual results is close to the expected value. It also states that the sample variance and standard deviation also converge towards the expected value.
ββββββββββββββββββββ-
π΄If you wanna switch your career from any domain to Data Science OR Data Analytics OR Business Analytics then check our MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!
Course Linkππ» https://ai.cloudyml.com/Mega-Combo
π1
Know how you can answer SQL interview questions asked by Reliance!
Check out our latest YouTube video in which we have covered SQL questions (with explained solutions) asked by Reliance. Link Below ππ»
β‘οΈ https://youtu.be/g-alZaxxXyE
Check out our latest YouTube video in which we have covered SQL questions (with explained solutions) asked by Reliance. Link Below ππ»
β‘οΈ https://youtu.be/g-alZaxxXyE
π1
Today's Interview QnAs
Company - Numerator
Role - Data Analyst
1.How to share Power BI dashboards?
Power BI reports/dashboards can be shared in multiple ways. If you and your end-users have a Power BI Pro license, then-
-We are using the share option in reports and dashboards :This option enables access to a report or dashboard to individual users.
-Using content packs :Publish your report/dashboard along with the data set as a content pack and then share it with either a group or individual or open it for the entire organization.
-Publish your dashboards and reports into App Workspace and share the App link to a group or individual or open it for the entire organization. When publishing as App- we can select the reports and datasets that we want to be included in the App.
-The report can be embedded into Web or SharePoint, for which we need the embed code. This embed code is added to the website code or the SharePoint code.
Another way to share the reports is by printing or exporting the report as PPT or PDF. With these options, we can share the report, but they are not interactive.
2. What is Lock Aspect in Power BI?
Lock Aspect is basically used to lock the proportion of a visual when you try to drag through the corners to resize it. So, for example, if you create a visualization chart & turn the Lock Aspect on β and then try to resize it with the help of your mouse, it wonβt change, and it keeps proportions the same as earlier.
You can find the lock aspect in the Format Tab in the Visualization pane.
3. How to delete duplicate rows in excel?
Select the range of cells you want to remove duplicates from.
Go to the Data tab and select the Data Tools group.
Click Remove Duplicates.
Under columns, select one or more columns that contain duplicates.
Click ok to continue.
4. How to protect an excel sheet?
Select the worksheet you want to protect.
Navigate to the Review tab and click Protect Sheet.
A dialog box will open with the βAllow all users of this worksheet toβ list.
Check βProtect worksheet and contents of locked cells.β
Check the options you want, such as βSelect locked cells.β
Click OK to continue.
ββββββββββββββββββββ-
π΄If you wanna switch your career from any domain to Data Science OR Data Analytics OR Business Analytics then check our MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!
Course Linkππ» https://ai.cloudyml.com/Mega-Combo
Company - Numerator
Role - Data Analyst
1.How to share Power BI dashboards?
Power BI reports/dashboards can be shared in multiple ways. If you and your end-users have a Power BI Pro license, then-
-We are using the share option in reports and dashboards :This option enables access to a report or dashboard to individual users.
-Using content packs :Publish your report/dashboard along with the data set as a content pack and then share it with either a group or individual or open it for the entire organization.
-Publish your dashboards and reports into App Workspace and share the App link to a group or individual or open it for the entire organization. When publishing as App- we can select the reports and datasets that we want to be included in the App.
-The report can be embedded into Web or SharePoint, for which we need the embed code. This embed code is added to the website code or the SharePoint code.
Another way to share the reports is by printing or exporting the report as PPT or PDF. With these options, we can share the report, but they are not interactive.
2. What is Lock Aspect in Power BI?
Lock Aspect is basically used to lock the proportion of a visual when you try to drag through the corners to resize it. So, for example, if you create a visualization chart & turn the Lock Aspect on β and then try to resize it with the help of your mouse, it wonβt change, and it keeps proportions the same as earlier.
You can find the lock aspect in the Format Tab in the Visualization pane.
3. How to delete duplicate rows in excel?
Select the range of cells you want to remove duplicates from.
Go to the Data tab and select the Data Tools group.
Click Remove Duplicates.
Under columns, select one or more columns that contain duplicates.
Click ok to continue.
4. How to protect an excel sheet?
Select the worksheet you want to protect.
Navigate to the Review tab and click Protect Sheet.
A dialog box will open with the βAllow all users of this worksheet toβ list.
Check βProtect worksheet and contents of locked cells.β
Check the options you want, such as βSelect locked cells.β
Click OK to continue.
ββββββββββββββββββββ-
π΄If you wanna switch your career from any domain to Data Science OR Data Analytics OR Business Analytics then check our MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!
Course Linkππ» https://ai.cloudyml.com/Mega-Combo
π΄Data Analyst VS Data Scientist VS Business Analyst
Watch our latest YouTube video to decide which role you should choose for yourself. Link given below.
ππ» https://youtu.be/KhqQJM37tfE
Watch our latest YouTube video to decide which role you should choose for yourself. Link given below.
ππ» https://youtu.be/KhqQJM37tfE
SQL Interview QnA Hand Written Notes by CloudyML.pdf
3.2 MB
π¨Download Nowπ¨
Media is too big
VIEW IN TELEGRAM
Make your career in Data Science and Analytics from scratch with our Mega Combo Course which will give you complete Hands-on Practical Learning Experience with 1-1 Doubt Clearance Support Everyday. Visit Course Now for complete details.
Course link ππ» https://www.cloudyml.com/mega-combo-package/
Course link ππ» https://www.cloudyml.com/mega-combo-package/
π2
Watch our latest YouTube video to Build Logic for Python Interview.
In this video we have covered Python interview questions (with explained solution) asked by Google. Link given below.
Check Nowππ» https://youtu.be/n1RKze-eWiU
In this video we have covered Python interview questions (with explained solution) asked by Google. Link given below.
Check Nowππ» https://youtu.be/n1RKze-eWiU
π1
Today's Interview QnAs
Company - Accenture
Role- Data Analyst
1.What is the extension of the saved file in ms excel?
Microsoft Excel saved file has a .xls extension. The XLS-based extension has been used as the default format for Microsoft Excel 1997-2003. Files saved in the .xlsx extension are associated with Microsoft Excel 2007-10.
2.How to calculate EMI in excel?
EMI can be calculated in Excel using the simple formula β
βPMT (rate, nper, pv, [fv], [type])β
Where PMT β Payment;
Rate β the interest rate on the EMI;
Nper β total number of EMIs paid;
Pv β Present value or outstanding amount;
Fv (optional) β Future value or Cash balance;
Type (optional) β when payment is due. The default value is 0.
3. What is DAX?
Data Analysis Expressions (DAX) is a scripting language that is used to create calculated columns, measurements, and custom tables in Microsoft Power BI. It is a set of functions, operators, and constants that may be used to compute and return one or more values using a formula, or expression. You may utilise DAX to handle a variety of computations and data analysis challenges, allowing you to generate new data from existing data in your model.
4. How to connect JIRA to Power BI?
We connect to JIRA using JIRA content packs and JIRA Rest API. There are a few steps involved.
In order to connect JIRA to Power BI, we need an API token that can be generated from the Atlassian link.
Next, we download and install the Jira content pack. Then the API token is added to the Jira content pack.
Once the connection is set, it automatically creates a workspace with a dashboard and a set of reports to visually analyze the data.
ββββββββββββββββββββ-
π΄If you wanna switch your career from any domain to Data Science OR Data Analytics OR Business Analytics then check our MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!
Course Linkππ» https://ai.cloudyml.com/Mega-Combo
Company - Accenture
Role- Data Analyst
1.What is the extension of the saved file in ms excel?
Microsoft Excel saved file has a .xls extension. The XLS-based extension has been used as the default format for Microsoft Excel 1997-2003. Files saved in the .xlsx extension are associated with Microsoft Excel 2007-10.
2.How to calculate EMI in excel?
EMI can be calculated in Excel using the simple formula β
βPMT (rate, nper, pv, [fv], [type])β
Where PMT β Payment;
Rate β the interest rate on the EMI;
Nper β total number of EMIs paid;
Pv β Present value or outstanding amount;
Fv (optional) β Future value or Cash balance;
Type (optional) β when payment is due. The default value is 0.
3. What is DAX?
Data Analysis Expressions (DAX) is a scripting language that is used to create calculated columns, measurements, and custom tables in Microsoft Power BI. It is a set of functions, operators, and constants that may be used to compute and return one or more values using a formula, or expression. You may utilise DAX to handle a variety of computations and data analysis challenges, allowing you to generate new data from existing data in your model.
4. How to connect JIRA to Power BI?
We connect to JIRA using JIRA content packs and JIRA Rest API. There are a few steps involved.
In order to connect JIRA to Power BI, we need an API token that can be generated from the Atlassian link.
Next, we download and install the Jira content pack. Then the API token is added to the Jira content pack.
Once the connection is set, it automatically creates a workspace with a dashboard and a set of reports to visually analyze the data.
ββββββββββββββββββββ-
π΄If you wanna switch your career from any domain to Data Science OR Data Analytics OR Business Analytics then check our MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!
Course Linkππ» https://ai.cloudyml.com/Mega-Combo
π2
π¨Data_Science_Interview_QnA
Check our latest YouTube Video to Crack SQL interview Round of infosys for Data Science job roles . Link given below
ππ» https://youtu.be/J6TtVskUnOQ
Check our latest YouTube Video to Crack SQL interview Round of infosys for Data Science job roles . Link given below
ππ» https://youtu.be/J6TtVskUnOQ
Company- Musigma
Role- ML Engineer
1. Differentiate between univariate, bivariate, and multivariate analysis.
Univariate data, as the name suggests, contains only one variable. The univariate analysis describes the data and finds patterns that exist within it.
Bivariate data contains two different variables. The bivariate analysis deals with causes, relationships and analysis between those two variables.
Multivariate analysis involves analyzing multiple variables (more than two) to identify any possible association among them
2. What is dimensionality reduction? What are its benefits?
Dimensionality reduction is defined as the process of converting a data set with vast dimensions into data with lesser dimensions β in order to convey similar information concisely.
This method is mainly beneficial in compressing data and reducing storage space. It is also useful in reducing computation time due to fewer dimensions. Finally, it helps remove redundant features β for instance, storing a value in two different units (meters and inches) is avoided.
In short, dimensionality reduction is the process of reducing the number of random variables under consideration, by obtaining a set of principal variables. It can be divided into feature selection and feature extraction.
3. What is a ROC Curve? Explain how a ROC Curve works?
Ans. AUC β ROC curve is a performance measurement for the classification problem at various thresholds settings. ROC is a probability curve and AUC represents the degree or measure of separability. It tells how much model is capable of distinguishing between classes. Higher the AUC, better the model is at predicting 0s as 0s and 1s as 1s.
4. What are the various steps involved in an analytics project?
The steps involved in a text analytics project are:
-Data collection
-Data cleansing
-Data pre-processing
-Creation of train test and validation sets
-Model creation
-Hyperparameter tuning
-Model deployment
ββββββββββββββββββββ-
π¨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 6000+ HAPPY LEARNERS
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!
Course Linkππ» https://ai.cloudyml.com/Mega-Combo
Role- ML Engineer
1. Differentiate between univariate, bivariate, and multivariate analysis.
Univariate data, as the name suggests, contains only one variable. The univariate analysis describes the data and finds patterns that exist within it.
Bivariate data contains two different variables. The bivariate analysis deals with causes, relationships and analysis between those two variables.
Multivariate analysis involves analyzing multiple variables (more than two) to identify any possible association among them
2. What is dimensionality reduction? What are its benefits?
Dimensionality reduction is defined as the process of converting a data set with vast dimensions into data with lesser dimensions β in order to convey similar information concisely.
This method is mainly beneficial in compressing data and reducing storage space. It is also useful in reducing computation time due to fewer dimensions. Finally, it helps remove redundant features β for instance, storing a value in two different units (meters and inches) is avoided.
In short, dimensionality reduction is the process of reducing the number of random variables under consideration, by obtaining a set of principal variables. It can be divided into feature selection and feature extraction.
3. What is a ROC Curve? Explain how a ROC Curve works?
Ans. AUC β ROC curve is a performance measurement for the classification problem at various thresholds settings. ROC is a probability curve and AUC represents the degree or measure of separability. It tells how much model is capable of distinguishing between classes. Higher the AUC, better the model is at predicting 0s as 0s and 1s as 1s.
4. What are the various steps involved in an analytics project?
The steps involved in a text analytics project are:
-Data collection
-Data cleansing
-Data pre-processing
-Creation of train test and validation sets
-Model creation
-Hyperparameter tuning
-Model deployment
ββββββββββββββββββββ-
π¨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 6000+ HAPPY LEARNERS
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!
Course Linkππ» https://ai.cloudyml.com/Mega-Combo
Today's Interview QnA
Company Name - Cloudera
Role: Data Scientist
1. What are the assumptions required for linear regression? What if some of these assumptions are violated?
The assumptions are as follows:
The sample data used to fit the model is representative of the population
The relationship between X and the mean of Y is linear
The variance of the residual is the same for any value of X (homoscedasticity)
Observations are independent of each other
For any value of X, Y is normally distributed.
Extreme violations of these assumptions will make the results redundant. Small violations of these assumptions will result in a greater bias or variance of the estimate.
2.What is multicollinearity and how to remove it?
Multicollinearity exists when an independent variable is highly correlated with another independent variable in a multiple regression equation. This can be problematic because it undermines the statistical significance of an independent variable.
You could use the Variance Inflation Factors (VIF) to determine if there is any multicollinearity between independent variables β a standard benchmark is that if the VIF is greater than 5 then multicollinearity exists.
3. What is overfitting and how to prevent it?
Overfitting is an error where the model βfitsβ the data too well, resulting in a model with high variance and low bias. As a consequence, an overfit model will inaccurately predict new data points even though it has a high accuracy on the training data.
Few approaches to prevent overfitting are:
- Cross-Validation:Cross-validation is a powerful preventative measure against overfitting. Here we use our initial training data to generate multiple mini train-test splits. Now we use these splits to tune our model.
- Train with more data: It wonβt work every time, but training with more data can help algorithms detect the signal better or it can help my model to understand general trends in particular.
- We can remove irrelevant information or the noise from our dataset.
- Early Stopping: When youβre training a learning algorithm iteratively, you can measure how well each iteration of the model performs.
Up until a certain number of iterations, new iterations improve the model. After that point, however, the modelβs ability to generalize can weaken as it begins to overfit the training data.
Early stopping refers stopping the training process before the learner passes that point.
- Regularization: It refers to a broad range of techniques for artificially forcing your model to be simpler. There are mainly 3 types of Regularization techniques:L1, L2,&,Elastic- net.
- Ensembling : Here we take number of learners and using these we get strong model. They are of two types : Bagging and Boosting.
4. Given two fair dices, what is the probability of getting scores that sum to 4 and 8?
There are 4 combinations of rolling a 4 (1+3, 3+1, 2+2):
P(rolling a 4) = 3/36 = 1/12
There are 5 combinations of rolling an 8 (2+6, 6+2, 3+5, 5+3, 4+4):
P(rolling an 8) = 5/36
Company Name - Cloudera
Role: Data Scientist
1. What are the assumptions required for linear regression? What if some of these assumptions are violated?
The assumptions are as follows:
The sample data used to fit the model is representative of the population
The relationship between X and the mean of Y is linear
The variance of the residual is the same for any value of X (homoscedasticity)
Observations are independent of each other
For any value of X, Y is normally distributed.
Extreme violations of these assumptions will make the results redundant. Small violations of these assumptions will result in a greater bias or variance of the estimate.
2.What is multicollinearity and how to remove it?
Multicollinearity exists when an independent variable is highly correlated with another independent variable in a multiple regression equation. This can be problematic because it undermines the statistical significance of an independent variable.
You could use the Variance Inflation Factors (VIF) to determine if there is any multicollinearity between independent variables β a standard benchmark is that if the VIF is greater than 5 then multicollinearity exists.
3. What is overfitting and how to prevent it?
Overfitting is an error where the model βfitsβ the data too well, resulting in a model with high variance and low bias. As a consequence, an overfit model will inaccurately predict new data points even though it has a high accuracy on the training data.
Few approaches to prevent overfitting are:
- Cross-Validation:Cross-validation is a powerful preventative measure against overfitting. Here we use our initial training data to generate multiple mini train-test splits. Now we use these splits to tune our model.
- Train with more data: It wonβt work every time, but training with more data can help algorithms detect the signal better or it can help my model to understand general trends in particular.
- We can remove irrelevant information or the noise from our dataset.
- Early Stopping: When youβre training a learning algorithm iteratively, you can measure how well each iteration of the model performs.
Up until a certain number of iterations, new iterations improve the model. After that point, however, the modelβs ability to generalize can weaken as it begins to overfit the training data.
Early stopping refers stopping the training process before the learner passes that point.
- Regularization: It refers to a broad range of techniques for artificially forcing your model to be simpler. There are mainly 3 types of Regularization techniques:L1, L2,&,Elastic- net.
- Ensembling : Here we take number of learners and using these we get strong model. They are of two types : Bagging and Boosting.
4. Given two fair dices, what is the probability of getting scores that sum to 4 and 8?
There are 4 combinations of rolling a 4 (1+3, 3+1, 2+2):
P(rolling a 4) = 3/36 = 1/12
There are 5 combinations of rolling an 8 (2+6, 6+2, 3+5, 5+3, 4+4):
P(rolling an 8) = 5/36