Date: 18-11-2022
Company name: LTI
Role: ML Engineer
Topic: cart, cross validation, knn, pandas data structures
1. Mention The Different Types Of Data Structures In pandas?
There are two data structures supported by pandas library, Series and DataFrames. Both of the data structures are built on top of Numpy. Series is a one-dimensional data structure in pandas and DataFrame is the two-dimensional data structure in pandas. There is one more axis label known as Panel which is a three-dimensional data structure and it includes items, major_axis, and minor_axis.
2. Why is KNN a non-parametric Algorithm?
The term “non-parametric” refers to not making any assumptions on the underlying data distribution. These methods do not have any fixed numbers of parameters in the model.
Similarly in KNN, the model parameters grow with the training data by considering each training case as a parameter of the model. So, KNN is a non-parametric algorithm.
3. Explain the CART Algorithm for Decision Trees.
CART is a variation of the decision tree algorithm. It can handle both classification and regression tasks.The CART stands for Classification and Regression Trees is a greedy algorithm that greedily searches for an optimum split at the top level, then repeats the same process at each of the subsequent levels. Moreover, it does verify whether the split will lead to the lowest impurity or not as well as the solution provided by the greedy algorithm is not guaranteed to be optimal, it often produces a solution that’s reasonably good since finding the optimal Tree is an NP-Complete problem that requires exponential time complexity.
4. Explain leave-p-out cross validation.
When using this exhaustive method, we take p number of points out from the total number of data points in the dataset(say n). While training the model we train it on these (n – p) data points and test the model on p data points. We repeat this process for all the possible combinations of p from the original dataset. Then to get the final accuracy, we average the accuracies from all these iterations.
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Company name: LTI
Role: ML Engineer
Topic: cart, cross validation, knn, pandas data structures
1. Mention The Different Types Of Data Structures In pandas?
There are two data structures supported by pandas library, Series and DataFrames. Both of the data structures are built on top of Numpy. Series is a one-dimensional data structure in pandas and DataFrame is the two-dimensional data structure in pandas. There is one more axis label known as Panel which is a three-dimensional data structure and it includes items, major_axis, and minor_axis.
2. Why is KNN a non-parametric Algorithm?
The term “non-parametric” refers to not making any assumptions on the underlying data distribution. These methods do not have any fixed numbers of parameters in the model.
Similarly in KNN, the model parameters grow with the training data by considering each training case as a parameter of the model. So, KNN is a non-parametric algorithm.
3. Explain the CART Algorithm for Decision Trees.
CART is a variation of the decision tree algorithm. It can handle both classification and regression tasks.The CART stands for Classification and Regression Trees is a greedy algorithm that greedily searches for an optimum split at the top level, then repeats the same process at each of the subsequent levels. Moreover, it does verify whether the split will lead to the lowest impurity or not as well as the solution provided by the greedy algorithm is not guaranteed to be optimal, it often produces a solution that’s reasonably good since finding the optimal Tree is an NP-Complete problem that requires exponential time complexity.
4. Explain leave-p-out cross validation.
When using this exhaustive method, we take p number of points out from the total number of data points in the dataset(say n). While training the model we train it on these (n – p) data points and test the model on p data points. We repeat this process for all the possible combinations of p from the original dataset. Then to get the final accuracy, we average the accuracies from all these iterations.
———————————————————-
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Date: 19-11-2022
Company name: Infosys
Role: Data Scientist
Topic: LSTM, list vs numpy, CTE, kmeans
1. Can you explain how the memory cell in an LSTM is implemented computationally?
The memory cell in an LSTM is implemented as a forget gate, an input gate, and an output gate. The forget gate controls how much information from the previous cell state is forgotten. The input gate controls how much new information from the current input is allowed into the cell state. The output gate controls how much information from the cell state is allowed to pass out to the next cell state.
2. What is CTE in SQL?
A CTE (Common Table Expression) is a one-time result set that only exists for the duration of the query. It allows us to refer to data within a single SELECT, INSERT, UPDATE, DELETE, CREATE VIEW, or MERGE statement's execution scope. It is temporary because its result cannot be stored anywhere and will be lost as soon as a query's execution is completed.
3. List the advantages NumPy Arrays have over Python lists?
Python’s lists, even though hugely efficient containers capable of a number of functions, have several limitations when compared to NumPy arrays. It is not possible to perform vectorised operations which includes element-wise addition and multiplication. They also require that Python store the type information of every element since they support objects of different types. This means a type dispatching code must be executed each time an operation on an element is done.
4. Explain some cases where k-Means clustering fails to give good results.
k-means has trouble clustering data where clusters are of various sizes and densities.
Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.
If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.
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Company name: Infosys
Role: Data Scientist
Topic: LSTM, list vs numpy, CTE, kmeans
1. Can you explain how the memory cell in an LSTM is implemented computationally?
The memory cell in an LSTM is implemented as a forget gate, an input gate, and an output gate. The forget gate controls how much information from the previous cell state is forgotten. The input gate controls how much new information from the current input is allowed into the cell state. The output gate controls how much information from the cell state is allowed to pass out to the next cell state.
2. What is CTE in SQL?
A CTE (Common Table Expression) is a one-time result set that only exists for the duration of the query. It allows us to refer to data within a single SELECT, INSERT, UPDATE, DELETE, CREATE VIEW, or MERGE statement's execution scope. It is temporary because its result cannot be stored anywhere and will be lost as soon as a query's execution is completed.
3. List the advantages NumPy Arrays have over Python lists?
Python’s lists, even though hugely efficient containers capable of a number of functions, have several limitations when compared to NumPy arrays. It is not possible to perform vectorised operations which includes element-wise addition and multiplication. They also require that Python store the type information of every element since they support objects of different types. This means a type dispatching code must be executed each time an operation on an element is done.
4. Explain some cases where k-Means clustering fails to give good results.
k-means has trouble clustering data where clusters are of various sizes and densities.
Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.
If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.
———————————————————-
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Date: 20-11-2022
Company name: Mindtree
Role: Database developer
Topic: Checkpoint, relation, schema
1. What is a checkpoint in DBMS?
The Checkpoint is a type of mechanism where all the previous logs are removed from the system and permanently stored in the storage disk. There are two ways which can help the DBMS in recovering and maintaining the ACID properties, and they are- maintaining the log of each transaction and maintaining shadow pages. So, when it comes to log based recovery systems, checkpoints come into existence. Checkpoints are those points to which the database engine can recover after a crash as a specified minimal point from which the transaction log record can be used to recover all the committed data up to the point of the crash.
2. What do you mean by transparent DBMS?
The transparent DBMS is a type of DBMS which keeps its physical structure hidden from users. Physical structure or physical storage structure implies to the memory manager of the DBMS, and it describes how the data is stored on disk.
3. Define a Relation Schema and a Relation.
A Relation Schema is specified as a set of attributes. It is also known as table schema. It defines what the name of the table is. Relation schema is known as the blueprint with the help of which we can explain how the data is organized into tables. This blueprint contains no data.
A relation is specified as a set of tuples. A relation is the set of related attributes with identifying key attributes
4. Explain the functionality of DML Compiler.
The DML Compiler translates DML statements in a query language that the query evaluation engine can understand. A DML Compiler is required because the DML is the family of syntax elements which is very similar to the other programming language which requires compilation. So, it is essential to compile the code in the language which the query evaluation engine can understand and then work on those queries with proper output.
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Company name: Mindtree
Role: Database developer
Topic: Checkpoint, relation, schema
1. What is a checkpoint in DBMS?
The Checkpoint is a type of mechanism where all the previous logs are removed from the system and permanently stored in the storage disk. There are two ways which can help the DBMS in recovering and maintaining the ACID properties, and they are- maintaining the log of each transaction and maintaining shadow pages. So, when it comes to log based recovery systems, checkpoints come into existence. Checkpoints are those points to which the database engine can recover after a crash as a specified minimal point from which the transaction log record can be used to recover all the committed data up to the point of the crash.
2. What do you mean by transparent DBMS?
The transparent DBMS is a type of DBMS which keeps its physical structure hidden from users. Physical structure or physical storage structure implies to the memory manager of the DBMS, and it describes how the data is stored on disk.
3. Define a Relation Schema and a Relation.
A Relation Schema is specified as a set of attributes. It is also known as table schema. It defines what the name of the table is. Relation schema is known as the blueprint with the help of which we can explain how the data is organized into tables. This blueprint contains no data.
A relation is specified as a set of tuples. A relation is the set of related attributes with identifying key attributes
4. Explain the functionality of DML Compiler.
The DML Compiler translates DML statements in a query language that the query evaluation engine can understand. A DML Compiler is required because the DML is the family of syntax elements which is very similar to the other programming language which requires compilation. So, it is essential to compile the code in the language which the query evaluation engine can understand and then work on those queries with proper output.
———————————————————-
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Date: 21-11-2022
Company name: Cloudera
Role: Tableau developer
Topic: aggregation, filters, data sources, fact dimensions
1. Give an overview of the fact and dimensions of the table?
Facts are numeric measures of data. They are stored in fact tables. Fact tables store that type of data that will be analyzed by dimension tables. Fact tables have foreign keys associating with dimension tables.
Dimensions are descriptive attributes of data. Those will be stored in the dimensions table. For example, customer’s information like name, number, and email will be stored in the dimension table.
2. Explain the limitation of context filters in Tableau?
Whenever we set a context filter, Tableau generates a temp table that needs to refresh each and every time, whenever the view is triggered. So, if the context filter is changed in the database, it needs to recompute the temp table, so the performance will be decreased.
3. What is the difference between published data and embedded data sources?
The published data source contains connection information that is independent of workbooks and can be used by multiple workbooks.
The embedded data source contains connection information but it is associated with the workbooks.
4. Explain the disaggregation and aggregation of data in Tableau?
Aggregation → The process of summarizing the data and viewing a single numeric value is called aggregation. Example – sum/avg of salary for each employee
Disaggregation →The process of viewing each transaction for analyzing all the measures both dependently and independently. Example – individual salary transactions for each employee.
Company name: Cloudera
Role: Tableau developer
Topic: aggregation, filters, data sources, fact dimensions
1. Give an overview of the fact and dimensions of the table?
Facts are numeric measures of data. They are stored in fact tables. Fact tables store that type of data that will be analyzed by dimension tables. Fact tables have foreign keys associating with dimension tables.
Dimensions are descriptive attributes of data. Those will be stored in the dimensions table. For example, customer’s information like name, number, and email will be stored in the dimension table.
2. Explain the limitation of context filters in Tableau?
Whenever we set a context filter, Tableau generates a temp table that needs to refresh each and every time, whenever the view is triggered. So, if the context filter is changed in the database, it needs to recompute the temp table, so the performance will be decreased.
3. What is the difference between published data and embedded data sources?
The published data source contains connection information that is independent of workbooks and can be used by multiple workbooks.
The embedded data source contains connection information but it is associated with the workbooks.
4. Explain the disaggregation and aggregation of data in Tableau?
Aggregation → The process of summarizing the data and viewing a single numeric value is called aggregation. Example – sum/avg of salary for each employee
Disaggregation →The process of viewing each transaction for analyzing all the measures both dependently and independently. Example – individual salary transactions for each employee.
Date: 22-11-22
Company name: Tredence
Role: Data Analyst
Topic: map, operation, relationship, 13having and where clause
1. What is the difference between HAVING and WHERE clauses?
WHERE Clause is used to filter the records from the table based on the specified condition. HAVING Clause is used to filter records from the groups based on the specified condition. WHERE Clause can be used without GROUP BY Clause HAVING Clause cannot be used without GROUP BY Clause. WHERE Clause implements in row operations. HAVING Clause implements in column operation. WHERE Clause cannot contain aggregate function. HAVING Clause can contain aggregate function
2. 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.
3. Mention the order of operations used in Excel while evaluating formulas.
The order of operations in Excel is referred to as PEMDAS. Shown below is the order of precedence while performing an Excel operation.
Parentheses
Exponentiation
Division/Multiplication
Addition
Subtraction
4.What is map function in Python?
Ans: map function executes the function given as the first argument on all the elements of the iterable given as the second argument. If the function given takes in more than 1 arguments, then many iterables are given.
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Company name: Tredence
Role: Data Analyst
Topic: map, operation, relationship, 13having and where clause
1. What is the difference between HAVING and WHERE clauses?
WHERE Clause is used to filter the records from the table based on the specified condition. HAVING Clause is used to filter records from the groups based on the specified condition. WHERE Clause can be used without GROUP BY Clause HAVING Clause cannot be used without GROUP BY Clause. WHERE Clause implements in row operations. HAVING Clause implements in column operation. WHERE Clause cannot contain aggregate function. HAVING Clause can contain aggregate function
2. 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.
3. Mention the order of operations used in Excel while evaluating formulas.
The order of operations in Excel is referred to as PEMDAS. Shown below is the order of precedence while performing an Excel operation.
Parentheses
Exponentiation
Division/Multiplication
Addition
Subtraction
4.What is map function in Python?
Ans: map function executes the function given as the first argument on all the elements of the iterable given as the second argument. If the function given takes in more than 1 arguments, then many iterables are given.
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Date: 23-11-2022
Company name: Snapdeal
Role: ML Engineer
Topic: svm, naive Bayes, kmeans, dbscan
1. Which algorithm is better in the case of outliers present in the dataset i.e., Logistic Regression or SVM?
SVM (Support Vector Machines) handles the outliers in a better manner than the Logistic Regression.
Logistic Regression: Logistic Regression will identify a linear boundary if it exists to accommodate the outliers. To accommodate the outliers, it will shift the linear boundary.
SVM: SVM is insensitive to individual samples. So, to accommodate an outlier there will not be a major shift in the linear boundary. SVM comes with inbuilt complexity controls, which take care of overfitting, which is not true in the case of Logistic Regression.
2. List out the Input parameters given to the DBSCAN Algorithm.
The DBSCAN algorithm uses the following two user-defined as the input parameters for clustering:
Epsilon (eps): It is defined as the maximum distance between two points which are considered as neighbouring points as well as can be viewed as the radius around each point.
Minimum Points (min_samples or min_pts): This defines the minimum number of neighbouring points that a given point needs to be considered a core data point including the point itself. In general terms, it can be considered as the minimum number of observations that should be around that point within that radius.
3. Impact of missing values on naive Bayes?
Naive Bayes is one of the algorithms that can handle the missing data at its end. Only the reason is that in this algo, all the attributes are handled separately during both model construction and prediction time If data points are missing for a certain feature, then it can be ignored when a probability is calculated for a separate class, which makes it handle the missing data at model building phase itself.
4. What is Mini Batch K-Means algorithm?
Mini Batch K-means algorithm‘s main idea is to use small random batches of data of a fixed size, so they can be stored in memory. Each iteration a new random sample from the dataset is obtained and used to update the clusters and this is repeated until convergence. Each mini batch updates the clusters using a convex combination of the values of the prototypes and the data, applying a learning rate that decreases with the number of iterations.
———————————————————-
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Company name: Snapdeal
Role: ML Engineer
Topic: svm, naive Bayes, kmeans, dbscan
1. Which algorithm is better in the case of outliers present in the dataset i.e., Logistic Regression or SVM?
SVM (Support Vector Machines) handles the outliers in a better manner than the Logistic Regression.
Logistic Regression: Logistic Regression will identify a linear boundary if it exists to accommodate the outliers. To accommodate the outliers, it will shift the linear boundary.
SVM: SVM is insensitive to individual samples. So, to accommodate an outlier there will not be a major shift in the linear boundary. SVM comes with inbuilt complexity controls, which take care of overfitting, which is not true in the case of Logistic Regression.
2. List out the Input parameters given to the DBSCAN Algorithm.
The DBSCAN algorithm uses the following two user-defined as the input parameters for clustering:
Epsilon (eps): It is defined as the maximum distance between two points which are considered as neighbouring points as well as can be viewed as the radius around each point.
Minimum Points (min_samples or min_pts): This defines the minimum number of neighbouring points that a given point needs to be considered a core data point including the point itself. In general terms, it can be considered as the minimum number of observations that should be around that point within that radius.
3. Impact of missing values on naive Bayes?
Naive Bayes is one of the algorithms that can handle the missing data at its end. Only the reason is that in this algo, all the attributes are handled separately during both model construction and prediction time If data points are missing for a certain feature, then it can be ignored when a probability is calculated for a separate class, which makes it handle the missing data at model building phase itself.
4. What is Mini Batch K-Means algorithm?
Mini Batch K-means algorithm‘s main idea is to use small random batches of data of a fixed size, so they can be stored in memory. Each iteration a new random sample from the dataset is obtained and used to update the clusters and this is repeated until convergence. Each mini batch updates the clusters using a convex combination of the values of the prototypes and the data, applying a learning rate that decreases with the number of iterations.
———————————————————-
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Date: 24-11-2022
Company name: Accenture
Role: SQL Developer
Topic: Grant and revoke, constraint, cte, one to many
1. Explain Grant and Revoke in SQL.
SQL Grant command is specifically used to provide privileges to database objects for a user. This command also allows users to grant permissions to other users too. Revoke command withdraw user privileges on database objects if any granted. It does operations opposite to the Grant command. When a privilege is revoked from a particular user U, then the privileges granted to all other users by user U will be revoked.
2. What are recursive cte, are they available in MYSQL workbench?
A recursive CTE references itself. It returns the result subset, then it repeatedly (recursively) references itself, and stops when it returns all the results. Yes they are available in MYSQL workbench.
3. What constraints come under the primary key constraint?
The PRIMARY KEY constraint uniquely identifies each record in a table. Primary keys must contain UNIQUE values, and cannot contain NULL values. A table can have only ONE primary key; and in the table, this primary key can consist of single or multiple columns (fields). Hence the unique constraint and the not null constraint come under the primary key by default.
4. What is the one to many relationship?
One-to-Many relationship in DBMS is a relationship between instances of an entity with more than one instance of another entity.A student can work on more than one project. Student and Project are entities here. An individual student working on 2 projects at a time would be considered as One-to-Many relationship in DBMS.
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Company name: Accenture
Role: SQL Developer
Topic: Grant and revoke, constraint, cte, one to many
1. Explain Grant and Revoke in SQL.
SQL Grant command is specifically used to provide privileges to database objects for a user. This command also allows users to grant permissions to other users too. Revoke command withdraw user privileges on database objects if any granted. It does operations opposite to the Grant command. When a privilege is revoked from a particular user U, then the privileges granted to all other users by user U will be revoked.
2. What are recursive cte, are they available in MYSQL workbench?
A recursive CTE references itself. It returns the result subset, then it repeatedly (recursively) references itself, and stops when it returns all the results. Yes they are available in MYSQL workbench.
3. What constraints come under the primary key constraint?
The PRIMARY KEY constraint uniquely identifies each record in a table. Primary keys must contain UNIQUE values, and cannot contain NULL values. A table can have only ONE primary key; and in the table, this primary key can consist of single or multiple columns (fields). Hence the unique constraint and the not null constraint come under the primary key by default.
4. What is the one to many relationship?
One-to-Many relationship in DBMS is a relationship between instances of an entity with more than one instance of another entity.A student can work on more than one project. Student and Project are entities here. An individual student working on 2 projects at a time would be considered as One-to-Many relationship in DBMS.
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Date: 25-11-2022
Company name: IKEA
Role: Data Analyst
Topic: powerbi, cte, data manipulation
1. What are the various types of refresh options provided in Power BI?
Package refresh - This synchronizes your Power BI Desktop or Excel file between the Power BI service and OneDrive, or SharePoint Online.
Model or data refresh - This refreshes the dataset within the Power BI service with data from the original data source.
Tile refresh - This updates the cache for tile visuals every 15 minutes on the dashboard once data changes.
Visual container refresh - This refreshes the visible container and updates the cached report visuals within a report once the data changes.
2. Explain some date manipulation functions in SQL.
Getdate: As its name suggests, the getdate function gives us today’s date. Dateadd: The dateadd function is used for adding a time or date interval to a date.Datediff: The datediff function is used for calculating the difference between two dates based on a given interval. Datename: The datename function can be used for extracting the parts of a date. Year, month, day: The year, month, and day functions allow for decomposing a date.
3. What is CTE in SQL?
A CTE (Common Table Expression) is a one-time result set that only exists for the duration of the query. It allows us to refer to data within a single SELECT, INSERT, UPDATE, DELETE, CREATE VIEW, or MERGE statement's execution scope. It is temporary because its result cannot be stored anywhere and will be lost as soon as a query's execution is completed.
Company name: IKEA
Role: Data Analyst
Topic: powerbi, cte, data manipulation
1. What are the various types of refresh options provided in Power BI?
Package refresh - This synchronizes your Power BI Desktop or Excel file between the Power BI service and OneDrive, or SharePoint Online.
Model or data refresh - This refreshes the dataset within the Power BI service with data from the original data source.
Tile refresh - This updates the cache for tile visuals every 15 minutes on the dashboard once data changes.
Visual container refresh - This refreshes the visible container and updates the cached report visuals within a report once the data changes.
2. Explain some date manipulation functions in SQL.
Getdate: As its name suggests, the getdate function gives us today’s date. Dateadd: The dateadd function is used for adding a time or date interval to a date.Datediff: The datediff function is used for calculating the difference between two dates based on a given interval. Datename: The datename function can be used for extracting the parts of a date. Year, month, day: The year, month, and day functions allow for decomposing a date.
3. What is CTE in SQL?
A CTE (Common Table Expression) is a one-time result set that only exists for the duration of the query. It allows us to refer to data within a single SELECT, INSERT, UPDATE, DELETE, CREATE VIEW, or MERGE statement's execution scope. It is temporary because its result cannot be stored anywhere and will be lost as soon as a query's execution is completed.
Date: 26-11-2022
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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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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Date: 27-11-2022
Company name: Elica
Role: ML Engineer
Topic: cost function, LDA, margin, knn
1. How does an implementation of kNN differ from other classification and regression algorithms like Gradient Descent, Random Forest, or Logistic Regression?
The main difference between kNN and other classification or regression algorithms is that kNN is a non-parametric algorithm, meaning that it does not make any assumptions about the underlying data. This makes kNN more flexible, but also means that it can be more computationally expensive. Other algorithms like Gradient Descent or Logistic Regression make assumptions about the data that allow them to be more efficient, but also less flexible.
2. What are Hard-Margin and Soft-Margin SVMs?
Hard-Margin SVMs have linearly separable training data. No data points are allowed in the margin areas. This type of linear classification is known as Hard margin classification.
Soft-Margin SVMs have training data that are not linearly separable. Margin violation means choosing a hyperplane, which can allow some data points to stay either in between the margin area or on the incorrect side of the hyperplane.
3. How does LDA calculate its maximum separation?
The goal is for there to be little variance within the classes and more variance between the classes. LDA tries to reduce the distance between data points in the same class while increasing the distance between the data points two different classes. The ratio of the variance within classes and the variance between classes should be as large as possible to ensure separation. Simply, the means of the classes should be far away from each other and the data point should be close to the means.
4. What is the difference between Cost Function vs Gradient Descent?
A Cost Function is something we want to minimize. For example, our cost function might be the sum of squared errors over the training set. Gradient Descent is a method for finding the minimum of a function of multiple variables.
Company name: Elica
Role: ML Engineer
Topic: cost function, LDA, margin, knn
1. How does an implementation of kNN differ from other classification and regression algorithms like Gradient Descent, Random Forest, or Logistic Regression?
The main difference between kNN and other classification or regression algorithms is that kNN is a non-parametric algorithm, meaning that it does not make any assumptions about the underlying data. This makes kNN more flexible, but also means that it can be more computationally expensive. Other algorithms like Gradient Descent or Logistic Regression make assumptions about the data that allow them to be more efficient, but also less flexible.
2. What are Hard-Margin and Soft-Margin SVMs?
Hard-Margin SVMs have linearly separable training data. No data points are allowed in the margin areas. This type of linear classification is known as Hard margin classification.
Soft-Margin SVMs have training data that are not linearly separable. Margin violation means choosing a hyperplane, which can allow some data points to stay either in between the margin area or on the incorrect side of the hyperplane.
3. How does LDA calculate its maximum separation?
The goal is for there to be little variance within the classes and more variance between the classes. LDA tries to reduce the distance between data points in the same class while increasing the distance between the data points two different classes. The ratio of the variance within classes and the variance between classes should be as large as possible to ensure separation. Simply, the means of the classes should be far away from each other and the data point should be close to the means.
4. What is the difference between Cost Function vs Gradient Descent?
A Cost Function is something we want to minimize. For example, our cost function might be the sum of squared errors over the training set. Gradient Descent is a method for finding the minimum of a function of multiple variables.
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Date - 28-11-2022
Company name: CRED
Role: Data Scientist
Topic: sets n groups, tpr-fpr, triggers, bag of words
1. What do Tableau's sets and groups mean?
Data is grouped using sets and groups according to predefined criteria. The primary distinction between the two is that although a set can have only two options—either in or out—a group can divide the dataset into several groups. A user should decide which group or sets to apply based on the conditions.
3.What do you mean by a Bag of Words (BOW)?
It is used for word frequency or occurrences to train a classifier.
It contains a text representation that describes the frequency with which words appear in a document.
It has two steps:
-A list of terms that are well-known.
-A metric for determining the existence of well-known terms.
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. What is a True positive rate and a false positive rate?
True positive rate or Recall: It gives us the percentage of the true positives captured by the model out of all the Actual Positive class.
TPR = TP/ (TP+FN)
False Positive rate: It gives us the percentage of all the false positives by my model prediction from the all Actual Negative class.
FPR = FP/(FP+TN)
Company name: CRED
Role: Data Scientist
Topic: sets n groups, tpr-fpr, triggers, bag of words
1. What do Tableau's sets and groups mean?
Data is grouped using sets and groups according to predefined criteria. The primary distinction between the two is that although a set can have only two options—either in or out—a group can divide the dataset into several groups. A user should decide which group or sets to apply based on the conditions.
3.What do you mean by a Bag of Words (BOW)?
It is used for word frequency or occurrences to train a classifier.
It contains a text representation that describes the frequency with which words appear in a document.
It has two steps:
-A list of terms that are well-known.
-A metric for determining the existence of well-known terms.
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. What is a True positive rate and a false positive rate?
True positive rate or Recall: It gives us the percentage of the true positives captured by the model out of all the Actual Positive class.
TPR = TP/ (TP+FN)
False Positive rate: It gives us the percentage of all the false positives by my model prediction from the all Actual Negative class.
FPR = FP/(FP+TN)
Date: 29-11-2022
Company name: Kenstar
Role: Data Scientist
Topic: RDBMS, f1 score, etc kernel
1. What is RDBMS? How is it different from DBMS?
RDBMS stands for Relational Database Management System that stores data in the form of a collection of tables, and relations can be defined between the common fields of these tables.
2.What is ETL in SQL?
ETL stands for Extract, Transform and Load. It is a three-step process, where we would have to start off by extracting the data from sources. Once we collate the data from different sources, what we have is raw data. This raw data has to be transformed into the tidy format, which will come in the second phase.Finally, we would have to load this tidy data into tools which would help us to find insights.
3. What is a kernel function in SVM?
In the SVM algorithm, a kernel function is a special mathematical function. In simple terms, a kernel function takes data as input and converts it into a required form. This transformation of the data is based on something called a kernel trick, which is what gives the kernel function its name. Using the kernel function, we can transform the data that is not linearly separable (cannot be separated using a straight line) into one that is linearly separable.
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.
Company name: Kenstar
Role: Data Scientist
Topic: RDBMS, f1 score, etc kernel
1. What is RDBMS? How is it different from DBMS?
RDBMS stands for Relational Database Management System that stores data in the form of a collection of tables, and relations can be defined between the common fields of these tables.
2.What is ETL in SQL?
ETL stands for Extract, Transform and Load. It is a three-step process, where we would have to start off by extracting the data from sources. Once we collate the data from different sources, what we have is raw data. This raw data has to be transformed into the tidy format, which will come in the second phase.Finally, we would have to load this tidy data into tools which would help us to find insights.
3. What is a kernel function in SVM?
In the SVM algorithm, a kernel function is a special mathematical function. In simple terms, a kernel function takes data as input and converts it into a required form. This transformation of the data is based on something called a kernel trick, which is what gives the kernel function its name. Using the kernel function, we can transform the data that is not linearly separable (cannot be separated using a straight line) into one that is linearly separable.
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.
Date: 30-11-2022
Company name: TCS
Role: Data Analyst
Topic : data cleaning, data wrangling, cte in sql
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 4 steps to use CTE in sql.
All CTE starts with "with" clause.
After with you need to define CTE name and the field names. For instance in the below code snippet I have 3 fields Count,Column and Id. The name of CTE is "MyTemp".
Once you have defined CTE we need to specify the SQL which will give the result for the CTE.
Finally you can use the CTE in your SQL query.
———————————————————-
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Company name: TCS
Role: Data Analyst
Topic : data cleaning, data wrangling, cte in sql
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 4 steps to use CTE in sql.
All CTE starts with "with" clause.
After with you need to define CTE name and the field names. For instance in the below code snippet I have 3 fields Count,Column and Id. The name of CTE is "MyTemp".
Once you have defined CTE we need to specify the SQL which will give the result for the CTE.
Finally you can use the CTE in your SQL query.
———————————————————-
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Date: 01-12-2022
Company name: Cognizant
Role: SQL Developer
Topic: superkey, buffer pool value, candidate key, null values
1. What are Query and Query language?
A query is nothing but a request sent to a database to retrieve data or information. The required data can be retrieved from a table or many tables in the database.
Query languages use various types of queries to retrieve data from databases. SQL, Datalog, and AQL are a few examples of query languages; however, SQL is known to be the widely used query language.
2. What are Superkey and candidate key?
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 attributes to identify records in a table. Unlike Superkey, all the attributes of the candidate key must be helpful to identify the records.
3. What do you mean by buffer pool and mention its benefits?
A buffer pool in SQL is also known as a buffer cache. All the resources can store their cached data pages in a buffer pool. The size of the buffer pool can be defined during the configuration of an instance of SQL Server.
The following are the benefits of a buffer pool:
Increase in I/O performance
Reduction in I/O latency
Increase in transaction throughput
Increase in reading performance
4. What is the difference between Zero and NULL values in SQL?
When a field in a column doesn’t have any value, it is said to be having a NULL value. Simply put, NULL is the blank field in a table. It can be considered as an unassigned, unknown, or unavailable value. On the contrary, zero is a number, and it is an available, assigned, and known value.
———————————————————-
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Company name: Cognizant
Role: SQL Developer
Topic: superkey, buffer pool value, candidate key, null values
1. What are Query and Query language?
A query is nothing but a request sent to a database to retrieve data or information. The required data can be retrieved from a table or many tables in the database.
Query languages use various types of queries to retrieve data from databases. SQL, Datalog, and AQL are a few examples of query languages; however, SQL is known to be the widely used query language.
2. What are Superkey and candidate key?
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 attributes to identify records in a table. Unlike Superkey, all the attributes of the candidate key must be helpful to identify the records.
3. What do you mean by buffer pool and mention its benefits?
A buffer pool in SQL is also known as a buffer cache. All the resources can store their cached data pages in a buffer pool. The size of the buffer pool can be defined during the configuration of an instance of SQL Server.
The following are the benefits of a buffer pool:
Increase in I/O performance
Reduction in I/O latency
Increase in transaction throughput
Increase in reading performance
4. What is the difference between Zero and NULL values in SQL?
When a field in a column doesn’t have any value, it is said to be having a NULL value. Simply put, NULL is the blank field in a table. It can be considered as an unassigned, unknown, or unavailable value. On the contrary, zero is a number, and it is an available, assigned, and known value.
———————————————————-
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Date: 02-12-2022
Company Name: Accenture
Role: Data Scientist
Topic: Silhouette, trend seasonality, bag of words, bagging boosting
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
———————————————————-
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Company Name: Accenture
Role: Data Scientist
Topic: Silhouette, trend seasonality, bag of words, bagging boosting
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
———————————————————-
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