Date- 20-04-2023
Company name: Tredence
Role: ML engineer
Topic: margin in svm, r2 and adjusted r2, curse of dimensions
1. What is the primary difference between R square and adjusted R square?
In linear regression, you use both these values for model validation. However, there is a clear distinction between the two. R square accounts for the variation of all independent variables on the dependent variable. In other words, it considers each independent variable for explaining the variation. In the case of Adjusted R square, it accounts for the significant variables alone for indicating the percentage of variation in the model. By significant, we refer to the P values less than 0.05.
2. What is the curse of dimensionality?
Curse of Dimensionality refers to a set of problems that arise when working with high-dimensional data. The dimension of a dataset corresponds to the number of attributes/features that exist in a dataset. A dataset with a large number of attributes, generally of the order of a hundred or more, is referred to as high dimensional data. Some of the difficulties that come with high dimensional data manifest during analyzing or visualizing the data to identify patterns, and some manifest while training machine learning models. The difficulties related to training machine learning models due to high dimensional data are referred to as the ‘Curse of Dimensionality’.
3. What are some Stopping Criteria for k-Means Clustering?
a. Convergence. No further changes, points stay in the same cluster.
b. The maximum number of iterations. When the maximum number of iterations has been reached, the algorithm will be stopped. This is done to limit the runtime of the algorithm.
c. Variance did not improve by at least x * initial variance
4. What are hard margin and soft Margin SVMs?
Hard margin SVMs work only if the data is linearly separable and these types of SVMs are quite sensitive to the outliers. But our main objective is to find a good balance between keeping the margins as large as possible and limiting the margin violation i.e. instances that end up in the middle of margin or even on the wrong side, and this method is called soft margin SVM.
Company name: Tredence
Role: ML engineer
Topic: margin in svm, r2 and adjusted r2, curse of dimensions
1. What is the primary difference between R square and adjusted R square?
In linear regression, you use both these values for model validation. However, there is a clear distinction between the two. R square accounts for the variation of all independent variables on the dependent variable. In other words, it considers each independent variable for explaining the variation. In the case of Adjusted R square, it accounts for the significant variables alone for indicating the percentage of variation in the model. By significant, we refer to the P values less than 0.05.
2. What is the curse of dimensionality?
Curse of Dimensionality refers to a set of problems that arise when working with high-dimensional data. The dimension of a dataset corresponds to the number of attributes/features that exist in a dataset. A dataset with a large number of attributes, generally of the order of a hundred or more, is referred to as high dimensional data. Some of the difficulties that come with high dimensional data manifest during analyzing or visualizing the data to identify patterns, and some manifest while training machine learning models. The difficulties related to training machine learning models due to high dimensional data are referred to as the ‘Curse of Dimensionality’.
3. What are some Stopping Criteria for k-Means Clustering?
a. Convergence. No further changes, points stay in the same cluster.
b. The maximum number of iterations. When the maximum number of iterations has been reached, the algorithm will be stopped. This is done to limit the runtime of the algorithm.
c. Variance did not improve by at least x * initial variance
4. What are hard margin and soft Margin SVMs?
Hard margin SVMs work only if the data is linearly separable and these types of SVMs are quite sensitive to the outliers. But our main objective is to find a good balance between keeping the margins as large as possible and limiting the margin violation i.e. instances that end up in the middle of margin or even on the wrong side, and this method is called soft margin SVM.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Happy Learning💙
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SQL BASIC HANDS-WRITTEN NOTE.pdf
7 MB
⬇️ DOWNLOAD SQL CHEAT SHEET FOR BEGINNERS - HAND WRITTEN NOTES
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Date: 23-04-2023
Company name: Capgemini
Role: Data Analyst
Topic: deep vs shallow, SQL, duplicate values, shelves in tableau
1. What is the Difference Between a Shallow Copy and Deep Copy in python?
Deepcopy creates a different object and populates it with the child objects of the original object. Therefore, changes in the original object are not reflected in the copy. copy.deepcopy() creates a Deep Copy. Shallow copy creates a different object and populates it with the references of the child objects within the original object. Therefore, changes in the original object are reflected in the copy. copy.copy creates a Shallow Copy.
2. How can you remove duplicate values in a range of cells?
1. To delete duplicate values in a column, select the highlighted cells, and press the delete button. After deleting the values, go to the ‘Conditional Formatting’ option present in the Home tab. Choose ‘Clear Rules’ to remove the rules from the sheet.
2. You can also delete duplicate values by selecting the ‘Remove Duplicates’ option under Data Tools present in the Data tab.
3. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.
4. Given a table Employee having columns empName and empId, what will be the result of the SQL query below?
select empName from Employee order by 2 asc;
“Order by 2” is valid when there are at least 2 columns used in SELECT statement. Here this query will throw error because only one column is used in the SELECT statement.
Company name: Capgemini
Role: Data Analyst
Topic: deep vs shallow, SQL, duplicate values, shelves in tableau
1. What is the Difference Between a Shallow Copy and Deep Copy in python?
Deepcopy creates a different object and populates it with the child objects of the original object. Therefore, changes in the original object are not reflected in the copy. copy.deepcopy() creates a Deep Copy. Shallow copy creates a different object and populates it with the references of the child objects within the original object. Therefore, changes in the original object are reflected in the copy. copy.copy creates a Shallow Copy.
2. How can you remove duplicate values in a range of cells?
1. To delete duplicate values in a column, select the highlighted cells, and press the delete button. After deleting the values, go to the ‘Conditional Formatting’ option present in the Home tab. Choose ‘Clear Rules’ to remove the rules from the sheet.
2. You can also delete duplicate values by selecting the ‘Remove Duplicates’ option under Data Tools present in the Data tab.
3. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.
4. Given a table Employee having columns empName and empId, what will be the result of the SQL query below?
select empName from Employee order by 2 asc;
“Order by 2” is valid when there are at least 2 columns used in SELECT statement. Here this query will throw error because only one column is used in the SELECT statement.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Happy Learning💙
❤16👏1
Date - 24-04-2023
Company name: Elica
Role: ML Engineer
Topic: PCA, SGD, decision tree, ARM
1. What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?
Gradient Descent and Stochastic Gradient Descent are the algorithms that find the set of parameters that will minimize a loss function.
The difference is that in Gradient Descent, all training samples are evaluated for each set of parameters. While in Stochastic Gradient Descent only one training sample is evaluated for the set of parameters identified.
2. Can you mention some advantages and disadvantages of decision trees?
The advantages of decision trees are that they are easier to interpret, are nonparametric and hence robust to outliers, and have relatively few parameters to tune.
On the other hand, the disadvantage is that they are prone to overfitting.
3. What do you mean by Associative Rule Mining (ARM)?
Associative Rule Mining is one of the techniques to discover patterns in data like features (dimensions) which occur together and features (dimensions) which are correlated. It is mostly used in Market-based Analysis to find how frequently an itemset occurs in a transaction. Association rules have to satisfy minimum support and minimum confidence at the very same time.
4. What is the Principle Component Analysis?
The idea here is to reduce the dimensionality of the data set by reducing the number of variables that are correlated with each other. Although the variation needs to be retained to the maximum extent.
The variables are transformed into a new set of variables that are known as Principal Components’. These PCs are the eigenvectors of a covariance matrix and therefore are orthogonal.
Company name: Elica
Role: ML Engineer
Topic: PCA, SGD, decision tree, ARM
1. What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?
Gradient Descent and Stochastic Gradient Descent are the algorithms that find the set of parameters that will minimize a loss function.
The difference is that in Gradient Descent, all training samples are evaluated for each set of parameters. While in Stochastic Gradient Descent only one training sample is evaluated for the set of parameters identified.
2. Can you mention some advantages and disadvantages of decision trees?
The advantages of decision trees are that they are easier to interpret, are nonparametric and hence robust to outliers, and have relatively few parameters to tune.
On the other hand, the disadvantage is that they are prone to overfitting.
3. What do you mean by Associative Rule Mining (ARM)?
Associative Rule Mining is one of the techniques to discover patterns in data like features (dimensions) which occur together and features (dimensions) which are correlated. It is mostly used in Market-based Analysis to find how frequently an itemset occurs in a transaction. Association rules have to satisfy minimum support and minimum confidence at the very same time.
4. What is the Principle Component Analysis?
The idea here is to reduce the dimensionality of the data set by reducing the number of variables that are correlated with each other. Although the variation needs to be retained to the maximum extent.
The variables are transformed into a new set of variables that are known as Principal Components’. These PCs are the eigenvectors of a covariance matrix and therefore are orthogonal.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Happy Learning💙
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Date - 25-04-2023
Company name: Walmart
Role: Data Scientist
Topic: kmean, cnn layers, constraint in SQL, tableau
1. Explain some cases where k-Means clustering fails to give good results
k-means has trouble clustering data where clusters are of various sizes and densities.
Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.
If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.
2. Explain the different layers in CNN.
The different layers involved in the architecture of CNN are as follows:
1. Input Layer: The input layer in CNN should contain image data. Image data is represented by a three-dimensional matrix. We have to reshape the image into a single column.
For Example, Suppose we have an MNIST dataset and you have an image of dimension 28 x 28 =784, you need to convert it into 784 x 1 before feeding it into the input. If we have “k” training examples in the dataset, then the dimension of input will be (784, k).
2. Convolutional Layer: To perform the convolution operation, this layer is used which creates several smaller picture windows to go over the data.
3. ReLU Layer: This layer introduces the non-linearity to the network and converts all the negative pixels to zero. The final output is a rectified feature map.
3. What Would You Do If Some Countries/Provinces (Any Geographical Entity) are Missing and Displaying a Null When You Use Map View in Tableau?
When working with maps and geographical fields, unknown or ambiguous locations are identified by the indicator in the lower right corner of the view.
Click the indicator and choose from the following options:
Edit Locations - correct the locations by mapping your data to known locations
Filter Data - exclude the unknown locations from the view using a filter. The locations will not be included in calculations
Show Data at Default Position - show the values at the default position of (0, 0) on the map.
4. What are Constraints in SQL?
Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command.
The constraints are:
NOT NULL - Restricts NULL value from being inserted into a column.
CHECK - Verifies that all values in a field satisfy a condition.
DEFAULT - Automatically assigns a default value if no value has been specified for the field.
UNIQUE - Ensures unique values to be inserted into the field.
INDEX - Indexes a field providing faster retrieval of records.
PRIMARY KEY - Uniquely identifies each record in a table.
FOREIGN KEY - Ensures referential integrity for a record in another table.
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Company name: Walmart
Role: Data Scientist
Topic: kmean, cnn layers, constraint in SQL, tableau
1. Explain some cases where k-Means clustering fails to give good results
k-means has trouble clustering data where clusters are of various sizes and densities.
Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.
If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.
2. Explain the different layers in CNN.
The different layers involved in the architecture of CNN are as follows:
1. Input Layer: The input layer in CNN should contain image data. Image data is represented by a three-dimensional matrix. We have to reshape the image into a single column.
For Example, Suppose we have an MNIST dataset and you have an image of dimension 28 x 28 =784, you need to convert it into 784 x 1 before feeding it into the input. If we have “k” training examples in the dataset, then the dimension of input will be (784, k).
2. Convolutional Layer: To perform the convolution operation, this layer is used which creates several smaller picture windows to go over the data.
3. ReLU Layer: This layer introduces the non-linearity to the network and converts all the negative pixels to zero. The final output is a rectified feature map.
3. What Would You Do If Some Countries/Provinces (Any Geographical Entity) are Missing and Displaying a Null When You Use Map View in Tableau?
When working with maps and geographical fields, unknown or ambiguous locations are identified by the indicator in the lower right corner of the view.
Click the indicator and choose from the following options:
Edit Locations - correct the locations by mapping your data to known locations
Filter Data - exclude the unknown locations from the view using a filter. The locations will not be included in calculations
Show Data at Default Position - show the values at the default position of (0, 0) on the map.
4. What are Constraints in SQL?
Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command.
The constraints are:
NOT NULL - Restricts NULL value from being inserted into a column.
CHECK - Verifies that all values in a field satisfy a condition.
DEFAULT - Automatically assigns a default value if no value has been specified for the field.
UNIQUE - Ensures unique values to be inserted into the field.
INDEX - Indexes a field providing faster retrieval of records.
PRIMARY KEY - Uniquely identifies each record in a table.
FOREIGN KEY - Ensures referential integrity for a record in another table.
👉🏻 If you wanna make your career in Data Science & Analytics domain then check out our Data Science & Analytics Placement Assurance Program. Learn @ Most Affordable Price From Scratch and Get Placed in 5 Months. Visit Our Website Now
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Linkedin
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It’s been a 2 years of roller coaster ride for CloudyML. We entered the most competitive market of Data Scientist & Analytics and disrupted it. At time where… | 41 comments on LinkedIn
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Date: 26-04-2023
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 “Create > Calculated Field” to open the calculation editor.
Name the new field and create a formula.
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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 “Create > Calculated Field” to open the calculation editor.
Name the new field and create a formula.
👉🏻 If you wanna make your career in Data Science & Analytics domain then check out our Data Science & Analytics Placement Assurance Program. Learn @ Most Affordable Price From Scratch and Get Placed in 5 Months. Visit Our Website Now
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ML supervised learning hand written notes by CloudyML.pdf
41.9 MB
⬇️ DOWNLOAD ML SUPERVISED LEARNING HAND-WRITTEN NOTES ⬇️
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Linkedin
Akash Raj on LinkedIn: #learnbydoing #kaggle #hackerank #datascience #competition #skills…
These Top 10 Online Competitions will challenge you and enhance your Data Science and Analytics skills :
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🆒6❤1🔥1
Date: 27-04-2023
Company name: TCS
Role: Data Analyst
Topic : data cleaning, data wrangling, cte
1. Define the term 'Data Wrangling.
Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format.
2. What are the best methods for data cleaning?
Create a data cleaning plan by understanding where the common errors take place and keep all the communications open. Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.
3. Explain 4 steps to use CTE in sql.
All CTE starts with "with" clause.
After with you need to define CTE name and the field names. For instance in the below code snippet I have 3 fields Count,Column and Id. The name of CTE is "MyTemp".
Once you have defined CTE we need to specify the SQL which will give the result for the CTE.
Finally you can use the CTE in your SQL query.
#Learnbydoing
Company name: TCS
Role: Data Analyst
Topic : data cleaning, data wrangling, cte
1. Define the term 'Data Wrangling.
Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format.
2. What are the best methods for data cleaning?
Create a data cleaning plan by understanding where the common errors take place and keep all the communications open. Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.
3. Explain 4 steps to use CTE in sql.
All CTE starts with "with" clause.
After with you need to define CTE name and the field names. For instance in the below code snippet I have 3 fields Count,Column and Id. The name of CTE is "MyTemp".
Once you have defined CTE we need to specify the SQL which will give the result for the CTE.
Finally you can use the CTE in your SQL query.
#Learnbydoing
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Date: 28-04-2023
Company name: Infosys
Role: Data Scientist
Topic: LSTM, kmeans, cte, numpy
1. Can you explain how the memory cell in an LSTM is implemented computationally?
The memory cell in an LSTM is implemented as a forget gate, an input gate, and an output gate. The forget gate controls how much information from the previous cell state is forgotten. The input gate controls how much new information from the current input is allowed into the cell state. The output gate controls how much information from the cell state is allowed to pass out to the next cell state.
2. What is CTE in SQL?
A CTE (Common Table Expression) is a one-time result set that only exists for the duration of the query. It allows us to refer to data within a single SELECT, INSERT, UPDATE, DELETE, CREATE VIEW, or MERGE statement's execution scope. It is temporary because its result cannot be stored anywhere and will be lost as soon as a query's execution is completed.
3. List the advantages NumPy Arrays have over Python lists?
Python’s lists, even though hugely efficient containers capable of a number of functions, have several limitations when compared to NumPy arrays. It is not possible to perform vectorised operations which includes element-wise addition and multiplication. They also require that Python store the type information of every element since they support objects of different types. This means a type dispatching code must be executed each time an operation on an element is done.
4. Explain some cases where k-Means clustering fails to give good results
k-means has trouble clustering data where clusters are of various sizes and densities.
Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.
If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.
👉🏻 #Learnbydoing
Company name: Infosys
Role: Data Scientist
Topic: LSTM, kmeans, cte, numpy
1. Can you explain how the memory cell in an LSTM is implemented computationally?
The memory cell in an LSTM is implemented as a forget gate, an input gate, and an output gate. The forget gate controls how much information from the previous cell state is forgotten. The input gate controls how much new information from the current input is allowed into the cell state. The output gate controls how much information from the cell state is allowed to pass out to the next cell state.
2. What is CTE in SQL?
A CTE (Common Table Expression) is a one-time result set that only exists for the duration of the query. It allows us to refer to data within a single SELECT, INSERT, UPDATE, DELETE, CREATE VIEW, or MERGE statement's execution scope. It is temporary because its result cannot be stored anywhere and will be lost as soon as a query's execution is completed.
3. List the advantages NumPy Arrays have over Python lists?
Python’s lists, even though hugely efficient containers capable of a number of functions, have several limitations when compared to NumPy arrays. It is not possible to perform vectorised operations which includes element-wise addition and multiplication. They also require that Python store the type information of every element since they support objects of different types. This means a type dispatching code must be executed each time an operation on an element is done.
4. Explain some cases where k-Means clustering fails to give good results
k-means has trouble clustering data where clusters are of various sizes and densities.
Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.
If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.
👉🏻 #Learnbydoing
❤5
20+ open source tools for ML Enthusiasts.pdf
4.7 MB
Check out 20+ Open Source Tools for Machine Learning. Download Now ⬇️ and Save it for later 🤩
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Company: Zen3 Infosolutions
Role: Data Analyst
Exp: 0-5 Years
Link: APPLY NOW
Company: AKN IT services
Role: Data Science Professional
Exp: 0-1 Years
Link: APPLY NOW
Company: UG Campus
Role: Data Scientist
Exp: 0-2 Year
Link: APPLY NOW
Company: Accurate traffic data
Role: Data Analyst
Exp: fresher
Link: APPLY NOW
Company: DataR labs
Role: Data Science
Exp: Internship
Link: APPLY NOW
Company: American express
Role: Business analyst
Exp: 0-2 Years
Link: APPLY NOW
Company: Cummins Inc
Role: Data Analyst
Exp: 2+ years
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Date: 01-05-2023
Company name: Musigma
Role: Data Analyst
Topic: Gantt chart, macro, Powerpivot, Fourier transform
1. What’s a Fourier transform?
A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. The Fourier transform finds the set of cycle speeds, amplitudes, and phases to match any time signal. A Fourier transform converts a signal from time to frequency domain—it’s a very common way to extract features from audio signals or other time series such as sensor data.
2. What is x-velocity in Power Pivot?
X-Velocity is the in-memory analytics engine behind Power Pivot that loads and handles huge data in Power BI. It stores data in columnar storage that results in faster processing.
3. Gantt chart in Tableau ?
A Tableau Gantt chart illustrates the duration of events as well as the progression of value across the period. Along with the time axis, it has bars. The Gantt chart is primarily used as a project management tool, with each bar representing a project job.
4. What in Excel is a macro?
An Excel macro is an algorithm or a group of steps that helps automate an operation by capturing and replaying the steps needed to finish it. Once the steps have been saved, you may construct a Macro that the user can alter and replay as often as they like.
Company name: Musigma
Role: Data Analyst
Topic: Gantt chart, macro, Powerpivot, Fourier transform
1. What’s a Fourier transform?
A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. The Fourier transform finds the set of cycle speeds, amplitudes, and phases to match any time signal. A Fourier transform converts a signal from time to frequency domain—it’s a very common way to extract features from audio signals or other time series such as sensor data.
2. What is x-velocity in Power Pivot?
X-Velocity is the in-memory analytics engine behind Power Pivot that loads and handles huge data in Power BI. It stores data in columnar storage that results in faster processing.
3. Gantt chart in Tableau ?
A Tableau Gantt chart illustrates the duration of events as well as the progression of value across the period. Along with the time axis, it has bars. The Gantt chart is primarily used as a project management tool, with each bar representing a project job.
4. What in Excel is a macro?
An Excel macro is an algorithm or a group of steps that helps automate an operation by capturing and replaying the steps needed to finish it. Once the steps have been saved, you may construct a Macro that the user can alter and replay as often as they like.
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3. Airbnb cleaned Europe Dataset…
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