Date: 13-10-2022
Company name: IKEA
Role: Data Scientist
Topic: encoding, z and t test , re module
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. Explain split(), sub(), subn() methods of “re” module in Python.
Ans: To modify the strings, Python’s “re” module is providing 3 methods. They are:
split() – uses a regex pattern to “split” a given string into a list.
sub() – finds all substrings where the regex pattern matches and then replace them with a different string
subn() – it is similar to sub() and also returns the new string along with the no. of replacements.
3. When should you use a t-test vs a z-test?
-> z tests are a statistical way of testing a hypothesis when either:
We know the population variance, or
We do not know the population variance but our sample size is large n ≥ 30
If we have a sample size of less than 30 and do not know the population variance, then we must use a t-test.
- > t-tests are a statistical way of testing a hypothesis when:
We do not know the population variance
Our sample size is small, n < 30
4. What is the meaning of term weight initialization in neural networks?
In neural networking, weight initialization is one of the essential factors. A bad weight initialization prevents a network from learning. On the other side, a good weight initialization helps in giving a quicker convergence and a better overall error. Biases can be initialized to zero. The standard rule for setting the weights is to be close to zero without being too small.
————————————————————-
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Company name: IKEA
Role: Data Scientist
Topic: encoding, z and t test , re module
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. Explain split(), sub(), subn() methods of “re” module in Python.
Ans: To modify the strings, Python’s “re” module is providing 3 methods. They are:
split() – uses a regex pattern to “split” a given string into a list.
sub() – finds all substrings where the regex pattern matches and then replace them with a different string
subn() – it is similar to sub() and also returns the new string along with the no. of replacements.
3. When should you use a t-test vs a z-test?
-> z tests are a statistical way of testing a hypothesis when either:
We know the population variance, or
We do not know the population variance but our sample size is large n ≥ 30
If we have a sample size of less than 30 and do not know the population variance, then we must use a t-test.
- > t-tests are a statistical way of testing a hypothesis when:
We do not know the population variance
Our sample size is small, n < 30
4. What is the meaning of term weight initialization in neural networks?
In neural networking, weight initialization is one of the essential factors. A bad weight initialization prevents a network from learning. On the other side, a good weight initialization helps in giving a quicker convergence and a better overall error. Biases can be initialized to zero. The standard rule for setting the weights is to be close to zero without being too small.
————————————————————-
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Date: 14-10-2022
Company name: TCS
Role: Data Analyst
Topic: excel, data cleaning, data wrangling, types of error
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 the Type I and Type II errors in Statistics?
In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.
A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.
4. How do you make a dropdown list in MS Excel?
First, click on the Data tab that is present in the ribbon.Under the Data Tools group, select Data Validation.Then navigate to Settings > Allow > List.Select the source you want to provide as a list array.
————————————————————-
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company name: TCS
Role: Data Analyst
Topic: excel, data cleaning, data wrangling, types of error
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 the Type I and Type II errors in Statistics?
In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.
A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.
4. How do you make a dropdown list in MS Excel?
First, click on the Data tab that is present in the ribbon.Under the Data Tools group, select Data Validation.Then navigate to Settings > Allow > List.Select the source you want to provide as a list array.
————————————————————-
🚨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
If you are someone who wanna make their career in Data Science & Analytics domain then you should check our Mega Combo Course Package.
We have designed our courses in such a way that learners can get complete hands-on practical learning experience from day one. For each and every topic you will get tutorial videos for theoretical knowledge and practice assignments for proper conceptual understanding. You will also get explained solution videos of the assignments.
While learning if you will stuck someone then you can use our 1-1 Chat Support for Doubt Clearance. We will assign you Personal Teaching Assistant and they will resolve all of your doubts and queries related to the course. They will be available everyday between 6pm to 12midnight (including weekends also).
We also provide capstone end-to-end projects & case studies that will boost your confidence and enhance your resume.
Our courses are self-paced course with lifetime validity that means you can learn according to your time availability. You can easily manage your work and studies with our course.
Now after completing the course, you will get certificate and offer of Paid Internship at CloudyML. You will get stipend equal to your course fee. So in this way, you also can learn literally for FREE.
We will also help you to improve your resume and send you job opening mails. In your course portal, you will also find sample resumes for reference, interview qna pdf collection, aptitude test quiz etc.
We focus on the skill development of our learners so they can become technically strong enough to crack Data Science interviews and you will not get all these features even in lakhs of rupees courses.
So if you wanna crack interviews of Data Scientist, Data Analyst, Business Analyst, ML Engineer, Research Analyst etc without wasting your lakhs of rupees then Enroll to our course now and start your learning journey today.
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
CHOSEN & TRUSTED BY 10000+ HAPPY LEARNERS 💙
We have designed our courses in such a way that learners can get complete hands-on practical learning experience from day one. For each and every topic you will get tutorial videos for theoretical knowledge and practice assignments for proper conceptual understanding. You will also get explained solution videos of the assignments.
While learning if you will stuck someone then you can use our 1-1 Chat Support for Doubt Clearance. We will assign you Personal Teaching Assistant and they will resolve all of your doubts and queries related to the course. They will be available everyday between 6pm to 12midnight (including weekends also).
We also provide capstone end-to-end projects & case studies that will boost your confidence and enhance your resume.
Our courses are self-paced course with lifetime validity that means you can learn according to your time availability. You can easily manage your work and studies with our course.
Now after completing the course, you will get certificate and offer of Paid Internship at CloudyML. You will get stipend equal to your course fee. So in this way, you also can learn literally for FREE.
We will also help you to improve your resume and send you job opening mails. In your course portal, you will also find sample resumes for reference, interview qna pdf collection, aptitude test quiz etc.
We focus on the skill development of our learners so they can become technically strong enough to crack Data Science interviews and you will not get all these features even in lakhs of rupees courses.
So if you wanna crack interviews of Data Scientist, Data Analyst, Business Analyst, ML Engineer, Research Analyst etc without wasting your lakhs of rupees then Enroll to our course now and start your learning journey today.
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
CHOSEN & TRUSTED BY 10000+ HAPPY LEARNERS 💙
Date: 15-10-2022
Company name: Tredence
Role: ML Engineer
Topic: exploding gradient, ARM, decision tree, standard deviation
1. 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. This is one of the most commonly asked interview questions on machine learning.
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. 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 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.
————————————————————-
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company name: Tredence
Role: ML Engineer
Topic: exploding gradient, ARM, decision tree, standard deviation
1. 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. This is one of the most commonly asked interview questions on machine learning.
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. 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 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.
————————————————————-
🚨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: 18-10-2022
Company name: Brookfield
Role: Data Scientist
Topic: PCA, time series, SQL, tableau
1. 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.
2.What is the bin in tableau?
Bins in tableau are containers of equal size used to store data values fitting in bin size. In other words, bins group the data into groups of equal size or data which can be used in systematic viewing of data. All the discrete fields in tableau can also be considered as set of bins.
3.What’s a Fourier transform?
A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. Or as this more intuitive tutorial puts it, given a smoothie, it’s how we find the recipe. 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.
4. What are Superkey and candidate key in SQL?
A super key may be a single or a combination of keys that help to identify a record in a table. Know that Super keys can have one or more attributes, even though all the attributes are not necessary to identify the records.
A candidate key is the subset of Superkey, which can have one or more than one attribute to identify records in a table. Unlike Superkey, all the attributes of the candidate key must be helpful to identify the records.
Company name: Brookfield
Role: Data Scientist
Topic: PCA, time series, SQL, tableau
1. 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.
2.What is the bin in tableau?
Bins in tableau are containers of equal size used to store data values fitting in bin size. In other words, bins group the data into groups of equal size or data which can be used in systematic viewing of data. All the discrete fields in tableau can also be considered as set of bins.
3.What’s a Fourier transform?
A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. Or as this more intuitive tutorial puts it, given a smoothie, it’s how we find the recipe. 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.
4. What are Superkey and candidate key in SQL?
A super key may be a single or a combination of keys that help to identify a record in a table. Know that Super keys can have one or more attributes, even though all the attributes are not necessary to identify the records.
A candidate key is the subset of Superkey, which can have one or more than one attribute to identify records in a table. Unlike Superkey, all the attributes of the candidate key must be helpful to identify the records.
Date: 17-10-2022
Company name: Elica
Role: Database Developer
Topic: views, record, field, table
1.Define RDBMS.
Answer: Relational Database Management System(RDBMS) is based on a relational model of data that is stored in databases in separate tables and they are related to the use of a common column. Data can be accessed easily from the relational database using Structured Query Language (SQL).
2.Define DML Compiler.
Answer: DML compiler translates DML statements in a query language into a low-level instruction and the generated instruction can be understood by Query Evaluation Engine.
3.Explain the terms ‘Record’, ‘Field’ and ‘Table’ in terms of database.
Answer:
Record: Record is a collection of values or fields of a specific entity. For Example, An employee, Salary account, etc.
Field: A field refers to an area within a record that is reserved for specific data. For Example, Employee ID.
Table: Table is the collection of records of specific types. For Example, the Employee table is a collection of records related to all the employees.
4.Define the relationship between ‘View’ and ‘Data Independence’.
Answer: View is a virtual table that does not have its data on its own rather the data is defined from one or more underlying base tables.
Views account for logical data independence as the growth and restructuring of base tables are not reflected in views.
————————————————————-
🚨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: Elica
Role: Database Developer
Topic: views, record, field, table
1.Define RDBMS.
Answer: Relational Database Management System(RDBMS) is based on a relational model of data that is stored in databases in separate tables and they are related to the use of a common column. Data can be accessed easily from the relational database using Structured Query Language (SQL).
2.Define DML Compiler.
Answer: DML compiler translates DML statements in a query language into a low-level instruction and the generated instruction can be understood by Query Evaluation Engine.
3.Explain the terms ‘Record’, ‘Field’ and ‘Table’ in terms of database.
Answer:
Record: Record is a collection of values or fields of a specific entity. For Example, An employee, Salary account, etc.
Field: A field refers to an area within a record that is reserved for specific data. For Example, Employee ID.
Table: Table is the collection of records of specific types. For Example, the Employee table is a collection of records related to all the employees.
4.Define the relationship between ‘View’ and ‘Data Independence’.
Answer: View is a virtual table that does not have its data on its own rather the data is defined from one or more underlying base tables.
Views account for logical data independence as the growth and restructuring of base tables are not reflected in views.
————————————————————-
🚨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: 18-10-2022
Company name: TigerAnalytics
role: Data Scientist
Topic: Cross Validation, Kernels
1. What’s the difference between a generative and discriminative model?
Answer: A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.
2. What cross-validation technique would you use on a time series dataset?
Answer: Instead of using standard k-folds cross-validation, you have to pay attention to the fact that a time series is not randomly distributed data—it is inherently ordered by chronological order. If a pattern emerges in later time periods, for example, your model may still pick up on it even if that effect doesn’t hold in earlier years!
You’ll want to do something like forward chaining where you’ll be able to model on past data then look at forward-facing data.
• Fold 1 : training [1], test [2]
• Fold 2 : training [1 2], test [3]
• Fold 3 : training [1 2 3], test [4]
• Fold 4 : training [1 2 3 4], test [5]
• Fold 5 : training [1 2 3 4 5], test [6]
3. What’s the “kernel trick” and how is it useful?
Answer: The Kernel trick involves kernel functions that can enable in higher-dimension spaces without explicitly calculating the coordinates of points within that dimension: instead, kernel functions compute the inner products between the images of all pairs of data in a feature space. This allows them the very useful attribute of calculating the coordinates of higher dimensions while being computationally cheaper than the explicit calculation of said coordinates.
————————————————————-
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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
Company name: TigerAnalytics
role: Data Scientist
Topic: Cross Validation, Kernels
1. What’s the difference between a generative and discriminative model?
Answer: A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.
2. What cross-validation technique would you use on a time series dataset?
Answer: Instead of using standard k-folds cross-validation, you have to pay attention to the fact that a time series is not randomly distributed data—it is inherently ordered by chronological order. If a pattern emerges in later time periods, for example, your model may still pick up on it even if that effect doesn’t hold in earlier years!
You’ll want to do something like forward chaining where you’ll be able to model on past data then look at forward-facing data.
• Fold 1 : training [1], test [2]
• Fold 2 : training [1 2], test [3]
• Fold 3 : training [1 2 3], test [4]
• Fold 4 : training [1 2 3 4], test [5]
• Fold 5 : training [1 2 3 4 5], test [6]
3. What’s the “kernel trick” and how is it useful?
Answer: The Kernel trick involves kernel functions that can enable in higher-dimension spaces without explicitly calculating the coordinates of points within that dimension: instead, kernel functions compute the inner products between the images of all pairs of data in a feature space. This allows them the very useful attribute of calculating the coordinates of higher dimensions while being computationally cheaper than the explicit calculation of said coordinates.
————————————————————-
🚨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
What will be the output of the following Python function?
min(max(False,-3,-4), 2,7)
min(max(False,-3,-4), 2,7)
Anonymous Quiz
9%
-4
21%
-3
17%
2
52%
False
Meet Sahaj who completed his B.Tech in Automotive Engineering and then switched to Data Science domain. Right now he is working as a Full Time Data Scientist at Aryng.
We gonna learn from his career transition journey in today's podcast episode of "Career Ki Baat With Akash Raj". It was a great conversation, Do Not Miss This !
Video Link - https://youtu.be/r48piVQfmiE
We gonna learn from his career transition journey in today's podcast episode of "Career Ki Baat With Akash Raj". It was a great conversation, Do Not Miss This !
Video Link - https://youtu.be/r48piVQfmiE
Date: 19-10-2022
Company Name: Walmart
Role: SQL Developer
Topic- renaming col, nested triggers, like operator
1. How to rename column name in SQL Server?
Rename column in SQL: When it comes to SQL Server, it is not possible to rename the column with the help of ALTER TABLE command, we would have to use sp_rename.
2. 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.
3. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.
————————————————————-
🚨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: SQL Developer
Topic- renaming col, nested triggers, like operator
1. How to rename column name in SQL Server?
Rename column in SQL: When it comes to SQL Server, it is not possible to rename the column with the help of ALTER TABLE command, we would have to use sp_rename.
2. 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.
3. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS FROM SCRATCH. CHECK OUT OUR MEGA COMBO COURSE PACKAGE.
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🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS FROM SCRATCH WITHOUT PAYING LAKHS OF RUPEES
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
✅Get Hands-on Practical Learning Experience.
✅1-1 Doubt Clearance Support Everyday
✅100% PAID Internship after course completion
✅Course Completion Certificate
✅Sample Resume & Resume Review Feature
✅Job Opening & Interview QnA Mails
✅Self-Paced Course with Lifetime Course Access & More
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Date: 20-10-2022
Company name: Cognizant
Role: Data Analyst
Topic :Gantt chart, RDBMS, triggers
1. 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.
2. 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.
3. What are Nested Triggers?
Triggers may implement DML by using INSERT, UPDATE, and DELETE statements. These triggers that contain DML and find other triggers for data modification are called Nested Triggers.
4. How does the IF() function in Excel work?
In Excel, the IF() function performs a logical test. It returns a value if the test evaluates to true and another value if the test result is false. It returns the value depending on whether the condition is valid for the entire selected range.
————————————————————-
🚨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: Cognizant
Role: Data Analyst
Topic :Gantt chart, RDBMS, triggers
1. 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.
2. 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.
3. What are Nested Triggers?
Triggers may implement DML by using INSERT, UPDATE, and DELETE statements. These triggers that contain DML and find other triggers for data modification are called Nested Triggers.
4. How does the IF() function in Excel work?
In Excel, the IF() function performs a logical test. It returns a value if the test evaluates to true and another value if the test result is false. It returns the value depending on whether the condition is valid for the entire selected range.
————————————————————-
🚨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
What will be the output of the following Python expression?
round(4.576)
round(4.576)
Anonymous Quiz
24%
4
35%
4.6
33%
5
8%
4.5
Date: 21-10-2022
Company name: IBM
Role: ML Engineer
Topic:
KNN, kmean, cross validation, F1 score, L1, L2 regularisation
1. How is KNN different from k-means clustering?
K-Nearest Neighbors is a supervised classification algorithm, while k-means clustering is an unsupervised clustering algorithm. For K-Nearest Neighbors to work, you need labeled data you want to classify an unlabeled point into. K-means clustering requires only a set of unlabeled points and a threshold: the algorithm will take unlabeled points and gradually learn how to cluster them into groups by computing the mean of the distance between different points.
2. Explain the difference between L1 and L2 regularization.
Answer: L2 regularization tends to spread error among all the terms, while L1 is more binary/sparse, with many variables either being assigned a 1 or 0 in weighting. L1 corresponds to setting a Laplacean prior on the terms, while L2 corresponds to a Gaussian prior.
3. What cross-validation technique would you use on a time series dataset?
Answer: Instead of using standard k-folds cross-validation, you have to pay attention to the fact that a time series is not randomly distributed data—it is inherently ordered by chronological order.
You’ll want to do something like forward chaining where you’ll be able to model on past data then look at forward-facing data.
Fold 1 : training [1], test [2]
Fold 2 : training [1 2], test [3]
Fold 3 : training [1 2 3], test [4]
Fold 4 : training [1 2 3 4], test [5]
4. What’s the F1 score? How would you use it?
Answer: The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst. You would use it in classification tests where true negatives don’t matter much.
————————————————————-
🚨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: ML Engineer
Topic:
KNN, kmean, cross validation, F1 score, L1, L2 regularisation
1. How is KNN different from k-means clustering?
K-Nearest Neighbors is a supervised classification algorithm, while k-means clustering is an unsupervised clustering algorithm. For K-Nearest Neighbors to work, you need labeled data you want to classify an unlabeled point into. K-means clustering requires only a set of unlabeled points and a threshold: the algorithm will take unlabeled points and gradually learn how to cluster them into groups by computing the mean of the distance between different points.
2. Explain the difference between L1 and L2 regularization.
Answer: L2 regularization tends to spread error among all the terms, while L1 is more binary/sparse, with many variables either being assigned a 1 or 0 in weighting. L1 corresponds to setting a Laplacean prior on the terms, while L2 corresponds to a Gaussian prior.
3. What cross-validation technique would you use on a time series dataset?
Answer: Instead of using standard k-folds cross-validation, you have to pay attention to the fact that a time series is not randomly distributed data—it is inherently ordered by chronological order.
You’ll want to do something like forward chaining where you’ll be able to model on past data then look at forward-facing data.
Fold 1 : training [1], test [2]
Fold 2 : training [1 2], test [3]
Fold 3 : training [1 2 3], test [4]
Fold 4 : training [1 2 3 4], test [5]
4. What’s the F1 score? How would you use it?
Answer: The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst. You would use it in classification tests where true negatives don’t matter much.
————————————————————-
🚨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
#Quiz
Python supports the creation of anonymous functions at runtime, using a construct called __________ .
Python supports the creation of anonymous functions at runtime, using a construct called __________ .
Anonymous Quiz
11%
pi
14%
anonymous
71%
lambda
4%
none of the mentioned
Date: 22-10-2022
Company name: Adecco
Role: Database developer
Topic: DBMS, Null values, E-R Model, Lock
1. Mention the issues with traditional file-based systems that make DBMS a better choice?
The absence of indexing in a traditional file-based system leaves us with the only option of scanning the full page and hence making the access of content tedious and super slow. The other issue is redundancy and inconsistency as files have many duplicate and redundant data and changing one of them makes all of them inconsistent. Accessing data is harder in traditional file-based systems because data is unorganized in them. Another issue is the lack of concurrency control, which leads to one operation locking the entire page, as compared to DBMS where multiple operations can work on a single file simultaneously.
2. Are NULL values in a database the same as that of blank space or zero?
No, a NULL value is very different from that of zero and blank space as it represents a value that is assigned, unknown, unavailable, or not applicable as compared to blank space which represents a character and zero represents a number.
Example: NULL value in “number_of_courses” taken by a student represents that its value is unknown whereas 0 in it means that the student hasn’t taken any courses.
3. What is a lock? Explain the major difference between a shared lock and an exclusive lock during a transaction in a database.
A database lock is a mechanism to protect a shared piece of data from getting updated by two or more database users at the same time. When a single database user or session has acquired a lock then no other database user or session can modify that data until the lock is released.
Shared Lock: A shared lock is required for reading a data item and many transactions may hold a lock on the same data item in a shared lock. Multiple transactions are allowed to read the data items in a shared lock.
Exclusive lock: An exclusive lock is a lock on any transaction that is about to perform a write operation. This type of lock doesn’t allow more than one transaction and hence prevents any inconsistency in the database.
4. What is meant by an entity-relationship (E-R) model? Explain the terms Entity, Entity Type, and Entity Set in DBMS.
An entity-relationship model is a diagrammatic approach to a database design where real-world objects are represented as entities and relationships between them are mentioned.
Entity: An entity is defined as a real-world object having attributes that represent characteristics of that particular object. For example, a student, an employee, or a teacher represents an entity.
Entity Type: An entity type is defined as a collection of entities that have the same attributes. For example, a student represents an entity that has attributes such as student_id, student_name, etc.
Entity Set: An entity set can be defined as a set of all the entities present in a specific entity type in a database. For example, a set of all the students, employees, teachers, etc. represent an entity set
Company name: Adecco
Role: Database developer
Topic: DBMS, Null values, E-R Model, Lock
1. Mention the issues with traditional file-based systems that make DBMS a better choice?
The absence of indexing in a traditional file-based system leaves us with the only option of scanning the full page and hence making the access of content tedious and super slow. The other issue is redundancy and inconsistency as files have many duplicate and redundant data and changing one of them makes all of them inconsistent. Accessing data is harder in traditional file-based systems because data is unorganized in them. Another issue is the lack of concurrency control, which leads to one operation locking the entire page, as compared to DBMS where multiple operations can work on a single file simultaneously.
2. Are NULL values in a database the same as that of blank space or zero?
No, a NULL value is very different from that of zero and blank space as it represents a value that is assigned, unknown, unavailable, or not applicable as compared to blank space which represents a character and zero represents a number.
Example: NULL value in “number_of_courses” taken by a student represents that its value is unknown whereas 0 in it means that the student hasn’t taken any courses.
3. What is a lock? Explain the major difference between a shared lock and an exclusive lock during a transaction in a database.
A database lock is a mechanism to protect a shared piece of data from getting updated by two or more database users at the same time. When a single database user or session has acquired a lock then no other database user or session can modify that data until the lock is released.
Shared Lock: A shared lock is required for reading a data item and many transactions may hold a lock on the same data item in a shared lock. Multiple transactions are allowed to read the data items in a shared lock.
Exclusive lock: An exclusive lock is a lock on any transaction that is about to perform a write operation. This type of lock doesn’t allow more than one transaction and hence prevents any inconsistency in the database.
4. What is meant by an entity-relationship (E-R) model? Explain the terms Entity, Entity Type, and Entity Set in DBMS.
An entity-relationship model is a diagrammatic approach to a database design where real-world objects are represented as entities and relationships between them are mentioned.
Entity: An entity is defined as a real-world object having attributes that represent characteristics of that particular object. For example, a student, an employee, or a teacher represents an entity.
Entity Type: An entity type is defined as a collection of entities that have the same attributes. For example, a student represents an entity that has attributes such as student_id, student_name, etc.
Entity Set: An entity set can be defined as a set of all the entities present in a specific entity type in a database. For example, a set of all the students, employees, teachers, etc. represent an entity set
Learn Python by making a Game🤩
Watch our latest Hungama tutorial videos on YouTube. Don't Miss it
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Watch our latest Hungama tutorial videos on YouTube. Don't Miss it
👉🏻 https://youtu.be/bmGxxeLrTYo
Date: 23-10-2022
Company name: Amazon
Role: Data Scientist
Topic: data analysis, ensemble, types of error, F1 score
1. What are the common problems that data analysts encounter during analysis?
The common problems steps involved in any analytics project are:
Handling duplicate data
Collecting the meaningful right data at the right time
Handling data purging and storage problems
Making data secure and dealing with compliance issues
2. Explain the Type I and Type II errors in Statistics?
In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.
A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.
3. What’s the F1 score? How would you use it?
The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst.
4. Name an example where ensemble techniques might be useful?
Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data). You could list some examples of ensemble methods (bagging, boosting, the “bucket of models” method) and demonstrate how they could increase predictive power.
————————————————————-
🚨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: Amazon
Role: Data Scientist
Topic: data analysis, ensemble, types of error, F1 score
1. What are the common problems that data analysts encounter during analysis?
The common problems steps involved in any analytics project are:
Handling duplicate data
Collecting the meaningful right data at the right time
Handling data purging and storage problems
Making data secure and dealing with compliance issues
2. Explain the Type I and Type II errors in Statistics?
In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.
A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.
3. What’s the F1 score? How would you use it?
The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst.
4. Name an example where ensemble techniques might be useful?
Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data). You could list some examples of ensemble methods (bagging, boosting, the “bucket of models” method) and demonstrate how they could increase predictive power.
————————————————————-
🚨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