In this tutorial we will work on call centre data to see the satisfaction of the customers after their call with the representative.
Video Link - https://youtu.be/6thFY-JgfpM
Video Link - https://youtu.be/6thFY-JgfpM
Date: 03-12-2022
Company name: Blinkit
Role: ML Engineer
Topic: Vectorization, regression & classification, bagging & boosting, f1 score
1. What is Vectorization in Python pandas?
Ans: The procedure of executing operations on the full array is known as vectorization. This is intended to limit the number of iterations that the methods do. Pandas have a series of vectorized methods, such as string functions and aggregations, that are optimized for use with series and dataframes.
2. What is the difference between classification and regression?
Regression and Classification algorithms are Supervised Learning algorithms.The main difference between Regression and Classification algorithms that Regression algorithms are used to predict the continuous values such as price, salary, age, etc. and Classification algorithms are used to predict/Classify the discrete values such as Male or Female, True or False, Spam or Not Spam, etc.
3. What are the similarities between bagging and boosting in Machine Learning?
Similarities of Bagging and Boosting
Both are the ensemble methods to get N learns from 1 learner.
Both generate several training data sets with random sampling.
Both generate the final result by taking the average of N learners.
Both reduce variance and provide higher scalability.
4. What do you understand by the F1 score?
The F1 score represents the measurement of a model's performance. It is referred to as a weighted average of the precision and recall of a model. The results tending to 1 are considered as the best, and those tending to 0 are the worst. It could be used in classification tests, where true negatives don't matter much.
———————————————————-
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Company name: Blinkit
Role: ML Engineer
Topic: Vectorization, regression & classification, bagging & boosting, f1 score
1. What is Vectorization in Python pandas?
Ans: The procedure of executing operations on the full array is known as vectorization. This is intended to limit the number of iterations that the methods do. Pandas have a series of vectorized methods, such as string functions and aggregations, that are optimized for use with series and dataframes.
2. What is the difference between classification and regression?
Regression and Classification algorithms are Supervised Learning algorithms.The main difference between Regression and Classification algorithms that Regression algorithms are used to predict the continuous values such as price, salary, age, etc. and Classification algorithms are used to predict/Classify the discrete values such as Male or Female, True or False, Spam or Not Spam, etc.
3. What are the similarities between bagging and boosting in Machine Learning?
Similarities of Bagging and Boosting
Both are the ensemble methods to get N learns from 1 learner.
Both generate several training data sets with random sampling.
Both generate the final result by taking the average of N learners.
Both reduce variance and provide higher scalability.
4. What do you understand by the F1 score?
The F1 score represents the measurement of a model's performance. It is referred to as a weighted average of the precision and recall of a model. The results tending to 1 are considered as the best, and those tending to 0 are the worst. It could be used in classification tests, where true negatives don't matter much.
———————————————————-
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🔥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
73%
lambda
3%
none of the mentioned
⬇️ Download Hand Written Notes of Some Important Interview QnAs of Excel & R
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Date: 05-12-2022
Company name: Datanyze
Role: SQL Developer
Topic: olap, OLTP, coalesce, foreign key
1. What are the differences between OLTP and OLAP?
OLTP stands for online transaction processing, whereas OLAP stands for online analytical processing. OLTP is an online database modification system, whereas OLAP is an online database query response system.
2. How to create empty tables with the same structure as another table?
To create empty tables:
Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure.
3. What is a Foreign key in SQL?
Foreign key maintains referential integrity by enforcing a link between the data in two tables.
The foreign key in the child table references the primary key in the parent table.
The foreign key constraint prevents actions that would destroy links between the child and parent tables.
4. What are the syntax and use of the COALESCE function?
From a succession of expressions, the COALESCE function returns the first non-NULL value. The expressions are evaluated in the order that they are supplied, and the function’s result is the first non-null value. Only if all of the inputs are null does the COALESCE method return NULL.
The syntax of COALESCE function is COALESCE (exp1, exp2, …. expn)
———————————————————-
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Company name: Datanyze
Role: SQL Developer
Topic: olap, OLTP, coalesce, foreign key
1. What are the differences between OLTP and OLAP?
OLTP stands for online transaction processing, whereas OLAP stands for online analytical processing. OLTP is an online database modification system, whereas OLAP is an online database query response system.
2. How to create empty tables with the same structure as another table?
To create empty tables:
Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure.
3. What is a Foreign key in SQL?
Foreign key maintains referential integrity by enforcing a link between the data in two tables.
The foreign key in the child table references the primary key in the parent table.
The foreign key constraint prevents actions that would destroy links between the child and parent tables.
4. What are the syntax and use of the COALESCE function?
From a succession of expressions, the COALESCE function returns the first non-NULL value. The expressions are evaluated in the order that they are supplied, and the function’s result is the first non-null value. Only if all of the inputs are null does the COALESCE method return NULL.
The syntax of COALESCE function is COALESCE (exp1, exp2, …. expn)
———————————————————-
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Date: 06-12-2022
Company name: Musigma
Role: Data Scientist
Topic : decision tree, loss function, cost function, root cause analysis, array vs list
1. How does a Decision Tree handle continuous(numerical) features?
Ans.
Decision Trees handle continuous features by converting these continuous features to a threshold-based boolean feature.
To decide The threshold value, we use the concept of Information Gain, choosing that threshold that maximizes the information gain.
2. What are Loss Function and Cost Functions?
Ans. the loss function is to capture the difference between the actual and predicted values for a single record whereas cost functions aggregate the difference for the entire training dataset.
The Most commonly used loss functions are Mean-squared error and Hinge loss.
3. What is the difference between Python Arrays and lists?
Ans. Arrays in python can only contain elements of same data types i.e., data type of array should be homogeneous. It is a thin wrapper around C language arrays and consumes far less memory than lists.
Lists in python can contain elements of different data types i.e., data type of lists can be heterogeneous. It has the disadvantage of consuming large memory.
4. What is root cause analysis? What is a causation vs. a correlation?
Ans. Root cause analysis: a method of problem-solving used for identifying the root cause(s) of a problem [5]
Correlation measures the relationship between two variables, range from -1 to 1. Causation is when a first event appears to have caused a second event. Causation essentially looks at direct relationships while correlation can look at both direct and indirect relationships.
———————————————————-
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Company name: Musigma
Role: Data Scientist
Topic : decision tree, loss function, cost function, root cause analysis, array vs list
1. How does a Decision Tree handle continuous(numerical) features?
Ans.
Decision Trees handle continuous features by converting these continuous features to a threshold-based boolean feature.
To decide The threshold value, we use the concept of Information Gain, choosing that threshold that maximizes the information gain.
2. What are Loss Function and Cost Functions?
Ans. the loss function is to capture the difference between the actual and predicted values for a single record whereas cost functions aggregate the difference for the entire training dataset.
The Most commonly used loss functions are Mean-squared error and Hinge loss.
3. What is the difference between Python Arrays and lists?
Ans. Arrays in python can only contain elements of same data types i.e., data type of array should be homogeneous. It is a thin wrapper around C language arrays and consumes far less memory than lists.
Lists in python can contain elements of different data types i.e., data type of lists can be heterogeneous. It has the disadvantage of consuming large memory.
4. What is root cause analysis? What is a causation vs. a correlation?
Ans. Root cause analysis: a method of problem-solving used for identifying the root cause(s) of a problem [5]
Correlation measures the relationship between two variables, range from -1 to 1. Causation is when a first event appears to have caused a second event. Causation essentially looks at direct relationships while correlation can look at both direct and indirect relationships.
———————————————————-
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🔥1
Date: 07-12-2022
Company name: Wipro
Role: Data scientist
Topic: knn , imputation, n gram, data mining
1. What is the difference between data mining and data profiling.
Data mining Process: It generally involves analyzing data to find relations that were not previously discovered. In this case, the emphasis is on finding unusual records, detecting dependencies, and analyzing clusters.
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. Explain the KNN imputation method.
A KNN (K-nearest neighbor) model is usually considered one of the most common techniques for imputation. It allows a point in multidimensional space to be matched with its closest k neighbors. By using the distance function, two attribute values are compared. Using this approach, the closest attribute values to the missing values are used to impute these missing values.
3. Explain Hierarchical clustering.
This algorithm group objects into clusters based on similarities, and it is also called hierarchical cluster analysis. When hierarchical clustering is performed, we obtain a set of clusters that differ from each other.
This clustering technique can be divided into two types:
Agglomerative Clustering (which uses bottom-up strategy to decompose clusters)
Divisive Clustering (which uses a top-down strategy to decompose clusters)
4. Explain N-gram
N-gram, known as the probabilistic language model, is defined as a connected sequence of n items in a given text or speech. It is basically composed of adjacent words or letters of length n that were present in the source text. In simple words, it is a way to predict the next item in a sequence, as in (n-1).
———————————————————-
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Company name: Wipro
Role: Data scientist
Topic: knn , imputation, n gram, data mining
1. What is the difference between data mining and data profiling.
Data mining Process: It generally involves analyzing data to find relations that were not previously discovered. In this case, the emphasis is on finding unusual records, detecting dependencies, and analyzing clusters.
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. Explain the KNN imputation method.
A KNN (K-nearest neighbor) model is usually considered one of the most common techniques for imputation. It allows a point in multidimensional space to be matched with its closest k neighbors. By using the distance function, two attribute values are compared. Using this approach, the closest attribute values to the missing values are used to impute these missing values.
3. Explain Hierarchical clustering.
This algorithm group objects into clusters based on similarities, and it is also called hierarchical cluster analysis. When hierarchical clustering is performed, we obtain a set of clusters that differ from each other.
This clustering technique can be divided into two types:
Agglomerative Clustering (which uses bottom-up strategy to decompose clusters)
Divisive Clustering (which uses a top-down strategy to decompose clusters)
4. Explain N-gram
N-gram, known as the probabilistic language model, is defined as a connected sequence of n items in a given text or speech. It is basically composed of adjacent words or letters of length n that were present in the source text. In simple words, it is a way to predict the next item in a sequence, as in (n-1).
———————————————————-
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🔥1
Date: 08-12-2022
Company name: Oracle
Role: Data Analyst
Topic: outlier, dax, filter, recursive stored procedure
1. What are the ways to detect outliers?
Outliers are detected using two methods:
Box Plot Method: According to this method, the value is considered an outlier if it exceeds or falls below 1.5*IQR (interquartile range), that is, if it lies above the top quartile (Q3) or below the bottom quartile (Q1).
Standard Deviation Method: According to this method, an outlier is defined as a value that is greater or lower than the mean ± (3*standard deviation).
2. What is a Recursive Stored Procedure?
A stored procedure that calls itself until a boundary condition is reached, is called a recursive stored procedure. This recursive function helps the programmers to deploy the same set of code several times as and when required.
3. What is the shortcut to add a filter to a table in EXCEL?
The filter mechanism is used when you want to display only specific data from the entire dataset. By doing so, there is no change being made to the data. The shortcut to add a filter to a table is Ctrl+Shift+L.
4. What is DAX in Power BI?
DAX stands for Data Analysis Expressions. It's a collection of functions, operators, and constants used in formulas to calculate and return values. In other words, it helps you create new info from data you already have.
———————————————————-
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Company name: Oracle
Role: Data Analyst
Topic: outlier, dax, filter, recursive stored procedure
1. What are the ways to detect outliers?
Outliers are detected using two methods:
Box Plot Method: According to this method, the value is considered an outlier if it exceeds or falls below 1.5*IQR (interquartile range), that is, if it lies above the top quartile (Q3) or below the bottom quartile (Q1).
Standard Deviation Method: According to this method, an outlier is defined as a value that is greater or lower than the mean ± (3*standard deviation).
2. What is a Recursive Stored Procedure?
A stored procedure that calls itself until a boundary condition is reached, is called a recursive stored procedure. This recursive function helps the programmers to deploy the same set of code several times as and when required.
3. What is the shortcut to add a filter to a table in EXCEL?
The filter mechanism is used when you want to display only specific data from the entire dataset. By doing so, there is no change being made to the data. The shortcut to add a filter to a table is Ctrl+Shift+L.
4. What is DAX in Power BI?
DAX stands for Data Analysis Expressions. It's a collection of functions, operators, and constants used in formulas to calculate and return values. In other words, it helps you create new info from data you already have.
———————————————————-
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🔥1
Date: 09-12-2022
Company name: Snapdeal
role: SQL Developer
Topic: constraint, clustered index, subsets of SQL
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. Find the Constraint information from the table?
There are so many times where user needs to find out the specific constraint information of the table. The following queries are useful, SELECT * From User_Constraints; SELECT * FROM User_Cons_Columns;
3. What is the difference between clustered and non-clustered indexes?
Clustered indexes can be read rapidly rather than non-clustered indexes.
Clustered indexes store data physically in the table or view whereas, non-clustered indexes do not store data in the table as it has separate structure from the data row.
4. What are the subsets of SQL?
DDL (Data Definition Language): Used to define the data structure it consists of the commands like CREATE, ALTER, DROP, etc.
DML (Data Manipulation Language): Used to manipulate already existing data in the database, commands like SELECT, UPDATE, INSERT
DCL (Data Control Language): Used to control access to data in the database, commands like GRANT, REVOKE.
———————————————————-
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Company name: Snapdeal
role: SQL Developer
Topic: constraint, clustered index, subsets of SQL
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. Find the Constraint information from the table?
There are so many times where user needs to find out the specific constraint information of the table. The following queries are useful, SELECT * From User_Constraints; SELECT * FROM User_Cons_Columns;
3. What is the difference between clustered and non-clustered indexes?
Clustered indexes can be read rapidly rather than non-clustered indexes.
Clustered indexes store data physically in the table or view whereas, non-clustered indexes do not store data in the table as it has separate structure from the data row.
4. What are the subsets of SQL?
DDL (Data Definition Language): Used to define the data structure it consists of the commands like CREATE, ALTER, DROP, etc.
DML (Data Manipulation Language): Used to manipulate already existing data in the database, commands like SELECT, UPDATE, INSERT
DCL (Data Control Language): Used to control access to data in the database, commands like GRANT, REVOKE.
———————————————————-
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🔥1
Date: 10-12-2022
Company Name: Accenture
Role: Data Scientist
Topic: Silhouette, trend seasonality, bag of words, bagging boosting , SQL
1. What do you understand by the term silhouette coefficient?
The silhouette coefficient is a measure of how well clustered together a data point is with respect to the other points in its cluster. It is a measure of how similar a point is to the points in its own cluster, and how dissimilar it is to the points in other clusters. The silhouette coefficient ranges from -1 to 1, with 1 being the best possible score and -1 being the worst possible score.
2. What is the difference between trend and seasonality in time series?
Trends and seasonality are two characteristics of time series metrics that break many models. Trends are continuous increases or decreases in a metric’s value. Seasonality, on the other hand, reflects periodic (cyclical) patterns that occur in a system, usually rising above a baseline and then decreasing again.
3. What is Bag of Words in NLP?
Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences irrespective of its grammatical structure or word order.
4. What is the difference between bagging and boosting?
Bagging is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average. Boosting is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm
5.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.
6. 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.
Company Name: Accenture
Role: Data Scientist
Topic: Silhouette, trend seasonality, bag of words, bagging boosting , SQL
1. What do you understand by the term silhouette coefficient?
The silhouette coefficient is a measure of how well clustered together a data point is with respect to the other points in its cluster. It is a measure of how similar a point is to the points in its own cluster, and how dissimilar it is to the points in other clusters. The silhouette coefficient ranges from -1 to 1, with 1 being the best possible score and -1 being the worst possible score.
2. What is the difference between trend and seasonality in time series?
Trends and seasonality are two characteristics of time series metrics that break many models. Trends are continuous increases or decreases in a metric’s value. Seasonality, on the other hand, reflects periodic (cyclical) patterns that occur in a system, usually rising above a baseline and then decreasing again.
3. What is Bag of Words in NLP?
Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences irrespective of its grammatical structure or word order.
4. What is the difference between bagging and boosting?
Bagging is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average. Boosting is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm
5.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.
6. 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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Date: 12-12-2022
Company name: Tredence
Role: ML engineer
Topic: margin in svm, r2 and adjusted r2, curse of dimensions
1. What is the primary difference between R square and adjusted R square?
In linear regression, you use both these values for model validation. However, there is a clear distinction between the two. R square accounts for the variation of all independent variables on the dependent variable. In other words, it considers each independent variable for explaining the variation. In the case of Adjusted R square, it accounts for the significant variables alone for indicating the percentage of variation in the model. By significant, we refer to the P values less than 0.05.
2. What is the curse of dimensionality?
Curse of Dimensionality refers to a set of problems that arise when working with high-dimensional data. The dimension of a dataset corresponds to the number of attributes/features that exist in a dataset. A dataset with a large number of attributes, generally of the order of a hundred or more, is referred to as high dimensional data. Some of the difficulties that come with high dimensional data manifest during analyzing or visualizing the data to identify patterns, and some manifest while training machine learning models. The difficulties related to training machine learning models due to high dimensional data are referred to as the ‘Curse of Dimensionality’.
3. What are some Stopping Criteria for k-Means Clustering?
a. Convergence. No further changes, points stay in the same cluster.
b. The maximum number of iterations. When the maximum number of iterations has been reached, the algorithm will be stopped. This is done to limit the runtime of the algorithm.
c. Variance did not improve by at least x * initial variance
4. What are hard margin and soft Margin SVMs?
Hard margin SVMs work only if the data is linearly separable and these types of SVMs are quite sensitive to the outliers. But our main objective is to find a good balance between keeping the margins as large as possible and limiting the margin violation i.e. instances that end up in the middle of margin or even on the wrong side, and this method is called soft margin SVM.
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Company name: Tredence
Role: ML engineer
Topic: margin in svm, r2 and adjusted r2, curse of dimensions
1. What is the primary difference between R square and adjusted R square?
In linear regression, you use both these values for model validation. However, there is a clear distinction between the two. R square accounts for the variation of all independent variables on the dependent variable. In other words, it considers each independent variable for explaining the variation. In the case of Adjusted R square, it accounts for the significant variables alone for indicating the percentage of variation in the model. By significant, we refer to the P values less than 0.05.
2. What is the curse of dimensionality?
Curse of Dimensionality refers to a set of problems that arise when working with high-dimensional data. The dimension of a dataset corresponds to the number of attributes/features that exist in a dataset. A dataset with a large number of attributes, generally of the order of a hundred or more, is referred to as high dimensional data. Some of the difficulties that come with high dimensional data manifest during analyzing or visualizing the data to identify patterns, and some manifest while training machine learning models. The difficulties related to training machine learning models due to high dimensional data are referred to as the ‘Curse of Dimensionality’.
3. What are some Stopping Criteria for k-Means Clustering?
a. Convergence. No further changes, points stay in the same cluster.
b. The maximum number of iterations. When the maximum number of iterations has been reached, the algorithm will be stopped. This is done to limit the runtime of the algorithm.
c. Variance did not improve by at least x * initial variance
4. What are hard margin and soft Margin SVMs?
Hard margin SVMs work only if the data is linearly separable and these types of SVMs are quite sensitive to the outliers. But our main objective is to find a good balance between keeping the margins as large as possible and limiting the margin violation i.e. instances that end up in the middle of margin or even on the wrong side, and this method is called soft margin SVM.
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Date: 13-12-2022
Company name: Brookfield
role: SQL Developer
Topic: stored procedure, normalisation, data abstraction, auto increment
1. What is meant by normalization and denormalization?
Normalization is a process of reducing redundancy by organizing the data into multiple tables. Normalization leads to better usage of disk spaces and makes it easier to maintain the integrity of the database.
Denormalization is the reverse process of normalization as it combines the tables which have been normalized into a single table so that data retrieval becomes faster. JOIN operation allows us to create a denormalized form of the data by reversing the normalization
2. What are the three levels of data abstraction?
Following are three levels of data abstraction:
Physical level: It is the lowest level of abstraction. It describes how data are stored.
Logical level: It is the next higher level of abstraction. It describes what data are stored in the database and what the relationship among those data is.
View level: It is the highest level of data abstraction. It describes only part of the entire database.
3. What is auto increment?
The auto increment in SQL is a feature that is applied to a field so that it can automatically generate and provide a unique value to every record that you enter into an SQL table. This field is often used as the PRIMARY KEY column, where you need to provide a unique value for every record you add.
4. Advantages and Disadvantages of Stored Procedure?
Stored procedure can be used as a modular programming – means create once, store and call for several times whenever required. This supports faster execution instead of executing multiple queries. This reduces network traffic and provides better security to the data.
Disadvantage is that it can be executed only in the Database and utilizes more memory in the database server.
IF YOU ARE NEW THEN DON'T FORGET TO JOIN OUR TELEGRAM CHANNEL FOR SUCH DATA SCIENCE & ANALYTICS DOMAIN UPDATES REGULARLY.
Company name: Brookfield
role: SQL Developer
Topic: stored procedure, normalisation, data abstraction, auto increment
1. What is meant by normalization and denormalization?
Normalization is a process of reducing redundancy by organizing the data into multiple tables. Normalization leads to better usage of disk spaces and makes it easier to maintain the integrity of the database.
Denormalization is the reverse process of normalization as it combines the tables which have been normalized into a single table so that data retrieval becomes faster. JOIN operation allows us to create a denormalized form of the data by reversing the normalization
2. What are the three levels of data abstraction?
Following are three levels of data abstraction:
Physical level: It is the lowest level of abstraction. It describes how data are stored.
Logical level: It is the next higher level of abstraction. It describes what data are stored in the database and what the relationship among those data is.
View level: It is the highest level of data abstraction. It describes only part of the entire database.
3. What is auto increment?
The auto increment in SQL is a feature that is applied to a field so that it can automatically generate and provide a unique value to every record that you enter into an SQL table. This field is often used as the PRIMARY KEY column, where you need to provide a unique value for every record you add.
4. Advantages and Disadvantages of Stored Procedure?
Stored procedure can be used as a modular programming – means create once, store and call for several times whenever required. This supports faster execution instead of executing multiple queries. This reduces network traffic and provides better security to the data.
Disadvantage is that it can be executed only in the Database and utilizes more memory in the database server.
Date: 14-12-2022
Company name: Biocon
Role: ML Engineer
Topic: nodes, adaboost, ensemble, hierarchical
1. List down the different types of nodes in Decision Trees.
The Decision Tree consists of the following different types of nodes:
1. Root node: It is the top-most node of the Tree from where the Tree starts.
2. Decision nodes: One or more Decision nodes that result in the splitting of data into multiple data segments and our main goal is to have the children nodes with maximum homogeneity or purity.
3. Leaf nodes: These nodes represent the data section having the highest homogeneity.
2. What is the Alpha Term in AdaBoost? How does it work?
In AdaBoost, multiple weak learners are trained to get the strong learner. As a result, hence calculating the error term of every weak learner is essential to know which weak learner is performing best and which is not.
The term Alpha is a parameter that indicates the weight that should be given to a particular weak learner algorithm. If the value of the term Alpha for a particular algorithm is high, that indicates that the model is performing best and the error rate for the same is low.
3. Since Ensemble Learning provides better output most of the time, why do you not use it all the time?
Although it provides a better outcome many times, it is not true that it will always perform better. There are several ensemble methods, each with its own advantages/disadvantages, and choosing one to use depends on the problem at hand. If there are models with high variance, then it will benefit from bagging. If the model is biased, it is better to use boosting. If the work is in probabilistic setting, the ensemble methods may not work because it is known that boosting delivers poor probability estimates.
4. What are the various types of Hierarchical Clustering?
The two different types of Hierarchical Clustering technique are as follows:
Agglomerative: It is a bottom-up approach, in which the algorithm starts with taking all data points as single clusters and merging them until one cluster is left.
Divisive: It is just the opposite of the agglomerative algorithm as it is a top-down approach.
IF YOU ARE NEW THEN DON'T FORGET TO JOIN OUR TELEGRAM CHANNEL FOR SUCH DATA SCIENCE & ANALYTICS DOMAIN UPDATES REGULARLY.
Company name: Biocon
Role: ML Engineer
Topic: nodes, adaboost, ensemble, hierarchical
1. List down the different types of nodes in Decision Trees.
The Decision Tree consists of the following different types of nodes:
1. Root node: It is the top-most node of the Tree from where the Tree starts.
2. Decision nodes: One or more Decision nodes that result in the splitting of data into multiple data segments and our main goal is to have the children nodes with maximum homogeneity or purity.
3. Leaf nodes: These nodes represent the data section having the highest homogeneity.
2. What is the Alpha Term in AdaBoost? How does it work?
In AdaBoost, multiple weak learners are trained to get the strong learner. As a result, hence calculating the error term of every weak learner is essential to know which weak learner is performing best and which is not.
The term Alpha is a parameter that indicates the weight that should be given to a particular weak learner algorithm. If the value of the term Alpha for a particular algorithm is high, that indicates that the model is performing best and the error rate for the same is low.
3. Since Ensemble Learning provides better output most of the time, why do you not use it all the time?
Although it provides a better outcome many times, it is not true that it will always perform better. There are several ensemble methods, each with its own advantages/disadvantages, and choosing one to use depends on the problem at hand. If there are models with high variance, then it will benefit from bagging. If the model is biased, it is better to use boosting. If the work is in probabilistic setting, the ensemble methods may not work because it is known that boosting delivers poor probability estimates.
4. What are the various types of Hierarchical Clustering?
The two different types of Hierarchical Clustering technique are as follows:
Agglomerative: It is a bottom-up approach, in which the algorithm starts with taking all data points as single clusters and merging them until one cluster is left.
Divisive: It is just the opposite of the agglomerative algorithm as it is a top-down approach.
Date: 15-12-2022
Company name: Cloudera
Role: Data Analyst
Topic: blending & joining, ensemble learning, formatting in excel, constraint in SQL
1. What is the Difference Between Joining and Blending in Tableau?
Combining the data from two or more different sources is data blending, such as Oracle, Excel, and SQL Server. In data blending, each data source contains its own set of dimensions and measures.
Combining the data between two or more tables or sheets within the same data source is data joining. All the combined tables or sheets contain a common set of dimensions and measures.
2. What are Ensemble Methods?
Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model. Random Forest is a type of ensemble method. The number of component classifier in an ensemble has a great impact on the accuracy of the prediction, although there is a law of diminishing results in ensemble construction.
3. What are constraints in SQL?
Constraints are the rules that we can apply on the type of data in a table. That is, we can specify the limit on the type of data that can be stored in a particular column in a table using constraints. NOT NULL, UNIQUE, DEFAULT, PRIMARY KEY, FOREIGN KEY, CHECK are the different constraints in SQL.
4. How do you apply a single format to all the sheets present in a workbook?
To apply the same format to all the sheets of a workbook, follow the given steps:
Right-click on any sheet present in that workbook
Then, click on the Select All Sheets option
Format any of the sheets and you will see that the format has been applied to all the other sheets as well
IF YOU ARE NEW THEN DON'T FORGET TO JOIN OUR TELEGRAM CHANNEL FOR SUCH DATA SCIENCE & ANALYTICS DOMAIN UPDATES REGULARLY.
Company name: Cloudera
Role: Data Analyst
Topic: blending & joining, ensemble learning, formatting in excel, constraint in SQL
1. What is the Difference Between Joining and Blending in Tableau?
Combining the data from two or more different sources is data blending, such as Oracle, Excel, and SQL Server. In data blending, each data source contains its own set of dimensions and measures.
Combining the data between two or more tables or sheets within the same data source is data joining. All the combined tables or sheets contain a common set of dimensions and measures.
2. What are Ensemble Methods?
Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model. Random Forest is a type of ensemble method. The number of component classifier in an ensemble has a great impact on the accuracy of the prediction, although there is a law of diminishing results in ensemble construction.
3. What are constraints in SQL?
Constraints are the rules that we can apply on the type of data in a table. That is, we can specify the limit on the type of data that can be stored in a particular column in a table using constraints. NOT NULL, UNIQUE, DEFAULT, PRIMARY KEY, FOREIGN KEY, CHECK are the different constraints in SQL.
4. How do you apply a single format to all the sheets present in a workbook?
To apply the same format to all the sheets of a workbook, follow the given steps:
Right-click on any sheet present in that workbook
Then, click on the Select All Sheets option
Format any of the sheets and you will see that the format has been applied to all the other sheets as well
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