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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
➡️ Why You Should Go With Our Data Science & Analytics Course?
✅If you want to get complete hands-on practical learning experience then this course is for you. We have covered everything from basic to advanced level with tutorial videos and practice assignments for each and every topic. You will be able to practice from concepts from day 1 and it will boost up your confidence to the next level.
✅If you do not want to waste your time and effort on searching doubts on the internet while learning then this course is for you. We will assign you personal teaching assistant for your doubt clearance. They will be available everyday between 6pm to 12 midnight and they will resolve all of your doubts and queries regarding the course.
✅If you are from non-coding background and want to learn everything from scratch then this course is for you. You will become technically strong enough so you can crack any Data Science Interview. In fact, 35% of our learners are from non-coding domain like mechanical, civil, electrical, commerce, arts, MBA, etc and so many of them have successfully switched to Data Science domain.
✅If you are looking for internship after learning all the tools then this course is for you. You will get 100% Paid Internship after completing the course.
Like these, there are so many more reasons to go with our course. You will not get all these features in a single package at such affordable price anywhere. Our course is helping more than 10000+ Students in their career transition journey, you also can be a part. ENROLL TODAY.
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
✅If you want to get complete hands-on practical learning experience then this course is for you. We have covered everything from basic to advanced level with tutorial videos and practice assignments for each and every topic. You will be able to practice from concepts from day 1 and it will boost up your confidence to the next level.
✅If you do not want to waste your time and effort on searching doubts on the internet while learning then this course is for you. We will assign you personal teaching assistant for your doubt clearance. They will be available everyday between 6pm to 12 midnight and they will resolve all of your doubts and queries regarding the course.
✅If you are from non-coding background and want to learn everything from scratch then this course is for you. You will become technically strong enough so you can crack any Data Science Interview. In fact, 35% of our learners are from non-coding domain like mechanical, civil, electrical, commerce, arts, MBA, etc and so many of them have successfully switched to Data Science domain.
✅If you are looking for internship after learning all the tools then this course is for you. You will get 100% Paid Internship after completing the course.
Like these, there are so many more reasons to go with our course. You will not get all these features in a single package at such affordable price anywhere. Our course is helping more than 10000+ Students in their career transition journey, you also can be a part. ENROLL TODAY.
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Date: 24-10-2022
Company name: Tredence
Role: ML Engineer
Topic: ARM, Decision tree, Exploding gradient
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.
===================================
5. How I can switch my career to Data Science domain ?
Just enroll in CloudyML Data Science & Analytics Mega Combo Course and you will get complete roadmap for career transition to Data Science. It is helping more than 10000+ students to achieve their goals. Get complete hands-on practical learning experience, 1-1 Doubt Clearance Support, Paid Internship after course completion and more. Click on the link below and Enroll Today. Course available @ 80% Discount.
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company name: Tredence
Role: ML Engineer
Topic: ARM, Decision tree, Exploding gradient
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.
===================================
5. How I can switch my career to Data Science domain ?
Just enroll in CloudyML Data Science & Analytics Mega Combo Course and you will get complete roadmap for career transition to Data Science. It is helping more than 10000+ students to achieve their goals. Get complete hands-on practical learning experience, 1-1 Doubt Clearance Support, Paid Internship after course completion and more. Click on the link below and Enroll Today. Course available @ 80% Discount.
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
❤1
Date: 25-10-2022
Company name: Musigma
Role: Data Analyst
Topic: Macro, DML, Operators, Relationship in powerBI
1.What is the purpose of ‘is’, ‘not’ and ‘in’ operators?
Ans: Operators are special functions. They take one or more values and produce a corresponding result.
is: returns true when 2 operands are true (Example: “a” is ‘a’)
not: returns the inverse of the boolean value
in: checks if some element is present in some sequence.
2. What is the update command in SQL?
The update command comes under the DML(Data Manipulation Langauge) part of sql and is used to update the existing data in the table.
3. Explain Macro in MS-Excel.
Macros are used for iterating over a group of tasks. Users can create macros for their customized repetitive functions and instructions. Macros can be either written or recorded depending on the user.
4. Explain how relationships are defined in Power BI Desktop?
Relationships between tables are defined in two ways:
Manually - Relationships between tables are manually defined using primary and foreign keys.
Automatic - When enabled, this automated feature of Power BI detects relationships between tables and creates them automatically.
————————————————————-
🚨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. Offer Ending Tonight.
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company name: Musigma
Role: Data Analyst
Topic: Macro, DML, Operators, Relationship in powerBI
1.What is the purpose of ‘is’, ‘not’ and ‘in’ operators?
Ans: Operators are special functions. They take one or more values and produce a corresponding result.
is: returns true when 2 operands are true (Example: “a” is ‘a’)
not: returns the inverse of the boolean value
in: checks if some element is present in some sequence.
2. What is the update command in SQL?
The update command comes under the DML(Data Manipulation Langauge) part of sql and is used to update the existing data in the table.
3. Explain Macro in MS-Excel.
Macros are used for iterating over a group of tasks. Users can create macros for their customized repetitive functions and instructions. Macros can be either written or recorded depending on the user.
4. Explain how relationships are defined in Power BI Desktop?
Relationships between tables are defined in two ways:
Manually - Relationships between tables are manually defined using primary and foreign keys.
Automatic - When enabled, this automated feature of Power BI detects relationships between tables and creates them automatically.
————————————————————-
🚨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. Offer Ending Tonight.
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Date: 26-10-2022
Company name: Flipkart
Role: ML Engineer
Topic: Cluster sampling, SVM, Correlation/Covariance, P value
1. What are Support Vectors in SVM?
A Support Vector Machine (SVM) is an algorithm that tries to fit a line (or plane or hyperplane) between the different classes that maximizes the distance from the line to the points of the classes.
In this way, it tries to find a robust separation between the classes. The Support Vectors are the points of the edge of the dividing hyperplane.
2. Explain Correlation and Covariance?
Covariance signifies the direction of the linear relationship between two variables, whereas correlation indicates both the direction and strength of the linear relationship between variables.
3.What is the cluster sampling techniques used for sampling?
Cluster sampling also involves dividing the population into sub-populations, but each subpopulation should have analogous characteristics to that of the whole sample. Rather than sampling individuals from each subpopulation, you randomly select the entire subpopulation.
4. 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.
————————————————————-
🚨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. Offer Ending Tonight.
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company name: Flipkart
Role: ML Engineer
Topic: Cluster sampling, SVM, Correlation/Covariance, P value
1. What are Support Vectors in SVM?
A Support Vector Machine (SVM) is an algorithm that tries to fit a line (or plane or hyperplane) between the different classes that maximizes the distance from the line to the points of the classes.
In this way, it tries to find a robust separation between the classes. The Support Vectors are the points of the edge of the dividing hyperplane.
2. Explain Correlation and Covariance?
Covariance signifies the direction of the linear relationship between two variables, whereas correlation indicates both the direction and strength of the linear relationship between variables.
3.What is the cluster sampling techniques used for sampling?
Cluster sampling also involves dividing the population into sub-populations, but each subpopulation should have analogous characteristics to that of the whole sample. Rather than sampling individuals from each subpopulation, you randomly select the entire subpopulation.
4. 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.
————————————————————-
🚨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. Offer Ending Tonight.
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Date: 27-10-2022
Company name: Swiggy
Role: Data Scientist
Topic: K means, recommendation, confusion matrix, boxplot vs histplot
1. How can you select k for k-means?
We use the elbow method to select k for k-means clustering. The idea of the elbow method is to run k-means clustering on the data set where 'k' is the number of clusters.
Within the sum of squares (WSS), it is defined as the sum of the squared distance between each member of the cluster and its centroid.
2. 'People who bought this also bought…' recommendations seen on Amazon are a result of which algorithm?
The recommendation engine is accomplished with collaborative filtering. Collaborative filtering explains the behavior of other users and their purchase history in terms of ratings, selection, etc.
The engine makes predictions on what might interest a person based on the preferences of other users. In this algorithm, item features are unknown.
3. What is a Confusion Matrix?
The Confusion Matrix is the summary of prediction results of a particular problem. It is a table that is used to describe the performance of the model. The Confusion Matrix is an n*n matrix that evaluates the performance of the classification model.
4. What is the difference between a box plot and a histogram?
The frequency of a certain feature’s values is denoted visually by both box plots
and histograms.
Boxplots are more often used in comparing several datasets and compared to histograms, take less space and contain fewer details. Histograms are used to know and understand the probability distribution underlying a dataset.
————————————————————-
🚨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, 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. Offer Ending Tonight.
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company name: Swiggy
Role: Data Scientist
Topic: K means, recommendation, confusion matrix, boxplot vs histplot
1. How can you select k for k-means?
We use the elbow method to select k for k-means clustering. The idea of the elbow method is to run k-means clustering on the data set where 'k' is the number of clusters.
Within the sum of squares (WSS), it is defined as the sum of the squared distance between each member of the cluster and its centroid.
2. 'People who bought this also bought…' recommendations seen on Amazon are a result of which algorithm?
The recommendation engine is accomplished with collaborative filtering. Collaborative filtering explains the behavior of other users and their purchase history in terms of ratings, selection, etc.
The engine makes predictions on what might interest a person based on the preferences of other users. In this algorithm, item features are unknown.
3. What is a Confusion Matrix?
The Confusion Matrix is the summary of prediction results of a particular problem. It is a table that is used to describe the performance of the model. The Confusion Matrix is an n*n matrix that evaluates the performance of the classification model.
4. What is the difference between a box plot and a histogram?
The frequency of a certain feature’s values is denoted visually by both box plots
and histograms.
Boxplots are more often used in comparing several datasets and compared to histograms, take less space and contain fewer details. Histograms are used to know and understand the probability distribution underlying a dataset.
————————————————————-
🚨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, 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. Offer Ending Tonight.
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
❤1
Anonymous Quiz
3%
for
3%
while
80%
do-while
13%
None of the above
Date: 28-10-2022
Company name: Hitachi
Role: SQL Developer
Topic :SQL vs MySQL, pattern match, cross join, stored procedure
1. What is the difference between SQL and MySQL?
SQL is a standard language for retrieving and manipulating structured databases. On the contrary, MySQL is a relational database management system, like SQL Server, Oracle or IBM DB2, that is used to manage SQL databases.
2. What is a Cross-Join?
Cross join can be defined as a cartesian product of the two tables included in the join. The table after join contains the same number of rows as in the cross-product of the number of rows in the two tables. If a WHERE clause is used in cross join then the query will work like an INNER JOIN.
3. What is a Stored Procedure?
A stored procedure is a subroutine available to applications that access a relational database management system (RDBMS). Such procedures are stored in the database data dictionary. The sole disadvantage of stored procedure is that it can be executed nowhere except in the database and occupies more memory in the database server.
4. What is Pattern Matching in SQL?
SQL pattern matching provides for pattern search in data if you have no clue as to what that word should be. This kind of SQL query uses wildcards to match a string pattern, rather than writing the exact word. The LIKE operator is used in conjunction with SQL Wildcards to fetch the required information.
————————————————————-
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This course package, you are getting Python, R, SQL, Statistics, Machine Learning, Deep Learning, 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. Offer Ending Tonight.
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company name: Hitachi
Role: SQL Developer
Topic :SQL vs MySQL, pattern match, cross join, stored procedure
1. What is the difference between SQL and MySQL?
SQL is a standard language for retrieving and manipulating structured databases. On the contrary, MySQL is a relational database management system, like SQL Server, Oracle or IBM DB2, that is used to manage SQL databases.
2. What is a Cross-Join?
Cross join can be defined as a cartesian product of the two tables included in the join. The table after join contains the same number of rows as in the cross-product of the number of rows in the two tables. If a WHERE clause is used in cross join then the query will work like an INNER JOIN.
3. What is a Stored Procedure?
A stored procedure is a subroutine available to applications that access a relational database management system (RDBMS). Such procedures are stored in the database data dictionary. The sole disadvantage of stored procedure is that it can be executed nowhere except in the database and occupies more memory in the database server.
4. What is Pattern Matching in SQL?
SQL pattern matching provides for pattern search in data if you have no clue as to what that word should be. This kind of SQL query uses wildcards to match a string pattern, rather than writing the exact word. The LIKE operator is used in conjunction with SQL Wildcards to fetch the required information.
————————————————————-
🚨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, 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. Offer Ending Tonight.
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
🚨JOB OPENING ALERT🚨
Role: Data Analyst
Company: Vellore Institute Of Technology (Vit)
Exp: Fresher(0-5yr)
Link: https://ai.cloudyml.com/3TMwnll
Role - Business Analyst Intern
Company: Aidash
Exp: Fresher
Link: https://ai.cloudyml.com/3FydyxY
Role: Junior Data Scientist
Company: Multiplr
Exp: Min 1 yr required but you are advised to apply anyway
Link: https://ai.cloudyml.com/3Dhnh9h
Role: Data Scientist
Company: Zeta Global
Exp: Fresher
Link: https://ai.cloudyml.com/3W3BP4W
All the best 💙
Role: Data Analyst
Company: Vellore Institute Of Technology (Vit)
Exp: Fresher(0-5yr)
Link: https://ai.cloudyml.com/3TMwnll
Role - Business Analyst Intern
Company: Aidash
Exp: Fresher
Link: https://ai.cloudyml.com/3FydyxY
Role: Junior Data Scientist
Company: Multiplr
Exp: Min 1 yr required but you are advised to apply anyway
Link: https://ai.cloudyml.com/3Dhnh9h
Role: Data Scientist
Company: Zeta Global
Exp: Fresher
Link: https://ai.cloudyml.com/3W3BP4W
All the best 💙
🙏1
Date: 29-10-2022
Role: Data Analyst
Company – MorningStar
Topic: Python, SQL, PowerBI
1. Is indentation required in python?
Indentation is necessary for Python. It specifies a block of code. All code within loops, classes, functions, etc is specified within an indented block. It is usually done using four space characters. If your code is not indented necessarily, it will not execute accurately and will throw errors as well.
2. What are Entities and Relationships?
Entity: An entity can be a real-world object that can be easily identifiable. For example, in a college database, students, professors, workers, departments, and projects can be referred to as entities.
Relationships: Relations or links between entities that have something to do with each other. For example – The employee’s table in a company’s database can be associated with the salary table in the same database.
3. 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.
4. What is Auto Increment?
Auto increment keyword allows the user to create a unique number to be generated when a new record is inserted into the table. AUTO INCREMENT keyword can be used in Oracle and IDENTITY keyword can be used in SQL SERVER.
Mostly this keyword can be used whenever PRIMARY KEY is used.
5. Which operator is used in query for pattern matching?
LIKE operator is used for pattern matching, and it can be used as -.
1. % – Matches zero or more characters.
2. _(Underscore) – Matching exactly one character.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS FROM SCRATCH. CHECK OUT OUR MEGA COMBO COURSE PACKAGE.
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Role: Data Analyst
Company – MorningStar
Topic: Python, SQL, PowerBI
1. Is indentation required in python?
Indentation is necessary for Python. It specifies a block of code. All code within loops, classes, functions, etc is specified within an indented block. It is usually done using four space characters. If your code is not indented necessarily, it will not execute accurately and will throw errors as well.
2. What are Entities and Relationships?
Entity: An entity can be a real-world object that can be easily identifiable. For example, in a college database, students, professors, workers, departments, and projects can be referred to as entities.
Relationships: Relations or links between entities that have something to do with each other. For example – The employee’s table in a company’s database can be associated with the salary table in the same database.
3. 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.
4. What is Auto Increment?
Auto increment keyword allows the user to create a unique number to be generated when a new record is inserted into the table. AUTO INCREMENT keyword can be used in Oracle and IDENTITY keyword can be used in SQL SERVER.
Mostly this keyword can be used whenever PRIMARY KEY is used.
5. Which operator is used in query for pattern matching?
LIKE operator is used for pattern matching, and it can be used as -.
1. % – Matches zero or more characters.
2. _(Underscore) – Matching exactly one character.
————————————————————-
🚨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, 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
Which of the following Python statements will result in the output: 6?
A = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
A = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
Anonymous Quiz
7%
A[2][1]
57%
A[1][2]
13%
A[3][2]
24%
A[2][3]
Learn Data Analysis Using Excel with UBER Drive Case Study from our latest YouTube video. Link given below.
➡️ https://youtu.be/e6lIOVIb_eY
➡️ https://youtu.be/e6lIOVIb_eY
Date: 30-10-2022
Company name: Walmart
Role: Data Scientist
Topic: PCA, outlier treatment, information gain, tpr-fpr
1. What are dimensionality reduction and its benefits?
The Dimensionality reduction refers to the process of converting a data set with vast dimensions into data with fewer dimensions (fields) to convey similar information concisely.
This reduction helps in compressing data and reducing storage space. It also reduces computation time as fewer dimensions lead to less computing. It removes redundant features; for example, there's no point in storing a value in two different units (meters and inches).
2. How can outlier values be treated?
Try a different model. Data detected as outliers by linear models can be fit by nonlinear models. Therefore, be sure you are choosing the correct model.
Try normalizing the data. This way, the extreme data points are pulled to a similar range.
You can use algorithms that are less affected by outliers; an example would be random forests.
3. What do you understand about the true-positive rate and false-positive rate?
TRUE-POSITIVE RATE: The true-positive rate gives the proportion of correct predictions of the positive class. It is also used to measure the percentage of actual positives that are accurately verified.
FALSE-POSITIVE RATE: The false-positive rate gives the proportion of incorrect predictions of the positive class. A false positive determines something is true when that is initially false.
4. What information is gained in a decision tree algorithm?
Information gain is the expected reduction in entropy. Information gain decides the building of the tree. Information Gain makes the decision tree smarter. Information gain includes parent node R and a set E of K training examples. It calculates the difference between entropy before and after the split.
————————————————————-
🚨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, 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: Data Scientist
Topic: PCA, outlier treatment, information gain, tpr-fpr
1. What are dimensionality reduction and its benefits?
The Dimensionality reduction refers to the process of converting a data set with vast dimensions into data with fewer dimensions (fields) to convey similar information concisely.
This reduction helps in compressing data and reducing storage space. It also reduces computation time as fewer dimensions lead to less computing. It removes redundant features; for example, there's no point in storing a value in two different units (meters and inches).
2. How can outlier values be treated?
Try a different model. Data detected as outliers by linear models can be fit by nonlinear models. Therefore, be sure you are choosing the correct model.
Try normalizing the data. This way, the extreme data points are pulled to a similar range.
You can use algorithms that are less affected by outliers; an example would be random forests.
3. What do you understand about the true-positive rate and false-positive rate?
TRUE-POSITIVE RATE: The true-positive rate gives the proportion of correct predictions of the positive class. It is also used to measure the percentage of actual positives that are accurately verified.
FALSE-POSITIVE RATE: The false-positive rate gives the proportion of incorrect predictions of the positive class. A false positive determines something is true when that is initially false.
4. What information is gained in a decision tree algorithm?
Information gain is the expected reduction in entropy. Information gain decides the building of the tree. Information Gain makes the decision tree smarter. Information gain includes parent node R and a set E of K training examples. It calculates the difference between entropy before and after the split.
————————————————————-
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#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
79%
lambda
8%
pi
10%
anonymous
3%
none of the mentioned
Date: 31-10-2022
Company name: Elica
Role: ML Engineer
Topic: PCA, SGD, decision tree, ARM
1. What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?
Gradient Descent and Stochastic Gradient Descent are the algorithms that find the set of parameters that will minimize a loss function.
The difference is that in Gradient Descent, all training samples are evaluated for each set of parameters. While in Stochastic Gradient Descent only one training sample is evaluated for the set of parameters identified.
2. Can you mention some advantages and disadvantages of decision trees?
The advantages of decision trees are that they are easier to interpret, are nonparametric and hence robust to outliers, and have relatively few parameters to tune.
On the other hand, the disadvantage is that they are prone to overfitting.
3. What do you mean by Associative Rule Mining (ARM)?
Associative Rule Mining is one of the techniques to discover patterns in data like features (dimensions) which occur together and features (dimensions) which are correlated. It is mostly used in Market-based Analysis to find how frequently an itemset occurs in a transaction. Association rules have to satisfy minimum support and minimum confidence at the very same time.
4. What is the Principle Component Analysis?
The idea here is to reduce the dimensionality of the data set by reducing the number of variables that are correlated with each other. Although the variation needs to be retained to the maximum extent.
The variables are transformed into a new set of variables that are known as Principal Components’. These PCs are the eigenvectors of a covariance matrix and therefore are orthogonal.
————————————————————-
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Company name: Elica
Role: ML Engineer
Topic: PCA, SGD, decision tree, ARM
1. What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?
Gradient Descent and Stochastic Gradient Descent are the algorithms that find the set of parameters that will minimize a loss function.
The difference is that in Gradient Descent, all training samples are evaluated for each set of parameters. While in Stochastic Gradient Descent only one training sample is evaluated for the set of parameters identified.
2. Can you mention some advantages and disadvantages of decision trees?
The advantages of decision trees are that they are easier to interpret, are nonparametric and hence robust to outliers, and have relatively few parameters to tune.
On the other hand, the disadvantage is that they are prone to overfitting.
3. What do you mean by Associative Rule Mining (ARM)?
Associative Rule Mining is one of the techniques to discover patterns in data like features (dimensions) which occur together and features (dimensions) which are correlated. It is mostly used in Market-based Analysis to find how frequently an itemset occurs in a transaction. Association rules have to satisfy minimum support and minimum confidence at the very same time.
4. What is the Principle Component Analysis?
The idea here is to reduce the dimensionality of the data set by reducing the number of variables that are correlated with each other. Although the variation needs to be retained to the maximum extent.
The variables are transformed into a new set of variables that are known as Principal Components’. These PCs are the eigenvectors of a covariance matrix and therefore are orthogonal.
————————————————————-
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Date: 01-11-2022
Company name: TREDENCE
Role: Data Analyst
Topic: OLTP, data profiling, data mining, row level security
1. Write the difference between data mining and data profiling.
Data mining Process: It generally involves analyzing data to find relations that were not previously discovered. It involves analyzing large datasets to determine trends and patterns in them.
Data Profiling Process: It generally involves analyzing that data's individual attributes. In this case, the emphasis is on providing useful information on data attributes such as data type, frequency, etc.
2. What is row-level security?
Row-level security limits the data a user can view and has access to, and it relies on filters. Users can define the rules and roles in Power BI Desktop and also publish them to Power BI Service to configure row-level security.
3. What is OLTP?
OLTP stands for Online Transaction Processing, is a class of software applications capable of supporting transaction-oriented programs. An essential attribute of an OLTP system is its ability to maintain concurrency. To avoid single points of failure, OLTP systems are often decentralized. These systems are usually designed for a large number of users who conduct short transactions
————————————————————-
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Company name: TREDENCE
Role: Data Analyst
Topic: OLTP, data profiling, data mining, row level security
1. Write the difference between data mining and data profiling.
Data mining Process: It generally involves analyzing data to find relations that were not previously discovered. It involves analyzing large datasets to determine trends and patterns in them.
Data Profiling Process: It generally involves analyzing that data's individual attributes. In this case, the emphasis is on providing useful information on data attributes such as data type, frequency, etc.
2. What is row-level security?
Row-level security limits the data a user can view and has access to, and it relies on filters. Users can define the rules and roles in Power BI Desktop and also publish them to Power BI Service to configure row-level security.
3. What is OLTP?
OLTP stands for Online Transaction Processing, is a class of software applications capable of supporting transaction-oriented programs. An essential attribute of an OLTP system is its ability to maintain concurrency. To avoid single points of failure, OLTP systems are often decentralized. These systems are usually designed for a large number of users who conduct short transactions
————————————————————-
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Date: 02-11-2022
Company: Walmart
Role: Data Scientist
Topic: Neural networks, Joins, Views, Cross Validation
1.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.
2.What is Cross-validation in Machine Learning?
Cross-validation allows a system to increase the performance of the given Machine Learning algorithm. 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
3.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.
4. What are the types of views in SQL?
In SQL, the views are classified into four types. They are:
Simple View: A view that is based on a single table and does not have a GROUP BY clause or other features.
Complex View: A view that is built from several tables and includes a GROUP BY clause as well as functions.
Inline View: A view that is built on a subquery in the FROM clause, which provides a temporary table and simplifies a complicated query.
Materialized View: A view that saves both the definition and the details. It builds data replicas by physically preserving them.
————————————————————-
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Company: Walmart
Role: Data Scientist
Topic: Neural networks, Joins, Views, Cross Validation
1.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.
2.What is Cross-validation in Machine Learning?
Cross-validation allows a system to increase the performance of the given Machine Learning algorithm. 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
3.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.
4. What are the types of views in SQL?
In SQL, the views are classified into four types. They are:
Simple View: A view that is based on a single table and does not have a GROUP BY clause or other features.
Complex View: A view that is built from several tables and includes a GROUP BY clause as well as functions.
Inline View: A view that is built on a subquery in the FROM clause, which provides a temporary table and simplifies a complicated query.
Materialized View: A view that saves both the definition and the details. It builds data replicas by physically preserving them.
————————————————————-
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🆒1
Date: 03-11-2022
Company name: TCS
Role: Data Scientist
Topic: slicing, nested triggers, ensemble learning , heatmap
1. What is slicing in Python?
Slicing is used to access parts of sequences like lists, tuples, and strings. The syntax of slicing is-[start:end:step]. The step can be omitted as well. When we write [start:end] this returns all the elements of the sequence from the start (inclusive) till the end-1 element. If the start or end element is negative i, it means the ith element from the end. The step indicates the jump or how many elements have to be skipped.
2. What do you mean by ensemble learning?
Numerous models, such as classifiers are strategically made and combined to solve a specific computational program which is known as ensemble learning. The ensemble methods are also known as committee-based learning or learning multiple classifier systems. One of the most suitable examples of ensemble modeling is the random forest trees where several decision trees are used to predict outcomes.
3. What are Nested Triggers in SQL?
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. Define Heat Map?
A heat map is a graphical representation of data that uses the color-coding technique to represent different values of data. As the marks heat up due to their higher value, dark colors will be shown on the map.
————————————————————-
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Company name: TCS
Role: Data Scientist
Topic: slicing, nested triggers, ensemble learning , heatmap
1. What is slicing in Python?
Slicing is used to access parts of sequences like lists, tuples, and strings. The syntax of slicing is-[start:end:step]. The step can be omitted as well. When we write [start:end] this returns all the elements of the sequence from the start (inclusive) till the end-1 element. If the start or end element is negative i, it means the ith element from the end. The step indicates the jump or how many elements have to be skipped.
2. What do you mean by ensemble learning?
Numerous models, such as classifiers are strategically made and combined to solve a specific computational program which is known as ensemble learning. The ensemble methods are also known as committee-based learning or learning multiple classifier systems. One of the most suitable examples of ensemble modeling is the random forest trees where several decision trees are used to predict outcomes.
3. What are Nested Triggers in SQL?
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. Define Heat Map?
A heat map is a graphical representation of data that uses the color-coding technique to represent different values of data. As the marks heat up due to their higher value, dark colors will be shown on the map.
————————————————————-
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❤1👏1
Date: 04-11-2022
Company name: Accenture
Role: SQL Developer
Topic: Unique constraint, self join, case statement, between and in operator
1. What is a UNIQUE constraint?
The UNIQUE Constraint prevents identical values in a column from appearing in two records. The UNIQUE constraint guarantees that every value in a column is unique.
2. 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.
3. What is the case when in SQL Server?
The CASE statement is used to construct logic in which one column’s value is determined by the values of other columns. The condition to be tested is specified by the WHEN statement. If the WHEN condition returns TRUE, the THEN sentence explains what to do.
When none of the WHEN conditions return true, the ELSE statement is executed. The END keyword brings the CASE statement to a close.
4. What is the main difference between ‘BETWEEN’ and ‘IN’ condition operators?
BETWEEN operator is used to display rows based on a range of values in a row whereas the IN condition operator is used to check for values contained in a specific set of values.
Company name: Accenture
Role: SQL Developer
Topic: Unique constraint, self join, case statement, between and in operator
1. What is a UNIQUE constraint?
The UNIQUE Constraint prevents identical values in a column from appearing in two records. The UNIQUE constraint guarantees that every value in a column is unique.
2. 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.
3. What is the case when in SQL Server?
The CASE statement is used to construct logic in which one column’s value is determined by the values of other columns. The condition to be tested is specified by the WHEN statement. If the WHEN condition returns TRUE, the THEN sentence explains what to do.
When none of the WHEN conditions return true, the ELSE statement is executed. The END keyword brings the CASE statement to a close.
4. What is the main difference between ‘BETWEEN’ and ‘IN’ condition operators?
BETWEEN operator is used to display rows based on a range of values in a row whereas the IN condition operator is used to check for values contained in a specific set of values.