Today's Interview QnAs
Company - Otis
Role- ML Engineer
1. How will you define the number of clusters in a clustering algorithm?
By determining the Silhouette score and elbow method, we determine the number of clusters in the algorithm.
2. What is Ensemble Learning? Define types.
Ensemble learning is clubbing of multiple weak learners (ml classifiers) and then using aggregation for result prediction. It is observed that even if the classifiers perform poorly individually, they do better when their results are aggregated. An example of ensemble learning is random forest classifier.
3.What are the support vectors in SVM?
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 maximise 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.
4.What is pruning in Decision Tree?
Pruning is the process of reducing the size of a decision tree. The reason for pruning is that the trees prepared by the base algorithm can be prone to overfitting as they become incredibly large and complex.
————————————————————-
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Company - Otis
Role- ML Engineer
1. How will you define the number of clusters in a clustering algorithm?
By determining the Silhouette score and elbow method, we determine the number of clusters in the algorithm.
2. What is Ensemble Learning? Define types.
Ensemble learning is clubbing of multiple weak learners (ml classifiers) and then using aggregation for result prediction. It is observed that even if the classifiers perform poorly individually, they do better when their results are aggregated. An example of ensemble learning is random forest classifier.
3.What are the support vectors in SVM?
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 maximise 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.
4.What is pruning in Decision Tree?
Pruning is the process of reducing the size of a decision tree. The reason for pruning is that the trees prepared by the base algorithm can be prone to overfitting as they become incredibly large and complex.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 6000+ HAPPY LEARNERS
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Today's Interview QnAs
Company - Numerator
Role- Data Analyst
1.How to create filters in Power BI?
Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.
Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.
Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)
2. What is MDX in Power BI?
MDX is an Analysis Services Tabular capability that is basically used to help improve the user experience in Excel and various other applications that use MDX query language against the tabular form of the models and Power BI datasets. To have a hands-on the MDX, try it by deploying your datasets in Power BI and then connecting with Excel by using analyze in Excel, the new Power BI Datasets within Excel feature, or you can connect to this dataset on Power BI Premium as well through the XMLA endpoint.
3.How to convert numbers to words in excel in rupees?
Select a cell adjacent to the cell you want the currency (Rupees) to be converted into words.
Enter the formula – NumberstoWords(A1) and press Enter. A1 is the cell containing the currency in Rupees.
Press Enter to confirm the formula.
4.How to round off in excel?
You can use the ROUNDUP function to round off in Excel. Here is how –
Select a blank cell.
Use the ROUND function by entering the syntax – “=ROUNDUP(number,num_digits).
Where number denotes the number to round up and num_digits refers to a number of digits to which it should be rounded up.
Press Return.
Using the button, you can round off in Excel by following these steps –
Select the cells you want to round off.
Go to the Home tab, click Increase Decimal or Decrease Decimal.
Press Enter.
————————————————————-
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company - Numerator
Role- Data Analyst
1.How to create filters in Power BI?
Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.
Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.
Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)
2. What is MDX in Power BI?
MDX is an Analysis Services Tabular capability that is basically used to help improve the user experience in Excel and various other applications that use MDX query language against the tabular form of the models and Power BI datasets. To have a hands-on the MDX, try it by deploying your datasets in Power BI and then connecting with Excel by using analyze in Excel, the new Power BI Datasets within Excel feature, or you can connect to this dataset on Power BI Premium as well through the XMLA endpoint.
3.How to convert numbers to words in excel in rupees?
Select a cell adjacent to the cell you want the currency (Rupees) to be converted into words.
Enter the formula – NumberstoWords(A1) and press Enter. A1 is the cell containing the currency in Rupees.
Press Enter to confirm the formula.
4.How to round off in excel?
You can use the ROUNDUP function to round off in Excel. Here is how –
Select a blank cell.
Use the ROUND function by entering the syntax – “=ROUNDUP(number,num_digits).
Where number denotes the number to round up and num_digits refers to a number of digits to which it should be rounded up.
Press Return.
Using the button, you can round off in Excel by following these steps –
Select the cells you want to round off.
Go to the Home tab, click Increase Decimal or Decrease Decimal.
Press Enter.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 6000+ HAPPY LEARNERS
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
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Today's Interview QnAs
Company - Biocon
Role- Data Scientist
1. How do you analyse the performance of the predictions generated by regression models versus classification models?
In the regression model, the most commonly known evaluation metrics include:
• R-squared (R2)
• Root Mean Squared Error (RMSE)
• Residual Standard Error (RSE)
• Mean Absolute Error (MAE)
Classification is the problem of identifying to which of a set of categories/classes a new observation belongs, based on the training set of data containing records whose class label is known. Following are the performance metrics used for evaluating a classification model:
• Accuracy
• Precision and Recall
• Specificity
• F1-score
• AUC-ROC
2. How do you assess logistic regression versus simple linear regression models?
• Linear Regression is used to handle regression problems whereas Logistic regression is used to handle the classification problems.
• Linear regression provides a continuous output but Logistic regression provides discreet output(actually it gives continuous probability output which is then converted into discreet values I.e. classes). • The purpose of Linear Regression is to find the best-fitted line while Logistic regression is one step ahead and fitting the line values to the sigmoid curve.
• The method for calculating loss function in linear regression is the mean squared error whereas for logistic regression it is maximum likelihood estimation.
3. What is cross-validation and why would you use it?
Cross-validation is a statistical method used to estimate the performance (or accuracy) of machine learning models. It is used to protect against overfitting in a predictive model, particularly in a case where the amount of data may be limited. In cross-validation, you make a fixed number of folds (or partitions) of the data, run the analysis on each fold, and then average the overall error estimate.
4. What’s the name of the matrix used to evaluate predictive models?
Confusion Matrix: A confusion matrix is a tabular summary of the number of correct and incorrect predictions made by a classifier. 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.
————————————————————-
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company - Biocon
Role- Data Scientist
1. How do you analyse the performance of the predictions generated by regression models versus classification models?
In the regression model, the most commonly known evaluation metrics include:
• R-squared (R2)
• Root Mean Squared Error (RMSE)
• Residual Standard Error (RSE)
• Mean Absolute Error (MAE)
Classification is the problem of identifying to which of a set of categories/classes a new observation belongs, based on the training set of data containing records whose class label is known. Following are the performance metrics used for evaluating a classification model:
• Accuracy
• Precision and Recall
• Specificity
• F1-score
• AUC-ROC
2. How do you assess logistic regression versus simple linear regression models?
• Linear Regression is used to handle regression problems whereas Logistic regression is used to handle the classification problems.
• Linear regression provides a continuous output but Logistic regression provides discreet output(actually it gives continuous probability output which is then converted into discreet values I.e. classes). • The purpose of Linear Regression is to find the best-fitted line while Logistic regression is one step ahead and fitting the line values to the sigmoid curve.
• The method for calculating loss function in linear regression is the mean squared error whereas for logistic regression it is maximum likelihood estimation.
3. What is cross-validation and why would you use it?
Cross-validation is a statistical method used to estimate the performance (or accuracy) of machine learning models. It is used to protect against overfitting in a predictive model, particularly in a case where the amount of data may be limited. In cross-validation, you make a fixed number of folds (or partitions) of the data, run the analysis on each fold, and then average the overall error estimate.
4. What’s the name of the matrix used to evaluate predictive models?
Confusion Matrix: A confusion matrix is a tabular summary of the number of correct and incorrect predictions made by a classifier. 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.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 6000+ HAPPY LEARNERS
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
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Watch our latest YouTube video to crack Walmart Aptitude Round for Data Science Roles.
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Today's Interview QnAs
Company - Google
Role- Jr.ML Engineer
1.How will you handle missing values in data?
There are several ways to handle missing values in the given data-
1.Dropping the values
2.Deleting the observation (not always recommended).
3.Replacing value with the mean, median and mode of the observation.
4.Predicting value with regression
5.Finding appropriate value with clustering
2. What is SVM? Can you name some kernels used in SVM?
SVM stands for support vector machine. They are used for classification and prediction tasks. SVM consists of a separating plane that discriminates between the two classes of variables. This separating plane is known as hyperplane. Some of the kernels used in SVM are –
Polynomial Kernel
Gaussian Kernel
Laplace RBF Kernel
Sigmoid Kernel
Hyperbolic Kernel
3.What is market basket analysis?
Market Basket Analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.
4.What is the benefit of batch normalization?
The model is less sensitive to hyperparameter tuning.
High learning rates become acceptable, which results in faster training of the model.
Weight initialization becomes an easy task.
Using different non-linear activation functions becomes feasible.
Deep neural networks are simplified because of batch normalization.
It introduces mild regularisation in the network.
Company - Google
Role- Jr.ML Engineer
1.How will you handle missing values in data?
There are several ways to handle missing values in the given data-
1.Dropping the values
2.Deleting the observation (not always recommended).
3.Replacing value with the mean, median and mode of the observation.
4.Predicting value with regression
5.Finding appropriate value with clustering
2. What is SVM? Can you name some kernels used in SVM?
SVM stands for support vector machine. They are used for classification and prediction tasks. SVM consists of a separating plane that discriminates between the two classes of variables. This separating plane is known as hyperplane. Some of the kernels used in SVM are –
Polynomial Kernel
Gaussian Kernel
Laplace RBF Kernel
Sigmoid Kernel
Hyperbolic Kernel
3.What is market basket analysis?
Market Basket Analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.
4.What is the benefit of batch normalization?
The model is less sensitive to hyperparameter tuning.
High learning rates become acceptable, which results in faster training of the model.
Weight initialization becomes an easy task.
Using different non-linear activation functions becomes feasible.
Deep neural networks are simplified because of batch normalization.
It introduces mild regularisation in the network.
👍7
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Role: Freshers- Business/Data Analytics
Company : 4onlinemedia
Exp : 0-3 yr
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Role: Data Analyst Trainee
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Company : Porter.In
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Role: Freshers- Business/Data Analytics
Company : 4onlinemedia
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Role: Data Analyst Trainee
Company : Quickxpert Infotech
Exp - 0-3 yr
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Role: Data Analyst
Company: G A Content Solutions
Exp - 0-2 yr
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Role: Business Analyst
Company : Porter.In
Exp - 0-1.5 yr
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Company: Schlumberger India Technology Centre Pvt Ltd
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Today's Interview QnAs
Company -Spins
Role- Data Analyst
1.How to calculate age in excel?
The easiest way to calculate age in MS Excel is using the formula –
“=DATEDIF(birth_date,as_of_date,”y”)”
The result will be the number of complete years.
2. What is macro in excel?
Macro refers to an algorithm or a set of actions that help automate a task in Excel by recording and playing back the steps taken to complete that task. Once the steps are stored, you create a Macro, and it can be edited and played back as many times as the user wants.
Macro is great for repetitive tasks and also eliminates errors. For example, suppose an account manager has to share reports regarding the company employees for non-payment of dues. In that case, it can be automated using a Macro and doing minor changes every month, as needed.
3.What is the Cartesian product of the table?
The output of Cross Join is called a Cartesian product. It returns rows combining each row from the first table with each row of the second table. For Example, if we join two tables having 15 and 20 columns the Cartesian product of two tables will be 15×20=300 rows.
4. Difference between NVL and NVL2 functions?
Both the NVL(exp1, exp2) and NVL2(exp1, exp2, exp3) functions check the value exp1 to see if it is null. With the NVL(exp1, exp2) function, if exp1 is not null, then the value of exp1 is returned; otherwise, the value of exp2 is returned, but case to the same data type as that of exp1. With the NVL2(exp1, exp2, exp3) function, if exp1 is not null, then exp2 is returned; otherwise, the value of exp3 is returned.
————————————————————-
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company -Spins
Role- Data Analyst
1.How to calculate age in excel?
The easiest way to calculate age in MS Excel is using the formula –
“=DATEDIF(birth_date,as_of_date,”y”)”
The result will be the number of complete years.
2. What is macro in excel?
Macro refers to an algorithm or a set of actions that help automate a task in Excel by recording and playing back the steps taken to complete that task. Once the steps are stored, you create a Macro, and it can be edited and played back as many times as the user wants.
Macro is great for repetitive tasks and also eliminates errors. For example, suppose an account manager has to share reports regarding the company employees for non-payment of dues. In that case, it can be automated using a Macro and doing minor changes every month, as needed.
3.What is the Cartesian product of the table?
The output of Cross Join is called a Cartesian product. It returns rows combining each row from the first table with each row of the second table. For Example, if we join two tables having 15 and 20 columns the Cartesian product of two tables will be 15×20=300 rows.
4. Difference between NVL and NVL2 functions?
Both the NVL(exp1, exp2) and NVL2(exp1, exp2, exp3) functions check the value exp1 to see if it is null. With the NVL(exp1, exp2) function, if exp1 is not null, then the value of exp1 is returned; otherwise, the value of exp2 is returned, but case to the same data type as that of exp1. With the NVL2(exp1, exp2, exp3) function, if exp1 is not null, then exp2 is returned; otherwise, the value of exp3 is returned.
————————————————————-
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https://youtu.be/J15lB-irfVI
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Anonymous Poll
22%
Zero Knowledge
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Beginner
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Intermediate
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Advanced
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Today's Interview QnAs
Company - Spinny
Role- Data Scientist
1. How would you deal with missing random values from a data set?
There are two forms of randomly missing values:
MCAR or Missing completely at random. Such errors happen when the missing values are randomly distributed across all observations.
We can confirm this error by partitioning the data into two parts –
One set with the missing values
Another set with the non-missing values.
After we have partitioned the data, we conduct a t-test of mean difference to check if there is any difference in the sample between the two data sets.
In case the data are MCAR, we may choose a pair-wise or a list-wise deletion of missing value cases.
MAR or Missing at random. It is a common occurrence. Here, the missing values are not randomly distributed across observations but are distributed within one or more sub-samples. We cannot predict the probability from the variables in the model. Data imputation is mainly performed to replace them.
2. Explain the difference between lists and tuples.
Both lists and tuples are made up of elements, which are values of any Python data type. However, these data types have a number of differences:
Lists are mutable, while tuples are immutable.
Lists are created in brackets (for example, my_list = [a, b, c]), while tuples are in parentheses (for example, my_tuple = (a, b, c)).
3.What is ‘fsck’?
‘fsck ‘ abbreviation for ‘ file system check.’ It is a type of command that searches for possible errors in the file. fsck generates a summary report, which lists the file system’s overall health and sends it to the Hadoop distributed file system.
4.What is method to convert date-strings to timeseries in a series.
Input:
s = pd.Series([’22 Feb 1984′, ’22-02-2013′, ‘20170105’, ‘2012/02/08’, ‘2016-11-04’, ‘2015-03-02T11:15])
We will use the to_datetime() function
pd.to_datetime(s)
————————————————————-
🚨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 Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company - Spinny
Role- Data Scientist
1. How would you deal with missing random values from a data set?
There are two forms of randomly missing values:
MCAR or Missing completely at random. Such errors happen when the missing values are randomly distributed across all observations.
We can confirm this error by partitioning the data into two parts –
One set with the missing values
Another set with the non-missing values.
After we have partitioned the data, we conduct a t-test of mean difference to check if there is any difference in the sample between the two data sets.
In case the data are MCAR, we may choose a pair-wise or a list-wise deletion of missing value cases.
MAR or Missing at random. It is a common occurrence. Here, the missing values are not randomly distributed across observations but are distributed within one or more sub-samples. We cannot predict the probability from the variables in the model. Data imputation is mainly performed to replace them.
2. Explain the difference between lists and tuples.
Both lists and tuples are made up of elements, which are values of any Python data type. However, these data types have a number of differences:
Lists are mutable, while tuples are immutable.
Lists are created in brackets (for example, my_list = [a, b, c]), while tuples are in parentheses (for example, my_tuple = (a, b, c)).
3.What is ‘fsck’?
‘fsck ‘ abbreviation for ‘ file system check.’ It is a type of command that searches for possible errors in the file. fsck generates a summary report, which lists the file system’s overall health and sends it to the Hadoop distributed file system.
4.What is method to convert date-strings to timeseries in a series.
Input:
s = pd.Series([’22 Feb 1984′, ’22-02-2013′, ‘20170105’, ‘2012/02/08’, ‘2016-11-04’, ‘2015-03-02T11:15])
We will use the to_datetime() function
pd.to_datetime(s)
————————————————————-
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
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Know the fundamentals of CNN using Cifar-10 Dataset in our latest YouTube video. Link given below 👇🏻
https://youtu.be/heWtPqkgb44
Know the fundamentals of CNN using Cifar-10 Dataset in our latest YouTube video. Link given below 👇🏻
https://youtu.be/heWtPqkgb44
👍3
Today's Interview QnAs
Company - MuSigma
Role- ML Engineer
1.What are techniques used for sampling?
There are various methods for drawing samples from data.
The two main Sampling techniques are
-Probability sampling
-Non-probability sampling
Probability sampling:
Probability sampling means that each individual of the population has a possibility of being included in the sample. Probability sampling methods include –
-Simple random sampling:
In simple random sampling, each individual of the population has an equivalent chance of being selected or included.
-Systematic sampling:
Systematic sampling is very much similar to random sampling. The difference is just that instead of randomly generating numbers, in systematic sampling every individual of the population is assigned a number and are chosen at regular intervals.
-Stratified sampling:
In stratified sampling, the population is split into sub-populations. It allows you to conclude more precise results by ensuring that every sub-population is represented in the sample.
-Cluster sampling:
Cluster sampling also involves dividing the population into sub-populations, but each subpopulation should have analogous characteristics to that of the whole sample. Rather than sampling individuals from each subpopulation, you randomly select the entire subpopulation.
-Non-probability sampling :
In non-probability sampling, individuals are selected using non-random ways and not every individual has a possibility of being included in the sample.
-Convenience sampling:
Convenience sampling is a method where data is collected from an easily accessible group.
-Voluntary Response sampling:
Voluntary Response sampling is similar to convenience sampling, but here instead of researchers choosing individuals and then contacting them, people or individuals volunteer themselves.
-Purposive sampling:
Purposive sampling also known as judgmental sampling is where the researchers use their expertise to select a sample that is useful or relevant to the purpose of the research.
-Snowball sampling:
Snowball sampling is used where the population is difficult to access. It can be used to recruit individuals via other individuals.
2.How to get the minimum, 25th percentile, median, 75th, and max of a numeric series?
"randomness= np.random.RandomState(100)
s = pd.Series(randomness.normal(100, 55, 5))
np.percentile(ser, q=[0, 25, 50, 75, 100])"
3.What is init in Python?
“init” is a reserved method in python classes. It is known as a constructor in object-oriented concepts. This method is called when an object is created from the class and it allows the class to initialise the attributes of the class.
4.Explain the Law of Large Numbers.
The ‘Law of Large Numbers’ 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.
Company - MuSigma
Role- ML Engineer
1.What are techniques used for sampling?
There are various methods for drawing samples from data.
The two main Sampling techniques are
-Probability sampling
-Non-probability sampling
Probability sampling:
Probability sampling means that each individual of the population has a possibility of being included in the sample. Probability sampling methods include –
-Simple random sampling:
In simple random sampling, each individual of the population has an equivalent chance of being selected or included.
-Systematic sampling:
Systematic sampling is very much similar to random sampling. The difference is just that instead of randomly generating numbers, in systematic sampling every individual of the population is assigned a number and are chosen at regular intervals.
-Stratified sampling:
In stratified sampling, the population is split into sub-populations. It allows you to conclude more precise results by ensuring that every sub-population is represented in the sample.
-Cluster sampling:
Cluster sampling also involves dividing the population into sub-populations, but each subpopulation should have analogous characteristics to that of the whole sample. Rather than sampling individuals from each subpopulation, you randomly select the entire subpopulation.
-Non-probability sampling :
In non-probability sampling, individuals are selected using non-random ways and not every individual has a possibility of being included in the sample.
-Convenience sampling:
Convenience sampling is a method where data is collected from an easily accessible group.
-Voluntary Response sampling:
Voluntary Response sampling is similar to convenience sampling, but here instead of researchers choosing individuals and then contacting them, people or individuals volunteer themselves.
-Purposive sampling:
Purposive sampling also known as judgmental sampling is where the researchers use their expertise to select a sample that is useful or relevant to the purpose of the research.
-Snowball sampling:
Snowball sampling is used where the population is difficult to access. It can be used to recruit individuals via other individuals.
2.How to get the minimum, 25th percentile, median, 75th, and max of a numeric series?
"randomness= np.random.RandomState(100)
s = pd.Series(randomness.normal(100, 55, 5))
np.percentile(ser, q=[0, 25, 50, 75, 100])"
3.What is init in Python?
“init” is a reserved method in python classes. It is known as a constructor in object-oriented concepts. This method is called when an object is created from the class and it allows the class to initialise the attributes of the class.
4.Explain the Law of Large Numbers.
The ‘Law of Large Numbers’ 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.
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🚨Python Interview Question asked in Bridgei2i for Data Scientist Role
👉🏻 https://youtu.be/p1ve2BDnmpM
"Write a program to find the number of occurrences of elements from list1 to list2
."
Watch our YouTube Video to get explained solution of this Python Coding Round Question. Link given below👉🏻 https://youtu.be/p1ve2BDnmpM
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Today's Interview QnAs
Company - Spinny
Role - Data Analyst
1.How to add drop down in excel?
Here is how to create a drop-down list in Excel –
Select the cells you want to contain in the drop-down list.
Go to ribbon and click “Data,” then “Data Validation.”
In the dialog box, select “Allow to List.”
Click on Source and type the numbers or text that you want in your drop-down lists.
Click OK to continue.
Note that commas should separate the numbers and texts.
2. What are the types of SQL Queries?
We have four types of SQL Queries:
DDL (Data Definition Language): the creation of objects
DML (Data Manipulation Language): manipulation of data
DCL (Data Control Language): assignment and removal of permissions
TCL (Transaction Control Language): saving and restoring changes to a database
3.What are the various refresh options available in Power BI?
There are essentially four major types of refresh options available in Power BI
Package refresh
Model/data refresh
Tile Refresh
Visual container refresh
4.What is bin in tableau?
Bins in tableau are containers of equal size used to store data values fitting in bin size. In other words, bins group the data into groups of equal size or data which can be used in systematic viewing of data. All the discrete fields in tableau can also be considered as set of bins.
————————————————————-
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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 Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company - Spinny
Role - Data Analyst
1.How to add drop down in excel?
Here is how to create a drop-down list in Excel –
Select the cells you want to contain in the drop-down list.
Go to ribbon and click “Data,” then “Data Validation.”
In the dialog box, select “Allow to List.”
Click on Source and type the numbers or text that you want in your drop-down lists.
Click OK to continue.
Note that commas should separate the numbers and texts.
2. What are the types of SQL Queries?
We have four types of SQL Queries:
DDL (Data Definition Language): the creation of objects
DML (Data Manipulation Language): manipulation of data
DCL (Data Control Language): assignment and removal of permissions
TCL (Transaction Control Language): saving and restoring changes to a database
3.What are the various refresh options available in Power BI?
There are essentially four major types of refresh options available in Power BI
Package refresh
Model/data refresh
Tile Refresh
Visual container refresh
4.What is bin in tableau?
Bins in tableau are containers of equal size used to store data values fitting in bin size. In other words, bins group the data into groups of equal size or data which can be used in systematic viewing of data. All the discrete fields in tableau can also be considered as set of bins.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ 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 Link👉🏻 https://ai.cloudyml.com/Mega-Combo
👍10
Stats interview hand written notes by CloudyML.pdf
3 MB
Statistics Interview QnA Notes by CloudyML.
Download Now!
Download Now!
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If you are preparing for Data Science and Analytics domain and want to get confidence in your skills and to get complete hands on practical learning from scratch then check our Mega Combo Course.
It Covers all the important topics which are required to become interview ready from scratch with 1-1 Live Doubt Clearance Support.
✅Python
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✅Maths & Statistics
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✅Deep Learning
✅AWS Sagamaker
✅Model Deployment
✅Tableau
✅PowerBi
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Stay Safe and Happy Learning 💙
It Covers all the important topics which are required to become interview ready from scratch with 1-1 Live Doubt Clearance Support.
✅Python
✅Advanced SQL
✅Maths & Statistics
✅Machine Learning
✅Deep Learning
✅AWS Sagamaker
✅Model Deployment
✅Tableau
✅PowerBi
✅MS Excel
✅Interview QnA PDFs
✅Capstone Projects
Combined Price of all the courses is Rs. 21488/- but you can get all the courses at special Mega Combo offer of Rs.6999/- for today
Follow the link below and visit our website. Check reviews of our learners and then decide.
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Stay Safe and Happy Learning 💙
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