Date: 30-12-2022
Company name: Cloudera
Role: Data Analyst
Topic: rank vs dense rank, one hot encoding , label encoding, tableau worksheets , splitting column
1. What is the difference between the RANK() and DENSE_RANK() functions?
The RANK() function in the result set defines the rank of each row within your ordered partition. If both rows have the same rank, the next number in the ranking will be the previous rank plus a number of duplicates. If we have three records at rank 4, for example, the next level indicated is 7. The DENSE_RANK() function assigns a distinct rank to each row within a partition based on the provided column value, with no gaps. If we have three records at rank 4, for example, the next level indicated is 5.
2. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?
One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesnβt affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.
3. Explain the Difference Between Tableau Worksheet, Dashboard, Story, and Workbook in Tableau?
Tableau uses a workbook and sheet file structure, much like Microsoft Excel.
A workbook contains sheets, which can be a worksheet, dashboard, or a story.
A worksheet contains a single view along with shelves, legends, and the Data pane.
A dashboard is a collection of views from multiple worksheets.
A story contains a sequence of worksheets or dashboards that work together to convey information.
4. How can you split a column into 2 or more columns?
You can split a column into 2 or more columns by following the below steps:
1. Select the cell that you want to split. Then, navigate to the Data tab, after that, select Text to Columns. 2. Select the delimiter. 3. Choose the column data format and select the destination you want to display the split. 4. The final output will look like below where the text is split into multiple columns.
5. Do you wanna make your career in Data Science & Analytics but don't know how to start ?
Get a complete roadmap from scratch that will make you technically strong enough to crack any Data Scientist / Analyst and also learn Pro Career Growth Hacks to land on your Dream Job.
Check India's Most Affordable Industry Oriented Course, chosen & trusted by 12000+ happy learners. Link Belowππ»
β‘οΈhttps://www.cloudyml.com/all-in-one-super-combo/
Happy Learning π
Company name: Cloudera
Role: Data Analyst
Topic: rank vs dense rank, one hot encoding , label encoding, tableau worksheets , splitting column
1. What is the difference between the RANK() and DENSE_RANK() functions?
The RANK() function in the result set defines the rank of each row within your ordered partition. If both rows have the same rank, the next number in the ranking will be the previous rank plus a number of duplicates. If we have three records at rank 4, for example, the next level indicated is 7. The DENSE_RANK() function assigns a distinct rank to each row within a partition based on the provided column value, with no gaps. If we have three records at rank 4, for example, the next level indicated is 5.
2. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?
One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesnβt affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.
3. Explain the Difference Between Tableau Worksheet, Dashboard, Story, and Workbook in Tableau?
Tableau uses a workbook and sheet file structure, much like Microsoft Excel.
A workbook contains sheets, which can be a worksheet, dashboard, or a story.
A worksheet contains a single view along with shelves, legends, and the Data pane.
A dashboard is a collection of views from multiple worksheets.
A story contains a sequence of worksheets or dashboards that work together to convey information.
4. How can you split a column into 2 or more columns?
You can split a column into 2 or more columns by following the below steps:
1. Select the cell that you want to split. Then, navigate to the Data tab, after that, select Text to Columns. 2. Select the delimiter. 3. Choose the column data format and select the destination you want to display the split. 4. The final output will look like below where the text is split into multiple columns.
5. Do you wanna make your career in Data Science & Analytics but don't know how to start ?
Get a complete roadmap from scratch that will make you technically strong enough to crack any Data Scientist / Analyst and also learn Pro Career Growth Hacks to land on your Dream Job.
Check India's Most Affordable Industry Oriented Course, chosen & trusted by 12000+ happy learners. Link Belowππ»
β‘οΈhttps://www.cloudyml.com/all-in-one-super-combo/
Happy Learning π
V2 - PowerBI - Dashboards
Check out our latest PowerBi Course first video on YouTube in Hindi. Link given below
Link - https://youtu.be/YFRysi5ula0
Stay Safe & Happy Learning π
Check out our latest PowerBi Course first video on YouTube in Hindi. Link given below
Link - https://youtu.be/YFRysi5ula0
Stay Safe & Happy Learning π
Date: 31-12-2022
Company name: CRED
Role: Data Scientist
Topic: sets & groups, bag of word, nested triggers, TPR vs FPR
1. What do Tableau's sets and groups mean?
Data is grouped using sets and groups according to predefined criteria. The primary distinction between the two is that although a set can have only two optionsβeither in or outβa group can divide the dataset into several groups. A user should decide which group or sets to apply based on the conditions.
3.What do you mean by a Bag of Words (BOW)?
It is used for word frequency or occurrences to train a classifier.
It contains a text representation that describes the frequency with which words appear in a document.
It has two steps:
-A list of terms that are well-known.
-A metric for determining the existence of well-known terms.
3. What are Nested Triggers?
Triggers may implement DML by using INSERT, UPDATE, and DELETE statements. These triggers that contain DML and find other triggers for data modification are called Nested Triggers.
4. What is a True positive rate and a false positive rate?
True positive rate or Recall: It gives us the percentage of the true positives captured by the model out of all the Actual Positive class.
TPR = TP/ (TP+FN)
False Positive rate: It gives us the percentage of all the false positives by my model prediction from the all Actual Negative class.
FPR = FP/(FP+TN)
Company name: CRED
Role: Data Scientist
Topic: sets & groups, bag of word, nested triggers, TPR vs FPR
1. What do Tableau's sets and groups mean?
Data is grouped using sets and groups according to predefined criteria. The primary distinction between the two is that although a set can have only two optionsβeither in or outβa group can divide the dataset into several groups. A user should decide which group or sets to apply based on the conditions.
3.What do you mean by a Bag of Words (BOW)?
It is used for word frequency or occurrences to train a classifier.
It contains a text representation that describes the frequency with which words appear in a document.
It has two steps:
-A list of terms that are well-known.
-A metric for determining the existence of well-known terms.
3. What are Nested Triggers?
Triggers may implement DML by using INSERT, UPDATE, and DELETE statements. These triggers that contain DML and find other triggers for data modification are called Nested Triggers.
4. What is a True positive rate and a false positive rate?
True positive rate or Recall: It gives us the percentage of the true positives captured by the model out of all the Actual Positive class.
TPR = TP/ (TP+FN)
False Positive rate: It gives us the percentage of all the false positives by my model prediction from the all Actual Negative class.
FPR = FP/(FP+TN)
π4β€1
Make your career in Data Science & Analytics without paying lakhs of rupees. Join India's #1 Most Affordable Industry Oriented Course.
This is the last chance to get this SUPER COMBO COURSE at 80% Discount.
APPLY NOW β‘οΈ https://www.cloudyml.com/all-in-one-super-combo/
Hurry Up! Offer Ending Tonight!
This is the last chance to get this SUPER COMBO COURSE at 80% Discount.
APPLY NOW β‘οΈ https://www.cloudyml.com/all-in-one-super-combo/
Hurry Up! Offer Ending Tonight!
π3
Date: 02-01-2023
Company name: Kenstar
Role: Data Scientist
Topic: dbscan, break continue, relationship in SQL, rnn
1. How many types of points do we get after applying a DBSCAN Algorithm to a particular dataset?
We get three types of points upon applying a DBSCAN algorithm to a particular dataset β Core point, Border point, and noise point.
Core Point: A data point is considered to be a core point if it has a minimum number of neighboring data points (min_pts) at an epsilon distance from it. These min_pts include the original data points also.
Border Point: A data point that has less than the minimum number of data points needed but has at least one core point in the neighborhood.
Noise Point: A data point that is not a core point or a border point is considered noise or an outlier.
2. List the different types of relationships in SQL.
One-to-One - This can be defined as the relationship between two tables where each record in one table is associated with the maximum of one record in the other table.
One-to-Many & Many-to-One - This is the most commonly used relationship where a record in a table is associated with multiple records in the other table.
Many-to-Many - This is used in cases when multiple instances on both sides are needed for defining a relationship.
Self-Referencing Relationships - This is used when a table needs to define a relationship with itself.
3. What are the main difficulties when training RNNs? How can you handle them?
The two main difficulties when training RNNs are unstable gradients (exploding or vanishing) and a very limited short-term memory. These problems both get worse when dealing with long sequences.
To alleviate the unstable gradients problem, we can:
Use a smaller learning rate.
Use a saturating activation function such as the hyperbolic tangent (which is the default), and possibly use gradient clipping, Layer Normalization, or dropout at each time step.
To tackle the limited short-term memory problem, we can use a Long Short-Term Memory layer or a Gated recurrent unit layer.
4. How does break, continue and pass work in python?
Break: Allows loop termination when some condition is met and the control is transferred to the next statement.
Continue: Allows skipping some part of a loop when some specific condition is met and the control is transferred to the beginning of the loop.
Pass: Used when you need some block of code syntactically, but you want to skip its execution. This is basically a null operation. Nothing happens when this is executed.
JOIN THIS CHANNEL IF YOU ARE NEWπ
Company name: Kenstar
Role: Data Scientist
Topic: dbscan, break continue, relationship in SQL, rnn
1. How many types of points do we get after applying a DBSCAN Algorithm to a particular dataset?
We get three types of points upon applying a DBSCAN algorithm to a particular dataset β Core point, Border point, and noise point.
Core Point: A data point is considered to be a core point if it has a minimum number of neighboring data points (min_pts) at an epsilon distance from it. These min_pts include the original data points also.
Border Point: A data point that has less than the minimum number of data points needed but has at least one core point in the neighborhood.
Noise Point: A data point that is not a core point or a border point is considered noise or an outlier.
2. List the different types of relationships in SQL.
One-to-One - This can be defined as the relationship between two tables where each record in one table is associated with the maximum of one record in the other table.
One-to-Many & Many-to-One - This is the most commonly used relationship where a record in a table is associated with multiple records in the other table.
Many-to-Many - This is used in cases when multiple instances on both sides are needed for defining a relationship.
Self-Referencing Relationships - This is used when a table needs to define a relationship with itself.
3. What are the main difficulties when training RNNs? How can you handle them?
The two main difficulties when training RNNs are unstable gradients (exploding or vanishing) and a very limited short-term memory. These problems both get worse when dealing with long sequences.
To alleviate the unstable gradients problem, we can:
Use a smaller learning rate.
Use a saturating activation function such as the hyperbolic tangent (which is the default), and possibly use gradient clipping, Layer Normalization, or dropout at each time step.
To tackle the limited short-term memory problem, we can use a Long Short-Term Memory layer or a Gated recurrent unit layer.
4. How does break, continue and pass work in python?
Break: Allows loop termination when some condition is met and the control is transferred to the next statement.
Continue: Allows skipping some part of a loop when some specific condition is met and the control is transferred to the beginning of the loop.
Pass: Used when you need some block of code syntactically, but you want to skip its execution. This is basically a null operation. Nothing happens when this is executed.
π11π11β€1
π96π6π€©4π2
Date: 04-01-2023
Company name: Numerator
Role: Data Scientist
Topic : RNN, pop & remove, calculated field, views
1. What are the uses of using RNN in NLP?
The RNN is a stateful neural network, which means that it not only retains information from the previous layer but also from the previous pass. Thus, this neuron is said to have connections between passes, and through time.
For the RNN the order of the input matters due to being stateful. The same words with different orders will yield different outputs.
RNN can be used for unsegmented, connected applications such as handwriting recognition or speech recognition.
2. How to remove values to a python array?
Ans: Array elements can be removed using pop() or remove() method. The difference between these two functions is that the former returns the deleted value whereas the latter does not.
3. What are the advantages and disadvantages of views in the database?
Answer: Advantages of Views:
As there is no physical location where the data in the view is stored, it generates output without wasting resources.
Data access is restricted as it does not allow commands like insertion, updation, and deletion.
Disadvantages of Views:
The view becomes irrelevant if we drop a table related to that view.
Much memory space is occupied when the view is created for large tables.
4. How to create a calculated field in Tableau?
Click the drop down to the right of Dimensions on the Data pane and select βCreate > Calculated Fieldβ to open the calculation editor.
Name the new field and create a formula.
JOIN THIS CHANNEL IF YOU ARE NEWπ
Company name: Numerator
Role: Data Scientist
Topic : RNN, pop & remove, calculated field, views
1. What are the uses of using RNN in NLP?
The RNN is a stateful neural network, which means that it not only retains information from the previous layer but also from the previous pass. Thus, this neuron is said to have connections between passes, and through time.
For the RNN the order of the input matters due to being stateful. The same words with different orders will yield different outputs.
RNN can be used for unsegmented, connected applications such as handwriting recognition or speech recognition.
2. How to remove values to a python array?
Ans: Array elements can be removed using pop() or remove() method. The difference between these two functions is that the former returns the deleted value whereas the latter does not.
3. What are the advantages and disadvantages of views in the database?
Answer: Advantages of Views:
As there is no physical location where the data in the view is stored, it generates output without wasting resources.
Data access is restricted as it does not allow commands like insertion, updation, and deletion.
Disadvantages of Views:
The view becomes irrelevant if we drop a table related to that view.
Much memory space is occupied when the view is created for large tables.
4. How to create a calculated field in Tableau?
Click the drop down to the right of Dimensions on the Data pane and select βCreate > Calculated Fieldβ to open the calculation editor.
Name the new field and create a formula.
π3β€1π1
Date: 05-01-2023
Company name: IBM
Role: Data Analyst
Topic: Clustering, excel, SQL, parameter in tableau
1. What is Density-based Clustering?
Density-Based Clustering is an unsupervised machine learning method that identifies different groups or clusters in the data space. These clustering techniques are based on the concept that a cluster in the data space is a contiguous region of high point density, separated from other such clusters by contiguous regions of low point density.
Partition-based(K-means) and Hierarchical clustering techniques are highly efficient with normal-shaped clusters while density-based techniques are efficient in arbitrary-shaped clusters or detecting outliers.
2. How to create empty tables with the same structure as another table?
To create empty tables:
Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure. As a result, SQL creates a new table with a duplicate structure to accept the fetched entries, but nothing is stored into the new table since the WHERE clause is active.
3. What is a Parameter in Tableau? Give an Example.
A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.
4. How will you write the formula for the following in Excel?
- Multiply the value in cell A1 by 10, add the result by 5, and divide it by 2.
To write a formula for the above-stated question, we have to follow the PEDMAS Precedence. The correct answer is ((A1*10)+5)/2.
Answers such as =A1*10+5/2 and =(A1*10)+5/2 are not correct. We must put parentheses brackets after a particular operation.
JOIN THIS CHANNEL IF YOU ARE NEWπ
Company name: IBM
Role: Data Analyst
Topic: Clustering, excel, SQL, parameter in tableau
1. What is Density-based Clustering?
Density-Based Clustering is an unsupervised machine learning method that identifies different groups or clusters in the data space. These clustering techniques are based on the concept that a cluster in the data space is a contiguous region of high point density, separated from other such clusters by contiguous regions of low point density.
Partition-based(K-means) and Hierarchical clustering techniques are highly efficient with normal-shaped clusters while density-based techniques are efficient in arbitrary-shaped clusters or detecting outliers.
2. How to create empty tables with the same structure as another table?
To create empty tables:
Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure. As a result, SQL creates a new table with a duplicate structure to accept the fetched entries, but nothing is stored into the new table since the WHERE clause is active.
3. What is a Parameter in Tableau? Give an Example.
A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.
4. How will you write the formula for the following in Excel?
- Multiply the value in cell A1 by 10, add the result by 5, and divide it by 2.
To write a formula for the above-stated question, we have to follow the PEDMAS Precedence. The correct answer is ((A1*10)+5)/2.
Answers such as =A1*10+5/2 and =(A1*10)+5/2 are not correct. We must put parentheses brackets after a particular operation.
π₯6β€1
Date: 06-01-2023
Company name: Mindtree
Role: Data Scientist
Topic: perceptron, margins in svm, joins, powerbi
1. What are the steps involved in training a perceptron in Deep Learning?
There are five main steps that determine the learning of a perceptron:
Initialize thresholds and weights
Provide inputs
Calculate outputs
Update weights in each step
Repeat steps 2 to 4
2. What are Hard-Margin and Soft-Margin SVMs?
Hard-Margin SVMs have linearly separable training data. No data points are allowed in the margin areas. This type of linear classification is known as Hard margin classification.
Soft-Margin SVMs have training data that are not linearly separable. Margin violation means choosing a hyperplane, which can allow some data points to stay either in between the margin area or on the incorrect side of the hyperplane.
Hard-Margin SVMs are quite sensitive to outliers.
Soft-Margin SVMs try to find the best balance between keeping the margin as large as possible and limiting the margin violations.
3. What are the building blocks of Power BI?
The major building blocks of Power BI are:
Datasets: Dataset is a collection of data gathered from various sources like SQL Server, Azure, Text, Oracle, XML, JSON, and many more. With the GetData feature in Power BI, we can easily fetch data from any data source.
Visualizations: Visualization is the visual aesthetic representation of data in the form of maps, charts, or tables.
Reports: Reports are a structured representation of datasets that consists of multiple pages. Reports help to extract important information and insights from datasets to take major business decisions.
Dashboards: A dashboard is a single-page representation of reports made of various datasets. Each element is termed a tile.
Tiles: Tiles are single-block containing visualizations of a report. Tiles help to differentiate each report
4. What is the Right JOIN in SQL?
The Right join is used to retrieve all rows from the right-hand table and only those rows from the other table that fulfilled the join condition. It returns all the rows from the right-hand side table even though there are no matches in the left-hand side table. If it finds unmatched records from the left side table, it returns a Null value. This join is also known as Right Outer Join.
5. Do you wanna make your career in Data Science & Analytics but don't know how to start ?
Get a complete roadmap from scratch that will make you technically strong enough to crack any Data Scientist / Analyst and also learn Pro Career Growth Hacks to land on your Dream Job.
Check India's Most Affordable Industry Oriented Course, chosen & trusted by 12000+ happy learners. Link Belowππ»
β‘οΈ https://www.cloudyml.com/all-in-one-super-combo/
Happy Learning π
Company name: Mindtree
Role: Data Scientist
Topic: perceptron, margins in svm, joins, powerbi
1. What are the steps involved in training a perceptron in Deep Learning?
There are five main steps that determine the learning of a perceptron:
Initialize thresholds and weights
Provide inputs
Calculate outputs
Update weights in each step
Repeat steps 2 to 4
2. What are Hard-Margin and Soft-Margin SVMs?
Hard-Margin SVMs have linearly separable training data. No data points are allowed in the margin areas. This type of linear classification is known as Hard margin classification.
Soft-Margin SVMs have training data that are not linearly separable. Margin violation means choosing a hyperplane, which can allow some data points to stay either in between the margin area or on the incorrect side of the hyperplane.
Hard-Margin SVMs are quite sensitive to outliers.
Soft-Margin SVMs try to find the best balance between keeping the margin as large as possible and limiting the margin violations.
3. What are the building blocks of Power BI?
The major building blocks of Power BI are:
Datasets: Dataset is a collection of data gathered from various sources like SQL Server, Azure, Text, Oracle, XML, JSON, and many more. With the GetData feature in Power BI, we can easily fetch data from any data source.
Visualizations: Visualization is the visual aesthetic representation of data in the form of maps, charts, or tables.
Reports: Reports are a structured representation of datasets that consists of multiple pages. Reports help to extract important information and insights from datasets to take major business decisions.
Dashboards: A dashboard is a single-page representation of reports made of various datasets. Each element is termed a tile.
Tiles: Tiles are single-block containing visualizations of a report. Tiles help to differentiate each report
4. What is the Right JOIN in SQL?
The Right join is used to retrieve all rows from the right-hand table and only those rows from the other table that fulfilled the join condition. It returns all the rows from the right-hand side table even though there are no matches in the left-hand side table. If it finds unmatched records from the left side table, it returns a Null value. This join is also known as Right Outer Join.
5. Do you wanna make your career in Data Science & Analytics but don't know how to start ?
Check India's Most Affordable Industry Oriented Course, chosen & trusted by 12000+ happy learners. Link Belowππ»
β‘οΈ
Happy Learning π
π₯8π€©4β€2π2π1π1
Data Science Case Study Video Is Hereπ
Watch our latest YouTube Video in which we have covered Diabetes Prediction Using Machine Learning. Link given below β¬οΈ
Check Now - https://youtu.be/XCdwqybQWcw
Watch our latest YouTube Video in which we have covered Diabetes Prediction Using Machine Learning. Link given below β¬οΈ
Check Now - https://youtu.be/XCdwqybQWcw
Date: 07-01-2023
Company name: Musigma
Role: ML Engineer
Topic: random forest, linear regression, naive bayes, gradient descent
1. How is the Error calculated in a Linear Regression model?
Measuring the distance of the observed y-values from the predicted y-values at each value of x.
Squaring each of these distances.
Calculating the mean of each of the squared distances.
MSE = (1/n) * Ξ£(actual β forecast)2
The smaller the Mean Squared Error, the closer you are to finding the line of best fit
How bad or good is this final value always depends on the context of the problem, but the main goal is that its value is as minimal as possible.
2. Explain the intuition behind the Gradient Descent algorithm.
Gradient descent is an optimization algorithm thatβs used when training a machine learning model and is based on a convex function and tweaks its parameters iteratively to minimize a given function to its local minimum (that is, slope = 0).
For a start, we have to select a random bias and weights, and then iterate over the slope function to get a slope of 0.
The way we change update the value of the bias and weights is through a variable called the learning rate. We have to be wise on the learning rate because choosing:
A small leaning rate may lead to the model to take some time to learn
A large learning rate will make the model converge as our pointer will shoot and weβll not be able to get to minima.
3. How is a Random Forest related to Decision Trees?
Random forest is an ensemble learning method that works by constructing a multitude of decision trees. A random forest can be constructed for both classification and regression tasks.
Random forest outperforms decision trees, and it also does not have the habit of overfitting the data as decision trees do.
A decision tree trained on a specific dataset will become very deep and cause overfitting. To create a random forest, decision trees can be trained on different subsets of the training dataset, and then the different decision trees can be averaged with the goal of decreasing the variance.
4. What are some disadvantages of using Naive Bayes Algorithm?
Some disadvantages of using Naive Bayes Algorithm are:
It relies on a very big assumption that the independent variables are not related to each other.
It is generally not suitable for datasets with large numbers of numerical attributes.
It has been observed that if a rare case is not in the training dataset but is in the testing dataset, then it will most definitely be wrong.
Company name: Musigma
Role: ML Engineer
Topic: random forest, linear regression, naive bayes, gradient descent
1. How is the Error calculated in a Linear Regression model?
Measuring the distance of the observed y-values from the predicted y-values at each value of x.
Squaring each of these distances.
Calculating the mean of each of the squared distances.
MSE = (1/n) * Ξ£(actual β forecast)2
The smaller the Mean Squared Error, the closer you are to finding the line of best fit
How bad or good is this final value always depends on the context of the problem, but the main goal is that its value is as minimal as possible.
2. Explain the intuition behind the Gradient Descent algorithm.
Gradient descent is an optimization algorithm thatβs used when training a machine learning model and is based on a convex function and tweaks its parameters iteratively to minimize a given function to its local minimum (that is, slope = 0).
For a start, we have to select a random bias and weights, and then iterate over the slope function to get a slope of 0.
The way we change update the value of the bias and weights is through a variable called the learning rate. We have to be wise on the learning rate because choosing:
A small leaning rate may lead to the model to take some time to learn
A large learning rate will make the model converge as our pointer will shoot and weβll not be able to get to minima.
3. How is a Random Forest related to Decision Trees?
Random forest is an ensemble learning method that works by constructing a multitude of decision trees. A random forest can be constructed for both classification and regression tasks.
Random forest outperforms decision trees, and it also does not have the habit of overfitting the data as decision trees do.
A decision tree trained on a specific dataset will become very deep and cause overfitting. To create a random forest, decision trees can be trained on different subsets of the training dataset, and then the different decision trees can be averaged with the goal of decreasing the variance.
4. What are some disadvantages of using Naive Bayes Algorithm?
Some disadvantages of using Naive Bayes Algorithm are:
It relies on a very big assumption that the independent variables are not related to each other.
It is generally not suitable for datasets with large numbers of numerical attributes.
It has been observed that if a rare case is not in the training dataset but is in the testing dataset, then it will most definitely be wrong.
Date: 08-01-2023
Company name: IKEA
Role: SQL Developer
Topic:
Entites and relationships, alias, drop and truncate
1. Can you explain entities and relationships with examples?
An entity is a real-world object. A university database can have entities such as Student, Lecturer, Course, Project, etc. A relationship signifies an association among the entities. Example, that a relationship exists between the student and lecturer.
2. What is an Alias?
Aliases are the temporary names given to table or column for the purpose of a particular SQL query. It is used when name of column or table is used other than their original names, but the modified name is only temporary.
3. Which operator is used in query for pattern matching?
The LIKE operator provides standard pattern matching in SQL that is always used after a WHERE clause. It matches any pattern based on some conditions provided using the wildcard characters.
Some of the commonly used wildcard characters in MySQL are as follows:
'%' represents zero or more characters.
'_' represents exactly 1 character.
4. Explain the difference between drop and truncate.
In SQL, the DROP command is used to remove the whole database or table indexes, data, and more. Whereas the TRUNCATE command is used to remove all the rows from the table.
JOIN THIS CHANNEL IF YOU ARE NEWπ
Company name: IKEA
Role: SQL Developer
Topic:
Entites and relationships, alias, drop and truncate
1. Can you explain entities and relationships with examples?
An entity is a real-world object. A university database can have entities such as Student, Lecturer, Course, Project, etc. A relationship signifies an association among the entities. Example, that a relationship exists between the student and lecturer.
2. What is an Alias?
Aliases are the temporary names given to table or column for the purpose of a particular SQL query. It is used when name of column or table is used other than their original names, but the modified name is only temporary.
3. Which operator is used in query for pattern matching?
The LIKE operator provides standard pattern matching in SQL that is always used after a WHERE clause. It matches any pattern based on some conditions provided using the wildcard characters.
Some of the commonly used wildcard characters in MySQL are as follows:
'%' represents zero or more characters.
'_' represents exactly 1 character.
4. Explain the difference between drop and truncate.
In SQL, the DROP command is used to remove the whole database or table indexes, data, and more. Whereas the TRUNCATE command is used to remove all the rows from the table.
This media is not supported in your browser
VIEW IN TELEGRAM
Follow these steps to grow on LinkedInππ₯
Connect with me on LinkedIn - https://www.linkedin.com/in/akashraj001/
Connect with me on LinkedIn - https://www.linkedin.com/in/akashraj001/
β€7
Check our latest YouTube Video which will help you to understand "Why Data Science & Analytics domain is a Good Career Option For Non-IT People Also"
Video Link β‘οΈ https://youtu.be/ulzrQBd4_1w
Stay Safe & Happy Learningπ
Video Link β‘οΈ https://youtu.be/ulzrQBd4_1w
Stay Safe & Happy Learningπ
β€6π€©2
Date: 10-01-2023
Company name: Elica
Role: Data Analyst
Topic: powerbi , DBMS, excel function, clauses in SQL
1. What data sources can Power BI connect to?
Ans: The list of data sources for Power BI is extensive, but it can be grouped into the following:
Files: Data can be imported from Excel (.xlsx, xlxm), Power BI Desktop files (.pbix) and Comma Separated Value (.csv).
Content Packs: It is a collection of related documents or files that are stored as a group. In Power BI, there are two types of content packs, firstly those from services providers like Google Analytics, Marketo, or Salesforce, and secondly those created and shared by other users in your organization.
Connectors to databases and other datasets such as Azure SQL, Database and SQL, Server Analysis Services tabular data, etc.
2. What are the different integrity rules present in the DBMS?
The different integrity rules present in DBMS are as follows:
Entity Integrity: This rule states that the value of the primary key can never be NULL. So, all the tuples in the column identified as the primary key should have a value.
Referential Integrity: This rule states that either the value of the foreign key is NULL or it should be the primary key of any other relation.
3. What are some common clauses used with SELECT query in SQL?
Some common SQL clauses used in conjuction with a SELECT query are as follows:
WHERE clause in SQL is used to filter records that are necessary, based on specific conditions.
ORDER BY clause in SQL is used to sort the records based on some field(s) in ascending (ASC) or descending order (DESC).
GROUP BY clause in SQL is used to group records with identical data and can be used in conjunction with some aggregation functions to produce summarized results from the database.
HAVING clause in SQL is used to filter records in combination with the GROUP BY clause. It is different from WHERE, since the WHERE clause cannot filter aggregated records.
4. What is the difference between count, counta, and countblank in Excel?
The count function is very often used in Excel. Here, letβs look at the difference between count, and itβs variants - counta and countblank.
1. COUNT
It counts the number of cells that contain numeric values only. Cells that have string values, special characters, and blank cells will not be counted.
2. COUNTA
It counts the number of cells that contain any form of content. Cells that have string values, special characters, and numeric values will be counted. However, a blank cell will not be counted.
3. COUNTBLANK
As the name suggests, it counts the number of blank cells only. Cells that have content will not be taken into consideration.
5. Do you wanna make your career in Data Science & Analytics but don't know how to start ?
Get a complete roadmap from scratch that will make you technically strong enough to crack any Data Scientist / Analyst and also learn Pro Career Growth Hacks to land on your Dream Job.
Check India's Most Affordable Industry Oriented Course, chosen & trusted by 12000+ happy learners. Link Belowππ»
β‘οΈ https://www.cloudyml.com/all-in-one-super-combo/
Happy Learning π
Company name: Elica
Role: Data Analyst
Topic: powerbi , DBMS, excel function, clauses in SQL
1. What data sources can Power BI connect to?
Ans: The list of data sources for Power BI is extensive, but it can be grouped into the following:
Files: Data can be imported from Excel (.xlsx, xlxm), Power BI Desktop files (.pbix) and Comma Separated Value (.csv).
Content Packs: It is a collection of related documents or files that are stored as a group. In Power BI, there are two types of content packs, firstly those from services providers like Google Analytics, Marketo, or Salesforce, and secondly those created and shared by other users in your organization.
Connectors to databases and other datasets such as Azure SQL, Database and SQL, Server Analysis Services tabular data, etc.
2. What are the different integrity rules present in the DBMS?
The different integrity rules present in DBMS are as follows:
Entity Integrity: This rule states that the value of the primary key can never be NULL. So, all the tuples in the column identified as the primary key should have a value.
Referential Integrity: This rule states that either the value of the foreign key is NULL or it should be the primary key of any other relation.
3. What are some common clauses used with SELECT query in SQL?
Some common SQL clauses used in conjuction with a SELECT query are as follows:
WHERE clause in SQL is used to filter records that are necessary, based on specific conditions.
ORDER BY clause in SQL is used to sort the records based on some field(s) in ascending (ASC) or descending order (DESC).
GROUP BY clause in SQL is used to group records with identical data and can be used in conjunction with some aggregation functions to produce summarized results from the database.
HAVING clause in SQL is used to filter records in combination with the GROUP BY clause. It is different from WHERE, since the WHERE clause cannot filter aggregated records.
4. What is the difference between count, counta, and countblank in Excel?
The count function is very often used in Excel. Here, letβs look at the difference between count, and itβs variants - counta and countblank.
1. COUNT
It counts the number of cells that contain numeric values only. Cells that have string values, special characters, and blank cells will not be counted.
2. COUNTA
It counts the number of cells that contain any form of content. Cells that have string values, special characters, and numeric values will be counted. However, a blank cell will not be counted.
3. COUNTBLANK
As the name suggests, it counts the number of blank cells only. Cells that have content will not be taken into consideration.
5. Do you wanna make your career in Data Science & Analytics but don't know how to start ?
Check India's Most Affordable Industry Oriented Course, chosen & trusted by 12000+ happy learners. Link Belowππ»
β‘οΈ
Happy Learning π
β€20π2π€©1
Tableau & PowerBI Interview QnA.pdf
363.3 KB
β¬οΈ Download Interview QnA PDF of Tableau & PowerBI.
π¨Next Gonna Be MS EXCEL INTERVIEW QNA
π¨Next Gonna Be MS EXCEL INTERVIEW QNA
β€34
π¨JOB OPENING UPDATEπ¨
Company: Optum
Role: Data scientist
Exp: fresher
Link: APPLY NOW
Company: Chlenge
Role: Data Analyst
Exp: fresher
Link: APPLY NOW
Company: Princeton
Role: Data Analyst
Exp: Fresher
Link: APPLY NOW
Company: NDS infoserv
Role: Data scientist
Exp: 0-2 years
Link: APPLY NOW
Company: kovai System
Role: Data scientist
Exp: Internship
Link: APPLY NOW
Company: Optum
Role: Data scientist
Exp: fresher
Link: APPLY NOW
Company: Chlenge
Role: Data Analyst
Exp: fresher
Link: APPLY NOW
Company: Princeton
Role: Data Analyst
Exp: Fresher
Link: APPLY NOW
Company: NDS infoserv
Role: Data scientist
Exp: 0-2 years
Link: APPLY NOW
Company: kovai System
Role: Data scientist
Exp: Internship
Link: APPLY NOW
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Stay Safe & Happy Learningπ
π12β€5π₯4π€©3
Date: 12-01-2023
Company name: Capgemini
Role: Data Scientist
Topic: adaboost, sliding window, subquery, workbook vs worksheet vs dashboard in tableau
1. What is the AdaBoost Algorithm?
AdaBoost also called Adaptive Boosting is a technique in Machine Learning used as an Ensemble Method. The most common algorithm used with AdaBoost is decision trees with one level that means with Decision trees with only 1 split. These trees are also called Decision Stumps. What this algorithm does is that it builds a model and gives equal weights to all the data points. It then assigns higher weights to points that are wrongly classified. Now all the points which have higher weights are given more importance in the next model. It will keep training models until and unless a lower error is received.
2. What is the Sliding Window method for Time Series Forecasting?
Time series can be phrased as supervised learning. Given a sequence of numbers for a time series dataset, we can restructure the data to look like a supervised learning problem.
In the sliding window method, the previous time steps can be used as input variables, and the next time steps can be used as the output variable.
In statistics and time series analysis, this is called a lag or lag method. The number of previous time steps is called the window width or size of the lag. This sliding window is the basis for how we can turn any time series dataset into a supervised learning problem.
3. What do you understand by sub-queries in SQL?
A subquery is a query inside another query where a query is defined to retrieve data or information back from the database. In a subquery, the outer query is called as the main query whereas the inner query is called subquery. Subqueries are always executed first and the result of the subquery is passed on to the main query. It can be nested inside a SELECT, UPDATE or any other query. A subquery can also use any comparison operators such as >,< or =.
4. Explain the Difference Between Tableau Worksheet, Dashboard, Story, and Workbook?
Tableau uses a workbook and sheet file structure, much like Microsoft Excel.
A workbook contains sheets, which can be a worksheet, dashboard, or a story.
A worksheet contains a single view along with shelves, legends, and the Data pane.
A dashboard is a collection of views from multiple worksheets.
A story contains a sequence of worksheets or dashboards that work together to convey information.
Company name: Capgemini
Role: Data Scientist
Topic: adaboost, sliding window, subquery, workbook vs worksheet vs dashboard in tableau
1. What is the AdaBoost Algorithm?
AdaBoost also called Adaptive Boosting is a technique in Machine Learning used as an Ensemble Method. The most common algorithm used with AdaBoost is decision trees with one level that means with Decision trees with only 1 split. These trees are also called Decision Stumps. What this algorithm does is that it builds a model and gives equal weights to all the data points. It then assigns higher weights to points that are wrongly classified. Now all the points which have higher weights are given more importance in the next model. It will keep training models until and unless a lower error is received.
2. What is the Sliding Window method for Time Series Forecasting?
Time series can be phrased as supervised learning. Given a sequence of numbers for a time series dataset, we can restructure the data to look like a supervised learning problem.
In the sliding window method, the previous time steps can be used as input variables, and the next time steps can be used as the output variable.
In statistics and time series analysis, this is called a lag or lag method. The number of previous time steps is called the window width or size of the lag. This sliding window is the basis for how we can turn any time series dataset into a supervised learning problem.
3. What do you understand by sub-queries in SQL?
A subquery is a query inside another query where a query is defined to retrieve data or information back from the database. In a subquery, the outer query is called as the main query whereas the inner query is called subquery. Subqueries are always executed first and the result of the subquery is passed on to the main query. It can be nested inside a SELECT, UPDATE or any other query. A subquery can also use any comparison operators such as >,< or =.
4. Explain the Difference Between Tableau Worksheet, Dashboard, Story, and Workbook?
Tableau uses a workbook and sheet file structure, much like Microsoft Excel.
A workbook contains sheets, which can be a worksheet, dashboard, or a story.
A worksheet contains a single view along with shelves, legends, and the Data pane.
A dashboard is a collection of views from multiple worksheets.
A story contains a sequence of worksheets or dashboards that work together to convey information.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Stay Safe & Happy Learningπ
π2
Watch our latest YouTube Video on PowerBi in Hindi. Link given below
Watch Now - https://youtu.be/DgkA_99OKlk
Stay Safe & Happy Learningπ
Watch Now - https://youtu.be/DgkA_99OKlk
Stay Safe & Happy Learningπ