If you want to know how he managed to crack Business Analyst Interview @EXL from non-coding background then watch our latest podcast episode of "Career Ki Baat With Akash Raj" on YouTube. Link Given Below
👉🏻 https://youtu.be/xpDKdtUSoYw
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Date: 02-10-2022
Company name: Swiggy
Role: ML Engineer
Topic: Supervised, unsupervised, reinforcement
1. How Can You Choose a Classifier Based on a Training Set Data Size?
If the training set is small, high bias / low variance models (e.g. Naive Bayes) tend to perform better because they are less likely to overfit.
If the training set is large, low bias / high variance models (e.g. Logistic Regression) tend to perform better because they can reflect more complex relationships.
2. What Are Unsupervised Machine Learning Techniques?
There are two techniques used in unsupervised learning: clustering and association.
I) Clustering
Clustering problems involve data to be divided into subsets. These subsets, also called clusters, contain data that are similar to each other. Different clusters reveal different details about the objects, unlike classification or regression.
II) Association
In an association problem, we identify patterns of associations between different variables or items.
For example, an e-commerce website can suggest other items for you to buy, based on the prior purchases that you have made, spending habits, items in your wishlist, other customers’ purchase habits, and so on.
3. Explain How a System Can Play a Game of Chess Using Reinforcement Learning.
Reinforcement learning has an environment and an agent. The agent performs some actions to achieve a specific goal. Every time the agent performs a task that is taking it towards the goal, it is rewarded. And, every time it takes a step that goes against that goal or in the reverse direction, it is penalized.
With reinforcement learning, the learning agent learns by playing the game. It will make a move (decision), check if it’s the right move (feedback), and keep the outcomes in memory for the next step it takes (learning). There is a reward for every correct decision the system takes and punishment for the wrong one.
4. How is Amazon Able to Recommend Other Things to Buy?
Once a user buys something from Amazon, Amazon stores that purchase data for future reference and finds products that are most likely also to be bought, it is possible because of the Association algorithm, which can identify patterns in a given dataset.
————————————————————-
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Company name: Swiggy
Role: ML Engineer
Topic: Supervised, unsupervised, reinforcement
1. How Can You Choose a Classifier Based on a Training Set Data Size?
If the training set is small, high bias / low variance models (e.g. Naive Bayes) tend to perform better because they are less likely to overfit.
If the training set is large, low bias / high variance models (e.g. Logistic Regression) tend to perform better because they can reflect more complex relationships.
2. What Are Unsupervised Machine Learning Techniques?
There are two techniques used in unsupervised learning: clustering and association.
I) Clustering
Clustering problems involve data to be divided into subsets. These subsets, also called clusters, contain data that are similar to each other. Different clusters reveal different details about the objects, unlike classification or regression.
II) Association
In an association problem, we identify patterns of associations between different variables or items.
For example, an e-commerce website can suggest other items for you to buy, based on the prior purchases that you have made, spending habits, items in your wishlist, other customers’ purchase habits, and so on.
3. Explain How a System Can Play a Game of Chess Using Reinforcement Learning.
Reinforcement learning has an environment and an agent. The agent performs some actions to achieve a specific goal. Every time the agent performs a task that is taking it towards the goal, it is rewarded. And, every time it takes a step that goes against that goal or in the reverse direction, it is penalized.
With reinforcement learning, the learning agent learns by playing the game. It will make a move (decision), check if it’s the right move (feedback), and keep the outcomes in memory for the next step it takes (learning). There is a reward for every correct decision the system takes and punishment for the wrong one.
4. How is Amazon Able to Recommend Other Things to Buy?
Once a user buys something from Amazon, Amazon stores that purchase data for future reference and finds products that are most likely also to be bought, it is possible because of the Association algorithm, which can identify patterns in a given dataset.
————————————————————-
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Date: 03-10-2022
Company name: IKEA
Role: Data Analyst
Topic: Macro, power query, power pivot, sets & groups
Q1. State some ways to improve the performance of Tableau?
Use an Extract to make workbooks run faster
Reduce the scope of data to decrease the volume of data
Reduce the number of marks on the view to avoid information overload
Try to use integers or Booleans in calculations as they are much faster than strings
Hide unused fields
Use Context filters
Reduce filter usage and use some alternative way to achieve the same result
Use indexing in tables and use the same fields for filtering
Remove unnecessary calculations and sheets.
Q2. What is Power Pivot & Power Query?
Power Pivot is an add-on provided by Microsoft for Excel since 2010. Power Pivot was designed to extend the analytical capabilities and services of Microsoft Excel.
Power Query is a business intelligence tool designed by Microsoft for Excel. Power Query allows you to import data from various data sources and will enable you to clean, transform and reshape your data as per the requirements. Power Query allows you to write your query once and then run it with a simple refresh.
3. 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.
4.What are sets and groups in Tableau?
Sets and groups are used group data based on some specific conditions. The main difference between these two is that a group can divide the dataset into multiple groups whereas a set can have only two options which is either in or out. A user should choose to apply group or sets based on the requirements.
————————————————————-
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company name: IKEA
Role: Data Analyst
Topic: Macro, power query, power pivot, sets & groups
Q1. State some ways to improve the performance of Tableau?
Use an Extract to make workbooks run faster
Reduce the scope of data to decrease the volume of data
Reduce the number of marks on the view to avoid information overload
Try to use integers or Booleans in calculations as they are much faster than strings
Hide unused fields
Use Context filters
Reduce filter usage and use some alternative way to achieve the same result
Use indexing in tables and use the same fields for filtering
Remove unnecessary calculations and sheets.
Q2. What is Power Pivot & Power Query?
Power Pivot is an add-on provided by Microsoft for Excel since 2010. Power Pivot was designed to extend the analytical capabilities and services of Microsoft Excel.
Power Query is a business intelligence tool designed by Microsoft for Excel. Power Query allows you to import data from various data sources and will enable you to clean, transform and reshape your data as per the requirements. Power Query allows you to write your query once and then run it with a simple refresh.
3. 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.
4.What are sets and groups in Tableau?
Sets and groups are used group data based on some specific conditions. The main difference between these two is that a group can divide the dataset into multiple groups whereas a set can have only two options which is either in or out. A user should choose to apply group or sets based on the requirements.
————————————————————-
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Anonymous Quiz
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6%
6
58%
4
10%
3
Date: 04-10-2022
Company name: Reliance JIO
Role: ML Engineer
Topic: Exploding/vanishing gradient, encoding, regularisation
1. 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 doesn’t 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.
2. When does regularization come into play in Machine Learning?
At times when the model begins to underfit or overfit, regularization becomes necessary. It is a regression that diverts or regularizes the coefficient estimates towards zero. It reduces flexibility and discourages learning in a model to avoid the risk of overfitting. The model complexity is reduced and it becomes better at predicting.
3. How can we relate standard deviation and variance?
Standard deviation refers to the spread of your data from the mean. Variance is the average degree to which each point differs from the mean i.e. the average of all data points. We can relate Standard deviation and Variance because it is the square root of Variance.
4. What is the exploding gradient problem while using the back propagation technique?
When large error gradients accumulate and result in large changes in the neural network weights during training, it is called the exploding gradient problem. The values of weights can become so large as to overflow and result in NaN values. This makes the model unstable and the learning of the model to stall just like the vanishing gradient problem.
————————————————————-
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Company name: Reliance JIO
Role: ML Engineer
Topic: Exploding/vanishing gradient, encoding, regularisation
1. 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 doesn’t 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.
2. When does regularization come into play in Machine Learning?
At times when the model begins to underfit or overfit, regularization becomes necessary. It is a regression that diverts or regularizes the coefficient estimates towards zero. It reduces flexibility and discourages learning in a model to avoid the risk of overfitting. The model complexity is reduced and it becomes better at predicting.
3. How can we relate standard deviation and variance?
Standard deviation refers to the spread of your data from the mean. Variance is the average degree to which each point differs from the mean i.e. the average of all data points. We can relate Standard deviation and Variance because it is the square root of Variance.
4. What is the exploding gradient problem while using the back propagation technique?
When large error gradients accumulate and result in large changes in the neural network weights during training, it is called the exploding gradient problem. The values of weights can become so large as to overflow and result in NaN values. This makes the model unstable and the learning of the model to stall just like the vanishing gradient problem.
————————————————————-
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This course package, you are getting Python, R, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, DSA, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Chat Support Everyday and PAID INTERNSHIP. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Date: 05-10-2022
Company name: Tredence
Role: Data Scientist
Topic: pickling, split & join, roc-auc, variance/standard dev
1.How can we relate standard deviation and variance?
Standard deviation refers to the spread of your data from the mean. Variance is the average degree to which each point differs from the mean i.e. the average of all data points. We can relate Standard deviation and Variance because it is the square root of Variance.
2.Explain how a ROC curve works.
The ROC curve is a graphical representation of the contrast between true positive rates and the false positive rate at various thresholds. It’s often used as a proxy for the trade-off between the sensitivity of the model (true positives) vs the fall-out or the probability it will trigger a false alarm (false positives).
3.Explain split() and join() functions in Python?
You can use split() function to split a string based on a delimiter to a list of strings.You can use join() function to join a list of strings based on a delimiter to give a single string.
4.What is pickling and unpickling?
Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function, this process is called pickling. While the process of retrieving original Python objects from the stored string representation is called unpickling.
————————————————————-
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Company name: Tredence
Role: Data Scientist
Topic: pickling, split & join, roc-auc, variance/standard dev
1.How can we relate standard deviation and variance?
Standard deviation refers to the spread of your data from the mean. Variance is the average degree to which each point differs from the mean i.e. the average of all data points. We can relate Standard deviation and Variance because it is the square root of Variance.
2.Explain how a ROC curve works.
The ROC curve is a graphical representation of the contrast between true positive rates and the false positive rate at various thresholds. It’s often used as a proxy for the trade-off between the sensitivity of the model (true positives) vs the fall-out or the probability it will trigger a false alarm (false positives).
3.Explain split() and join() functions in Python?
You can use split() function to split a string based on a delimiter to a list of strings.You can use join() function to join a list of strings based on a delimiter to give a single string.
4.What is pickling and unpickling?
Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function, this process is called pickling. While the process of retrieving original Python objects from the stored string representation is called unpickling.
————————————————————-
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Date: 06-10-2022
Company name : Elica
Role: SQL Developer
Topic: DBMS, ALTER, Nested Triggers
1. What is RDBMS? How is it different from DBMS?
RDBMS stands for Relational Database Management System that stores data in the form of a collection of tables, and relations can be defined between the common fields of these tables.
2. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
3. What is ETL in SQL?
ETL stands for Extract, Transform and Load. It is a three-step process, where we would have to start off by extracting the data from sources. Once we collate the data from different sources, what we have is raw data. This raw data has to be transformed into the tidy format, which will come in the second phase. Finally, we would have to load this tidy data into tools which would help us to find insights.
4. 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.
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company name : Elica
Role: SQL Developer
Topic: DBMS, ALTER, Nested Triggers
1. What is RDBMS? How is it different from DBMS?
RDBMS stands for Relational Database Management System that stores data in the form of a collection of tables, and relations can be defined between the common fields of these tables.
2. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
3. What is ETL in SQL?
ETL stands for Extract, Transform and Load. It is a three-step process, where we would have to start off by extracting the data from sources. Once we collate the data from different sources, what we have is raw data. This raw data has to be transformed into the tidy format, which will come in the second phase. Finally, we would have to load this tidy data into tools which would help us to find insights.
4. 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.
————————————————————-
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Check out our latest YouTube video which will help you to understand code, LINE BY LINE with Python.
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🚨JOB OPENING ALERT🚨
Company: ArchOver Solutions
Open Positions: Data Analyst / Business Analyst / Data Scientist
APPLY NOW - https://forms.gle/J3ZPG3wA4RyeMrwT7
Company: ArchOver Solutions
Open Positions: Data Analyst / Business Analyst / Data Scientist
APPLY NOW - https://forms.gle/J3ZPG3wA4RyeMrwT7
Date: 07-10-2022
Company - The Math company
Role - Tableau developer
Topic: context filter, charts, referential integrity
1. How do you create a context filter in Tableau?
Pull your dimension onto Columns
Drag measure A to Rows and measure B to Rows next to A
You’ll have SUM(A) next to SUM(B)
Right-click on SUM(B) and choose Dual Axis
The two axes on your chart will have two scales
If you want the axes to be synchronized, right-click on the B axis and select Synchronize Axis.
2. What is assume referential integrity in tableau?
Assume referential integrity is useful in cases where you know that two data sources have same references in a column. Then you can use the assume referential integrity feature to define the relation between both the data sources and perform a join on two data sources. To implement referential integrity, drop second table on side of the first table and then it will ask you to match the column to perform join then select the reference column and tableau will join based on the reference and type of join provided.
3. How to create stacked bar chart in tableau?
A stacked bar chart can be created by following steps:
Drag a dimension to columns shelf.
Use “Measure Names” on colour in the marks card.
Right click on “Measure Names” and make it as a filter and select the checkboxes for the measures to display.
Now drag “Measure Values” to rows shelf and change the chart type to bar in marks card.
Your stacked bar chart is ready now you can change the size and colours as per your creativeness.
4.What is cascading filter in tableau?
Cascading filters can also be understood as giving preference to a particular filter and then applying other filters on previously filtered data source. Right-click on the filter you want to use as a main filter and make sure it is set as all values in dashboard then select the subsequent filter and select only relevant values to cascade the filters. This will improve the performance of the dashboard as you have decreased the time wasted in running all the filters over complete data source.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS FROM SCRATCH. CHECK OUT OUR MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, R, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, DSA, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Chat Support Everyday and PAID INTERNSHIP. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company - The Math company
Role - Tableau developer
Topic: context filter, charts, referential integrity
1. How do you create a context filter in Tableau?
Pull your dimension onto Columns
Drag measure A to Rows and measure B to Rows next to A
You’ll have SUM(A) next to SUM(B)
Right-click on SUM(B) and choose Dual Axis
The two axes on your chart will have two scales
If you want the axes to be synchronized, right-click on the B axis and select Synchronize Axis.
2. What is assume referential integrity in tableau?
Assume referential integrity is useful in cases where you know that two data sources have same references in a column. Then you can use the assume referential integrity feature to define the relation between both the data sources and perform a join on two data sources. To implement referential integrity, drop second table on side of the first table and then it will ask you to match the column to perform join then select the reference column and tableau will join based on the reference and type of join provided.
3. How to create stacked bar chart in tableau?
A stacked bar chart can be created by following steps:
Drag a dimension to columns shelf.
Use “Measure Names” on colour in the marks card.
Right click on “Measure Names” and make it as a filter and select the checkboxes for the measures to display.
Now drag “Measure Values” to rows shelf and change the chart type to bar in marks card.
Your stacked bar chart is ready now you can change the size and colours as per your creativeness.
4.What is cascading filter in tableau?
Cascading filters can also be understood as giving preference to a particular filter and then applying other filters on previously filtered data source. Right-click on the filter you want to use as a main filter and make sure it is set as all values in dashboard then select the subsequent filter and select only relevant values to cascade the filters. This will improve the performance of the dashboard as you have decreased the time wasted in running all the filters over complete data source.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS FROM SCRATCH. CHECK OUT OUR MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, R, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, DSA, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Chat Support Everyday and PAID INTERNSHIP. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
🚨JOB OPENING ALERT🚨
Role: Principal Data Scientist/Senior Data Scientist/Data Scientist - NLP
Company: Benovymed Healthcare Private Ltd
Exp: Fresher
Link: https://ai.cloudyml.com/3e8R1Ni
Role: Junior Data Analyst
Company: Webhelp India
Exp: fresher
Link: https://ai.cloudyml.com/3fR5A8o
Role: Business Analyst
Company: Launchpad Fintech
Exp: fresher
Link: https://ai.cloudyml.com/3rE0sHn
Role: Data Analyst
Company: Quickxpert Infotech
Exp: fresher
Link: https://ai.cloudyml.com/3Va1Cbi
ALL THE BEST💙
Role: Principal Data Scientist/Senior Data Scientist/Data Scientist - NLP
Company: Benovymed Healthcare Private Ltd
Exp: Fresher
Link: https://ai.cloudyml.com/3e8R1Ni
Role: Junior Data Analyst
Company: Webhelp India
Exp: fresher
Link: https://ai.cloudyml.com/3fR5A8o
Role: Business Analyst
Company: Launchpad Fintech
Exp: fresher
Link: https://ai.cloudyml.com/3rE0sHn
Role: Data Analyst
Company: Quickxpert Infotech
Exp: fresher
Link: https://ai.cloudyml.com/3Va1Cbi
ALL THE BEST💙
Date: 08-10-2022
Company name : Elica
Role: Data Scientist
Topic: KNN vs Kmeans, P value, ROC curve, data analysis
Q1. How are KNN and K-means clustering different?
Firstly, KNN is a supervised learning algorithm. In order to train this algorithm, we require labeled data. K-means is an unsupervised learning algorithm that looks for patterns that are intrinsic to the data. The K in KNN is the number of nearest data points. On the contrary, the K in K-means specify the number of centroids.
Q2. Explain how a ROC curve works.
The ROC curve is a graphical representation of the contrast between true positive rates and the false positive rate at various thresholds. It’s often used as a proxy for the trade-off between the sensitivity of the model (true positives) vs the fall-out or the probability it will trigger a false alarm (false positives).
Q3. What is P-value?
P-values are used to make a decision about a hypothesis test. P-value is the minimum significant level at which you can reject the null hypothesis. The lower the p-value, the more likely you reject the null hypothesis
Q4. What is the data analysis process?
Data analysis generally refers to the process of assembling, cleaning, interpreting, transforming, and modeling data to gain insights or conclusions and generate reports to help businesses become more profitable.
• Collect Data: The data is collected from a variety of sources and is then stored to be cleaned and prepared. This step involves removing all missing values and outliers.
• Analyse Data: As soon as the data is prepared, the next step is to analyze it. Improvements are made by running a model repeatedly. Following that, the model is validated to ensure that it is meeting the requirements.
• Create Reports: In the end, the model is implemented, and reports are generated as well as distributed to stakeholders.
2. Explain two different ways to detect outliers.
• Box Plot Method: According to this method, the value is considered an outlier if it exceeds or falls below 1.5*IQR (interquartile range), that is, if it lies above the top quartile (Q3) or below the bottom quartile (Q1).
• Standard Deviation Method: According to this method, an outlier is defined as a value that is greater or lower than the mean ± (3*standard deviation)
————————————————————-
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Company name : Elica
Role: Data Scientist
Topic: KNN vs Kmeans, P value, ROC curve, data analysis
Q1. How are KNN and K-means clustering different?
Firstly, KNN is a supervised learning algorithm. In order to train this algorithm, we require labeled data. K-means is an unsupervised learning algorithm that looks for patterns that are intrinsic to the data. The K in KNN is the number of nearest data points. On the contrary, the K in K-means specify the number of centroids.
Q2. Explain how a ROC curve works.
The ROC curve is a graphical representation of the contrast between true positive rates and the false positive rate at various thresholds. It’s often used as a proxy for the trade-off between the sensitivity of the model (true positives) vs the fall-out or the probability it will trigger a false alarm (false positives).
Q3. What is P-value?
P-values are used to make a decision about a hypothesis test. P-value is the minimum significant level at which you can reject the null hypothesis. The lower the p-value, the more likely you reject the null hypothesis
Q4. What is the data analysis process?
Data analysis generally refers to the process of assembling, cleaning, interpreting, transforming, and modeling data to gain insights or conclusions and generate reports to help businesses become more profitable.
• Collect Data: The data is collected from a variety of sources and is then stored to be cleaned and prepared. This step involves removing all missing values and outliers.
• Analyse Data: As soon as the data is prepared, the next step is to analyze it. Improvements are made by running a model repeatedly. Following that, the model is validated to ensure that it is meeting the requirements.
• Create Reports: In the end, the model is implemented, and reports are generated as well as distributed to stakeholders.
2. Explain two different ways to detect outliers.
• Box Plot Method: According to this method, the value is considered an outlier if it exceeds or falls below 1.5*IQR (interquartile range), that is, if it lies above the top quartile (Q3) or below the bottom quartile (Q1).
• Standard Deviation Method: According to this method, an outlier is defined as a value that is greater or lower than the mean ± (3*standard deviation)
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS FROM SCRATCH. CHECK OUT OUR MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, R, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, DSA, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Chat Support Everyday and PAID INTERNSHIP. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Anonymous Quiz
12%
Revoke
7%
Grant
76%
Both A and B
5%
None of the above
Date: 09-10-2022
Company name: Wipro
role: Data Scientist
Topic: dropout, epoch, pca, stored procedure
1. What is Dropout?
Dropout is a regularization technique to avoid overfitting thus increasing the generalizing power. Generally, we should use a small dropout value of 20%-50% of neurons with 20% providing a good starting point.
2. What is a stored procedure?
Stored Procedure is a function consists of many SQL statements to access the database system. Several SQL statements are consolidated into a stored procedure and execute them whenever and wherever required.
3. What is Epoch in Machine Learning?
Epoch in Machine Learning is used to indicate the count of passes in a given training dataset where the Machine Learning algorithm has done its job. Generally, when there is a large chunk of data, it is grouped into several batches. All these batches go through the given model, and this process is referred to as iteration. Now, if the batch size comprises the complete training dataset, then the count of iterations is the same as that of epochs.
4. What is Dimensionality Reduction?
In the real world, Machine Learning models are built on top of features and parameters. These features can be multidimensional and large in number. Sometimes, the features may be irrelevant and it becomes a difficult task to visualize them. This is where dimensionality reduction is used to cut down irrelevant and redundant features with the help of principal variables. These principal variables conserve the features, and are a subgroup, of the parent variables.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS FROM SCRATCH. CHECK OUT OUR MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, R, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, DSA, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Chat Support Everyday and PAID INTERNSHIP. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company name: Wipro
role: Data Scientist
Topic: dropout, epoch, pca, stored procedure
1. What is Dropout?
Dropout is a regularization technique to avoid overfitting thus increasing the generalizing power. Generally, we should use a small dropout value of 20%-50% of neurons with 20% providing a good starting point.
2. What is a stored procedure?
Stored Procedure is a function consists of many SQL statements to access the database system. Several SQL statements are consolidated into a stored procedure and execute them whenever and wherever required.
3. What is Epoch in Machine Learning?
Epoch in Machine Learning is used to indicate the count of passes in a given training dataset where the Machine Learning algorithm has done its job. Generally, when there is a large chunk of data, it is grouped into several batches. All these batches go through the given model, and this process is referred to as iteration. Now, if the batch size comprises the complete training dataset, then the count of iterations is the same as that of epochs.
4. What is Dimensionality Reduction?
In the real world, Machine Learning models are built on top of features and parameters. These features can be multidimensional and large in number. Sometimes, the features may be irrelevant and it becomes a difficult task to visualize them. This is where dimensionality reduction is used to cut down irrelevant and redundant features with the help of principal variables. These principal variables conserve the features, and are a subgroup, of the parent variables.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS FROM SCRATCH. CHECK OUT OUR MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, R, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, DSA, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Chat Support Everyday and PAID INTERNSHIP. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
❤1
Check out our latest YouTube video in which we have explained the solution of SQL Interview Questions asked by Google in Data Science job interview. Link below
👉🏻 https://youtu.be/P5D0jmntbag
#CRACKING_FAANG🔥
👉🏻 https://youtu.be/P5D0jmntbag
#CRACKING_FAANG🔥
Date: 10-10-2022
Company name: IBM
Role: Data Analyst
Topic: sql, excel, tableau
1. What is a Self-Join?
A self-join is a type of join that can be used to connect two tables. As a result, it is a unary relationship. Each row of the table is attached to itself and all other rows of the same table in a self-join. As a result, a self-join is mostly used to combine and compare rows from the same database table.
2. What is OLTP?
OLTP, or online transactional processing, allows huge groups of people to execute massive amounts of database transactions in real time, usually via the internet. A database transaction occurs when data in a database is changed, inserted, deleted, or queried.
3. What is the difference between joining and blending in Tableau?
Joining term is used when you are combining data from the same source, for example, worksheet in an Excel file or tables in Oracle databaseWhile blending requires two completely defined data sources in your report.
4. How to prevent someone from copying the cell from your worksheet in excel?
If you want to protect your worksheet from being copied, go into Menu bar > Review > Protect sheet > Password.
By entering password you can prevent your worksheet from getting copied.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS FROM SCRATCH. CHECK OUT OUR MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, R, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, DSA, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Chat Support Everyday and PAID INTERNSHIP. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company name: IBM
Role: Data Analyst
Topic: sql, excel, tableau
1. What is a Self-Join?
A self-join is a type of join that can be used to connect two tables. As a result, it is a unary relationship. Each row of the table is attached to itself and all other rows of the same table in a self-join. As a result, a self-join is mostly used to combine and compare rows from the same database table.
2. What is OLTP?
OLTP, or online transactional processing, allows huge groups of people to execute massive amounts of database transactions in real time, usually via the internet. A database transaction occurs when data in a database is changed, inserted, deleted, or queried.
3. What is the difference between joining and blending in Tableau?
Joining term is used when you are combining data from the same source, for example, worksheet in an Excel file or tables in Oracle databaseWhile blending requires two completely defined data sources in your report.
4. How to prevent someone from copying the cell from your worksheet in excel?
If you want to protect your worksheet from being copied, go into Menu bar > Review > Protect sheet > Password.
By entering password you can prevent your worksheet from getting copied.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS FROM SCRATCH. CHECK OUT OUR MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, R, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, DSA, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Chat Support Everyday and PAID INTERNSHIP. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Date: 11-10-2022
Company name : Walmart
Role: ML Engineer
Topic: Cross validation, bagging, boosting, sampling
1. What is Cross-validation in Machine Learning?
Cross-validation is a resampling method that uses different portions of the data to test and train a model on different iterations. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice This sampling process is done to break the dataset into smaller parts that have the same number of rows, out of which a random part is selected as a test set and the rest of the parts are kept as train sets. Cross-validation consists of the following techniques:
• Holdout method
• K-fold cross-validation
• Stratified k-fold cross-validation
• Leave p-out cross-validation
2. What is bagging and boosting in Machine Learning?
Bagging is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average.
Boosting is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm.
3.What is systematic sampling and cluster sampling ?
Systematic sampling is a type of probability sampling method. The sample members are selected from a larger population with a random starting point but a fixed periodic interval. This interval is known as the sampling interval. The sampling interval is calculated by dividing the population size by the desired sample size.
Cluster sampling involves dividing the sample population into separate groups, called clusters. Then, a simple random sample of clusters is selected from the population. Analysis is conducted on data from the sampled clusters.
4.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.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS FROM SCRATCH. CHECK OUT OUR MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, R, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, DSA, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Chat Support Everyday and PAID INTERNSHIP. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company name : Walmart
Role: ML Engineer
Topic: Cross validation, bagging, boosting, sampling
1. What is Cross-validation in Machine Learning?
Cross-validation is a resampling method that uses different portions of the data to test and train a model on different iterations. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice This sampling process is done to break the dataset into smaller parts that have the same number of rows, out of which a random part is selected as a test set and the rest of the parts are kept as train sets. Cross-validation consists of the following techniques:
• Holdout method
• K-fold cross-validation
• Stratified k-fold cross-validation
• Leave p-out cross-validation
2. What is bagging and boosting in Machine Learning?
Bagging is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average.
Boosting is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm.
3.What is systematic sampling and cluster sampling ?
Systematic sampling is a type of probability sampling method. The sample members are selected from a larger population with a random starting point but a fixed periodic interval. This interval is known as the sampling interval. The sampling interval is calculated by dividing the population size by the desired sample size.
Cluster sampling involves dividing the sample population into separate groups, called clusters. Then, a simple random sample of clusters is selected from the population. Analysis is conducted on data from the sampled clusters.
4.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.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS FROM SCRATCH. CHECK OUT OUR MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, R, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, DSA, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Chat Support Everyday and PAID INTERNSHIP. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Date: 12-10-2022
Company name: Datanyze
Role: Tableau developer
Topic: charts, cyclic fields, sets and groups
1. What is the use of cycle fields in tableau?
Cycle fields help in switching and trying different colour combinations or views in a cyclic order. It will work only if we have a chart that allows more than one measure such as stacked bar chart and we are unable to finalize the visualizations then we can use cycle fields. To use cycle field, click on analysis menu in the toolbar then select cycle fields to take a quick look at an alternative visualization.
2. 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 options—either in or out—a group can divide the dataset into several groups. A user should decide which group or sets to apply based on the conditions.
3. What is 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. State some ways to improve the performance of Tableau?
Use an Extract to make workbooks run faster.
Reduce the scope of data to decrease the volume of data.
Reduce the number of marks on the view to avoid information overload.
Hide unused fields.
Use Context filters.
Use indexing in tables and use the same fields for filtering.
Remove unnecessary calculations and sheets.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS FROM SCRATCH. CHECK OUT OUR MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, R, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, DSA, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Chat Support Everyday and PAID INTERNSHIP. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company name: Datanyze
Role: Tableau developer
Topic: charts, cyclic fields, sets and groups
1. What is the use of cycle fields in tableau?
Cycle fields help in switching and trying different colour combinations or views in a cyclic order. It will work only if we have a chart that allows more than one measure such as stacked bar chart and we are unable to finalize the visualizations then we can use cycle fields. To use cycle field, click on analysis menu in the toolbar then select cycle fields to take a quick look at an alternative visualization.
2. 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 options—either in or out—a group can divide the dataset into several groups. A user should decide which group or sets to apply based on the conditions.
3. What is 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. State some ways to improve the performance of Tableau?
Use an Extract to make workbooks run faster.
Reduce the scope of data to decrease the volume of data.
Reduce the number of marks on the view to avoid information overload.
Hide unused fields.
Use Context filters.
Use indexing in tables and use the same fields for filtering.
Remove unnecessary calculations and sheets.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS FROM SCRATCH. CHECK OUT OUR MEGA COMBO COURSE PACKAGE.
This course package, you are getting Python, R, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, DSA, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Chat Support Everyday and PAID INTERNSHIP. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
🚨JOB OPENING ALERT🚨
Role: Data Analytics Internship
Company : Tbm Technologies
Exp: fresher
Link: https://ai.cloudyml.com/3RVWwwD
Role: Associate Data Scientist
Company : Aibridge Ml Pvt Ltd
Exp: fresher
Link : https://ai.cloudyml.com/3RVjKTi
Role: Data Research Analyst
Company : Nyusoft Solutions
Exp: fresher
Link: https://ai.cloudyml.com/3yzmFKA
Role: Business Intelligence Analyst, I
Company : Zebra Technologies
Exp: fresher
Link: https://ai.cloudyml.com/3ywD0zW
ALL THE BEST💙
Role: Data Analytics Internship
Company : Tbm Technologies
Exp: fresher
Link: https://ai.cloudyml.com/3RVWwwD
Role: Associate Data Scientist
Company : Aibridge Ml Pvt Ltd
Exp: fresher
Link : https://ai.cloudyml.com/3RVjKTi
Role: Data Research Analyst
Company : Nyusoft Solutions
Exp: fresher
Link: https://ai.cloudyml.com/3yzmFKA
Role: Business Intelligence Analyst, I
Company : Zebra Technologies
Exp: fresher
Link: https://ai.cloudyml.com/3ywD0zW
ALL THE BEST💙