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Do check our Blog in which we have explained about the Roadmap that you can follow to become a Data Scientist. Link Below
👉🏻 https://www.cloudyml.com/blog/data-scientist-roadmap/
👉🏻 https://www.cloudyml.com/blog/data-scientist-roadmap/
Date: 02nd August, 2022
Company Name - Tredence
Role: Data Scientist
Topic: Python, NLP
1. Explain how the filter function works in python?
The filter() method filters a series using a function that checks if each element in the sequence is true or not. The filter() function takes two arguments: function - a function and iterable - an iterable like sets, lists, tuples etc.
2. How to remove duplicate elements from a list?
First we have a List that contains duplicates. Create a dictionary, using the List items as keys. This will automatically remove any duplicates because dictionaries cannot have duplicate keys. Then, convert the dictionary back into a list.
3. Difference between a shallow and a deep copy?
It's faster to do shallow repetitions. It does, however, handle pointers and references in a "lazy" manner. It just copies over the pointer price rather than producing a current copy of the specific knowledge the pointer links to. As a result, each of the initial and subsequent copies can have pointers that relate to the same underlying knowledge. Deep repetition clones the underlying data completely. It is not shared by the first and, as a result, by the copy.
4. What is TF/IDF vectorization?
The TF-IDF statistic, which stands for term frequency–inverse document frequency, is a numerical measure of how essential a word is to a document in a collection or corpus. It's frequently used in information retrieval, text mining, and user modelling searches as a weighting factor. The tf–idf value rises in proportion to the number of times a word appears in a document and is offset by the number of documents in the corpus that contain the term, which helps to compensate for the fact that some words appear more frequently than others.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, 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 Support over Skype chat everyday. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company Name - Tredence
Role: Data Scientist
Topic: Python, NLP
1. Explain how the filter function works in python?
The filter() method filters a series using a function that checks if each element in the sequence is true or not. The filter() function takes two arguments: function - a function and iterable - an iterable like sets, lists, tuples etc.
2. How to remove duplicate elements from a list?
First we have a List that contains duplicates. Create a dictionary, using the List items as keys. This will automatically remove any duplicates because dictionaries cannot have duplicate keys. Then, convert the dictionary back into a list.
3. Difference between a shallow and a deep copy?
It's faster to do shallow repetitions. It does, however, handle pointers and references in a "lazy" manner. It just copies over the pointer price rather than producing a current copy of the specific knowledge the pointer links to. As a result, each of the initial and subsequent copies can have pointers that relate to the same underlying knowledge. Deep repetition clones the underlying data completely. It is not shared by the first and, as a result, by the copy.
4. What is TF/IDF vectorization?
The TF-IDF statistic, which stands for term frequency–inverse document frequency, is a numerical measure of how essential a word is to a document in a collection or corpus. It's frequently used in information retrieval, text mining, and user modelling searches as a weighting factor. The tf–idf value rises in proportion to the number of times a word appears in a document and is offset by the number of documents in the corpus that contain the term, which helps to compensate for the fact that some words appear more frequently than others.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, 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 Support over Skype chat everyday. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Why is Data Analysis vital for every business?
👇🏻Check our Blog Now👇🏻
https://www.cloudyml.com/blog/why-is-data-analysis-vital-for-every-business/
👇🏻Check our Blog Now👇🏻
https://www.cloudyml.com/blog/why-is-data-analysis-vital-for-every-business/
CloudyML
Why is Data Analysis vital for every business?
Businesses have been using data analytics for years now. But what exactly does it mean? And why is it so important? Read on to know more!
Python Project for Beginner. Watch our latest YouTube Video and get explained Python Project Video For Data Science. Link Below
👉🏻 https://youtu.be/YeiPdFxQCw0
👉🏻 https://youtu.be/YeiPdFxQCw0
How Data Science is changing the World ? Read our latest Blog🤩 👇🏻
https://www.cloudyml.com/blog/how-data-science-is-changing-the-world/
https://www.cloudyml.com/blog/how-data-science-is-changing-the-world/
CloudyML
How Data Science is Changing the World?
From preventing blindness, treating drugs to alcoholism data science is being utilized in many areas. Let's find out the future of data science.
🚨Job Openings Alert🚨
Role: Data Scientist
Company: Springer Nature
Exp: fresher
Link: https://ai.cloudyml.com/3zqmeBX
Role: Data Analyst Trainee
Company: Quickxpert Infotech
Exp: fresher
Link: https://ai.cloudyml.com/3zsKyTP
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Company: Tvs Educational Society
Exp: fresher
Link: https://ai.cloudyml.com/3SlDH7c
Role: Analyst - Risk And Compliance
Company: IBM
Exp: fresher
Link: https://ai.cloudyml.com/3BAKGmU
All the best 💙
Role: Data Scientist
Company: Springer Nature
Exp: fresher
Link: https://ai.cloudyml.com/3zqmeBX
Role: Data Analyst Trainee
Company: Quickxpert Infotech
Exp: fresher
Link: https://ai.cloudyml.com/3zsKyTP
Role: Data Analyst
Company: Tvs Educational Society
Exp: fresher
Link: https://ai.cloudyml.com/3SlDH7c
Role: Analyst - Risk And Compliance
Company: IBM
Exp: fresher
Link: https://ai.cloudyml.com/3BAKGmU
All the best 💙
Date: 03 August, 2022
Company : Tiger Analytics
Role: Data Scientist
Topic: ML, Tableau, SQL
Q. Explain the data preprocessing steps in data analysis.
Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.
Q. What Are the Three Stages of Building a Model in Machine Learning?
Ans. The three stages of building a machine learning model are:
Model Building: Choosing a suitable algorithm for the model and train it according to the requirement
Model Testing: Checking the accuracy of the model through the test data
Applying the Model: Making the required changes after testing and use the final model for real-time projects
Q. What are the subsets of SQL?
Ans. The following are the four significant subsets of the SQL:
Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.
Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.
Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.
Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.
Q. What is a Parameter in Tableau? Give an Example.
Ans. 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.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, 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 Support over Skype chat everyday. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company : Tiger Analytics
Role: Data Scientist
Topic: ML, Tableau, SQL
Q. Explain the data preprocessing steps in data analysis.
Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.
Q. What Are the Three Stages of Building a Model in Machine Learning?
Ans. The three stages of building a machine learning model are:
Model Building: Choosing a suitable algorithm for the model and train it according to the requirement
Model Testing: Checking the accuracy of the model through the test data
Applying the Model: Making the required changes after testing and use the final model for real-time projects
Q. What are the subsets of SQL?
Ans. The following are the four significant subsets of the SQL:
Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.
Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.
Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.
Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.
Q. What is a Parameter in Tableau? Give an Example.
Ans. 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.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, 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 Support over Skype chat everyday. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Date: 03 August, 2022
Company - Google
Role- Jr.ML Engineer
Topics: Machine Learning
1.How will you handle missing values in data?
There are several ways to handle missing values in the given data-
1.Dropping the values
2.Deleting the observation (not always recommended).
3.Replacing value with the mean, median and mode of the observation.
4.Predicting value with regression
5.Finding appropriate value with clustering
2. What is SVM? Can you name some kernels used in SVM?
SVM stands for support vector machine. They are used for classification and prediction tasks. SVM consists of a separating plane that discriminates between the two classes of variables. This separating plane is known as hyperplane. Some of the kernels used in SVM are –
Polynomial Kernel
Gaussian Kernel
Laplace RBF Kernel
Sigmoid Kernel
Hyperbolic Kernel
3.What is market basket analysis?
Market Basket Analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.
4.What is the benefit of batch normalization?
The model is less sensitive to hyperparameter tuning.
High learning rates become acceptable, which results in faster training of the model.
Weight initialization becomes an easy task.
Using different non-linear activation functions becomes feasible.
Deep neural networks are simplified because of batch normalization.
It introduces mild regularisation in the network.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, 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 Support over Skype chat everyday. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company - Google
Role- Jr.ML Engineer
Topics: Machine Learning
1.How will you handle missing values in data?
There are several ways to handle missing values in the given data-
1.Dropping the values
2.Deleting the observation (not always recommended).
3.Replacing value with the mean, median and mode of the observation.
4.Predicting value with regression
5.Finding appropriate value with clustering
2. What is SVM? Can you name some kernels used in SVM?
SVM stands for support vector machine. They are used for classification and prediction tasks. SVM consists of a separating plane that discriminates between the two classes of variables. This separating plane is known as hyperplane. Some of the kernels used in SVM are –
Polynomial Kernel
Gaussian Kernel
Laplace RBF Kernel
Sigmoid Kernel
Hyperbolic Kernel
3.What is market basket analysis?
Market Basket Analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.
4.What is the benefit of batch normalization?
The model is less sensitive to hyperparameter tuning.
High learning rates become acceptable, which results in faster training of the model.
Weight initialization becomes an easy task.
Using different non-linear activation functions becomes feasible.
Deep neural networks are simplified because of batch normalization.
It introduces mild regularisation in the network.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, 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 Support over Skype chat everyday. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Learn Data Structure & Algorithm !
In our latest YouTube video we have covered sorting Arrays without sorting Algorithm. Link Below
👉🏻 https://youtu.be/oeftObn8HJY
In our latest YouTube video we have covered sorting Arrays without sorting Algorithm. Link Below
👉🏻 https://youtu.be/oeftObn8HJY
Date: 05th August, 2022
Company name: TVS
Role: Data Analyst
Topic: SQL
1.What are the subsets in SQL ?
The following are the four significant subsets of the SQL:
Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.
Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.
Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.
Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.
2. What is the purpose of DCL Language?
Data control language allows users to control access and permission management to the database. It is the subset of a database, which decides that what part of the database should be accessed by which user at what point of time. It includes two commands, GRANT and REVOKE.
GRANT: It enables system administrators to assign privileges and roles to the specific user accounts to perform specific tasks on the database.
REVOKE: It enables system administrators to revoke privileges and roles from the user accounts so that they cannot use the previously assigned permission on the database.
3. What is a Database?
A database is an organized collection of data that is structured into tables, rows, columns, and indexes. It helps the user to find the relevant information frequently. It is an electronic system that makes data access, data manipulation, data retrieval, data storing, and data management very easy. Almost every organization uses the database for storing the data due to its easily accessible and high operational ease. The database provides perfect access to data and lets us perform required tasks.
The following are the common features of a database:
Manages large amounts of data
Accurate
Easy to update
Security
Data integrity
Easy to research data
4. What is meant by DBMS?
DBMS stands for Database Management System. It is a software program that primarily functions as an interface between the database and the end-user. It provides us the power such as managing the data, the database engine, and the database schema to facilitate the organization and manipulation of data using a simple query in almost no time. It is like a File Manager that manages data in a database rather than saving it in file systems. Without the database management system, it would be far more difficult for the user to access the database's data.
The following are the components of a DBMS:
Software
Data
Procedures
Database Languages
Query Processor
Database Manager
Database Engine
Reporting
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, 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 Support over Skype chat everyday. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company name: TVS
Role: Data Analyst
Topic: SQL
1.What are the subsets in SQL ?
The following are the four significant subsets of the SQL:
Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.
Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.
Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.
Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.
2. What is the purpose of DCL Language?
Data control language allows users to control access and permission management to the database. It is the subset of a database, which decides that what part of the database should be accessed by which user at what point of time. It includes two commands, GRANT and REVOKE.
GRANT: It enables system administrators to assign privileges and roles to the specific user accounts to perform specific tasks on the database.
REVOKE: It enables system administrators to revoke privileges and roles from the user accounts so that they cannot use the previously assigned permission on the database.
3. What is a Database?
A database is an organized collection of data that is structured into tables, rows, columns, and indexes. It helps the user to find the relevant information frequently. It is an electronic system that makes data access, data manipulation, data retrieval, data storing, and data management very easy. Almost every organization uses the database for storing the data due to its easily accessible and high operational ease. The database provides perfect access to data and lets us perform required tasks.
The following are the common features of a database:
Manages large amounts of data
Accurate
Easy to update
Security
Data integrity
Easy to research data
4. What is meant by DBMS?
DBMS stands for Database Management System. It is a software program that primarily functions as an interface between the database and the end-user. It provides us the power such as managing the data, the database engine, and the database schema to facilitate the organization and manipulation of data using a simple query in almost no time. It is like a File Manager that manages data in a database rather than saving it in file systems. Without the database management system, it would be far more difficult for the user to access the database's data.
The following are the components of a DBMS:
Software
Data
Procedures
Database Languages
Query Processor
Database Manager
Database Engine
Reporting
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, 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 Support over Skype chat everyday. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
SQL INTERVIEW QUESTIONS ASKED IN COGNIZANT DATA SCIENCE INTERVIEW.
Watch our latest YouTube video which covers interview questions with explained solutions. Link given below
👉🏻 https://youtu.be/ty8UhYePnRc
Watch our latest YouTube video which covers interview questions with explained solutions. Link given below
👉🏻 https://youtu.be/ty8UhYePnRc
Date: 06th August, 2022
Company- HSBC solutions
Role- Data Analyst
Topic: Excel, Tableau, PowerBI
1.Can you name the wildcards in Excel?
There are 3 wildcards in Excel that can ve used in formulas.
Asterisk (*) – 0 or more characters. For example, Ex* could mean Excel, Extra, Expertise, etc.
Question mark (?) – Represents any 1 character. For example, R?ain may mean Rain or Ruin.
Tilde (~) – Used to identify a wildcard character (~, *, ?). For example, If you need to find the exact phrase India* in a list. If you use India* as the search string, you may get any word with India at the beginning followed by different characters (such as Indian, Indiana). If you have to look for India” exclusively, use ~.
Hence, the search string will be india~*. ~ is used to ensure that the spreadsheet reads the following character as is, and not as a wildcard.
2.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.
3.What is the difference between .twb and .twbx extension?
A .twb file contains information on all the sheets, dashboards and stories, but it won’t contain any information regarding data source. Whereas .twbx file contains all the sheets, dashboards, stories and also compressed data sources. For saving a .twbx extract needs to be performed on the data source. If we forward .twb file to someone else than they will be able to see the worksheets and dashboards but won’t be able to look into the dataset.
4.What are the various Power BI versions?
Power BI Premium capacity-based license, for example, allows users with a free license to act on content in workspaces with Premium capacity. A user with a free license can only use the Power BI service to connect to data and produce reports and dashboards in My Workspace outside of Premium capacity. They are unable to exchange material or publish it in other workspaces. To process material, a Power BI license with a free or Pro per-user license only uses a shared and restricted capacity. Users with a Power BI Pro license can only work with other Power BI Pro users if the material is stored in that shared capacity. They may consume user-generated information, post material to app workspaces, share dashboards, and subscribe to dashboards and reports. Pro users can share material with users who don’t have a Power BI Pro subscription while workspaces are at Premium capacity.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, 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 Support over Skype chat everyday. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company- HSBC solutions
Role- Data Analyst
Topic: Excel, Tableau, PowerBI
1.Can you name the wildcards in Excel?
There are 3 wildcards in Excel that can ve used in formulas.
Asterisk (*) – 0 or more characters. For example, Ex* could mean Excel, Extra, Expertise, etc.
Question mark (?) – Represents any 1 character. For example, R?ain may mean Rain or Ruin.
Tilde (~) – Used to identify a wildcard character (~, *, ?). For example, If you need to find the exact phrase India* in a list. If you use India* as the search string, you may get any word with India at the beginning followed by different characters (such as Indian, Indiana). If you have to look for India” exclusively, use ~.
Hence, the search string will be india~*. ~ is used to ensure that the spreadsheet reads the following character as is, and not as a wildcard.
2.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.
3.What is the difference between .twb and .twbx extension?
A .twb file contains information on all the sheets, dashboards and stories, but it won’t contain any information regarding data source. Whereas .twbx file contains all the sheets, dashboards, stories and also compressed data sources. For saving a .twbx extract needs to be performed on the data source. If we forward .twb file to someone else than they will be able to see the worksheets and dashboards but won’t be able to look into the dataset.
4.What are the various Power BI versions?
Power BI Premium capacity-based license, for example, allows users with a free license to act on content in workspaces with Premium capacity. A user with a free license can only use the Power BI service to connect to data and produce reports and dashboards in My Workspace outside of Premium capacity. They are unable to exchange material or publish it in other workspaces. To process material, a Power BI license with a free or Pro per-user license only uses a shared and restricted capacity. Users with a Power BI Pro license can only work with other Power BI Pro users if the material is stored in that shared capacity. They may consume user-generated information, post material to app workspaces, share dashboards, and subscribe to dashboards and reports. Pro users can share material with users who don’t have a Power BI Pro subscription while workspaces are at Premium capacity.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, 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 Support over Skype chat everyday. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
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❤1
Date: 7th August, 2022
Company name : Swiggy
Role: Junior ML Engineer
Topic : Machine learning
1. What is Supervised Learning?
Supervised learning is a machine learning algorithm of inferring a function from labeled training data. The training data consists of a set of training examples.
Example: 01
Knowing the height and weight identifying the gender of the person. Below are the popular supervised learning algorithms.
Support Vector Machines
Regression
Naive Bayes
Decision Trees
K-nearest Neighbour Algorithm and Neural Networks.
Example: 02
If you build a T-shirt classifier, the labels will be “this is an S, this is an M and this is L”, based on showing the classifier examples of S, M, and L.
2. What is Unsupervised Learning?
Unsupervised learning is also a type of machine learning algorithm used to find patterns on the set of data given. In this, we don’t have any dependent variable or label to predict. Unsupervised Learning Algorithms:
Clustering,
Anomaly Detection,
Neural Networks and Latent Variable Models.
Example:
In the same example, a T-shirt clustering will categorize as “collar style and V neck style”, “crew neck style” and “sleeve types”.
3. What are Loss Function and Cost Functions? Explain the key Difference Between them?
When calculating loss we consider only a single data point, then we use the term loss function.
Whereas, when calculating the sum of error for multiple data then we use the cost function. There is no major difference.
In other words, the loss function is to capture the difference between the actual and predicted values for a single record whereas cost functions aggregate the difference for the entire training dataset.
The Most commonly used loss functions are Mean-squared error and Hinge loss.
Mean-Squared Error(MSE): In simple words, we can say how our model predicted values against the actual values.
MSE = summation(predicted value - actual value)**2/n(no of data points)
Hinge loss: It is used to train the machine learning classifier, which is
L(y) = max(0,1- y_true*y_pred)
Where y = -1 or 1 indicating two classes and y represents the output form of the classifier. The most common cost function represents the total cost as the sum of the fixed costs and the variable costs in the equation y = mx + b
4. What is Ensemble learning?
Ensemble learning is a method that combines multiple machine learning models to create more powerful models.
There are many reasons for a model to be different. Few reasons are:
Different Population
Different Hypothesis
Different modeling techniques
When working with the model’s training and testing data, we will experience an error. This error might be bias, variance, and irreducible error.
Now the model should always have a balance between bias and variance, which we call a bias-variance trade-off.
This ensemble learning is a way to perform this trade-off.
There are many ensemble techniques available but when aggregating multiple models there are two general methods:
Bagging, a native method: take the training set and generate new training sets off of it.
Boosting, a more elegant method: similar to bagging, boosting is used to optimize the best weighting scheme for a training set.
Company name : Swiggy
Role: Junior ML Engineer
Topic : Machine learning
1. What is Supervised Learning?
Supervised learning is a machine learning algorithm of inferring a function from labeled training data. The training data consists of a set of training examples.
Example: 01
Knowing the height and weight identifying the gender of the person. Below are the popular supervised learning algorithms.
Support Vector Machines
Regression
Naive Bayes
Decision Trees
K-nearest Neighbour Algorithm and Neural Networks.
Example: 02
If you build a T-shirt classifier, the labels will be “this is an S, this is an M and this is L”, based on showing the classifier examples of S, M, and L.
2. What is Unsupervised Learning?
Unsupervised learning is also a type of machine learning algorithm used to find patterns on the set of data given. In this, we don’t have any dependent variable or label to predict. Unsupervised Learning Algorithms:
Clustering,
Anomaly Detection,
Neural Networks and Latent Variable Models.
Example:
In the same example, a T-shirt clustering will categorize as “collar style and V neck style”, “crew neck style” and “sleeve types”.
3. What are Loss Function and Cost Functions? Explain the key Difference Between them?
When calculating loss we consider only a single data point, then we use the term loss function.
Whereas, when calculating the sum of error for multiple data then we use the cost function. There is no major difference.
In other words, the loss function is to capture the difference between the actual and predicted values for a single record whereas cost functions aggregate the difference for the entire training dataset.
The Most commonly used loss functions are Mean-squared error and Hinge loss.
Mean-Squared Error(MSE): In simple words, we can say how our model predicted values against the actual values.
MSE = summation(predicted value - actual value)**2/n(no of data points)
Hinge loss: It is used to train the machine learning classifier, which is
L(y) = max(0,1- y_true*y_pred)
Where y = -1 or 1 indicating two classes and y represents the output form of the classifier. The most common cost function represents the total cost as the sum of the fixed costs and the variable costs in the equation y = mx + b
4. What is Ensemble learning?
Ensemble learning is a method that combines multiple machine learning models to create more powerful models.
There are many reasons for a model to be different. Few reasons are:
Different Population
Different Hypothesis
Different modeling techniques
When working with the model’s training and testing data, we will experience an error. This error might be bias, variance, and irreducible error.
Now the model should always have a balance between bias and variance, which we call a bias-variance trade-off.
This ensemble learning is a way to perform this trade-off.
There are many ensemble techniques available but when aggregating multiple models there are two general methods:
Bagging, a native method: take the training set and generate new training sets off of it.
Boosting, a more elegant method: similar to bagging, boosting is used to optimize the best weighting scheme for a training set.
#Aptitude Quiz
The product of two numbers is 9375 and the quotient, when the larger one is divided by the smaller, is 15. The sum of the numbers is:
The product of two numbers is 9375 and the quotient, when the larger one is divided by the smaller, is 15. The sum of the numbers is:
Anonymous Quiz
17%
380
36%
395
37%
400
10%
415
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Watch our latest YouTube video and Increase Hiring Chances With These Tips mentioned in the video for Data Analyst Role. Link given below
👉🏻 https://youtu.be/UBWvkVYZWL8
Date: 08th August, 2022
Company – Cognizant
Role: Data Analyst
Topic: Excel, SQL, Tableau
1. Describe Joins in SQL.
Ans. SQL Join statement is used to combine data or rows from two or more tables based on a common field between them. Different types of Joins are as follows:
• INNER JOIN- returns rows when there is a match in both tables.
• LEFT JOIN- returns all rows from the left table and matching rows in the right table.
• RIGHT JOIN- returns all rows from the right table and matching rows in the left table.
• OUTER JOIN- returns all records from both tables that satisfy the join condition
2. What is a Calculated Field in Tableau?
Ans: A calculated field is used to create new (modified) fields from existing data in the data source. It can be used to create more robust visualizations and doesn’t affect the original dataset.
3. What is a Parameter in Tableau? Give an Example.
Ans: 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 do you create a hyperlink in Excel?
Ans: Hyperlinks are used to navigate between worksheets and files/websites. To create a hyperlink, the shortcut used is Ctrl+K.
The ‘Insert Hyperlink’ box appears. Enter the address and the text to display.
5. What are the types of SQL Queries?
Ans: We have four types of SQL Queries:
DDL (Data Definition Language): the creation of objects
DML (Data Manipulation Language): manipulation of data
DCL (Data Control Language): assignment and removal of permissions
TCL (Transaction Control Language): saving and restoring changes to a database
————————————————————-
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Company – Cognizant
Role: Data Analyst
Topic: Excel, SQL, Tableau
1. Describe Joins in SQL.
Ans. SQL Join statement is used to combine data or rows from two or more tables based on a common field between them. Different types of Joins are as follows:
• INNER JOIN- returns rows when there is a match in both tables.
• LEFT JOIN- returns all rows from the left table and matching rows in the right table.
• RIGHT JOIN- returns all rows from the right table and matching rows in the left table.
• OUTER JOIN- returns all records from both tables that satisfy the join condition
2. What is a Calculated Field in Tableau?
Ans: A calculated field is used to create new (modified) fields from existing data in the data source. It can be used to create more robust visualizations and doesn’t affect the original dataset.
3. What is a Parameter in Tableau? Give an Example.
Ans: 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 do you create a hyperlink in Excel?
Ans: Hyperlinks are used to navigate between worksheets and files/websites. To create a hyperlink, the shortcut used is Ctrl+K.
The ‘Insert Hyperlink’ box appears. Enter the address and the text to display.
5. What are the types of SQL Queries?
Ans: We have four types of SQL Queries:
DDL (Data Definition Language): the creation of objects
DML (Data Manipulation Language): manipulation of data
DCL (Data Control Language): assignment and removal of permissions
TCL (Transaction Control Language): saving and restoring changes to a database
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7000+ HAPPY LEARNERS
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, 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 Support over Skype chat everyday. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo