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Date: 23-11-2023
Company name: Capgemini
Role: Data Analyst
Topic: python, tableau
1. What is the Difference Between a Shallow Copy and Deep Copy in python?
Deepcopy creates a different object and populates it with the child objects of the original object. Therefore, changes in the original object are not reflected in the copy. copy.deepcopy() creates a Deep Copy. Shallow copy creates a different object and populates it with the references of the child objects within the original object. Therefore, changes in the original object are reflected in the copy. copy.copy creates a Shallow Copy.
2. How can you remove duplicate values in a range of cells?
1. To delete duplicate values in a column, select the highlighted cells, and press the delete button. After deleting the values, go to the ‘Conditional Formatting’ option present in the Home tab. Choose ‘Clear Rules’ to remove the rules from the sheet. 2. You can also delete duplicate values by selecting the ‘Remove Duplicates’ option under Data Tools present in the Data tab.
3. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.
Company name: Capgemini
Role: Data Analyst
Topic: python, tableau
1. What is the Difference Between a Shallow Copy and Deep Copy in python?
Deepcopy creates a different object and populates it with the child objects of the original object. Therefore, changes in the original object are not reflected in the copy. copy.deepcopy() creates a Deep Copy. Shallow copy creates a different object and populates it with the references of the child objects within the original object. Therefore, changes in the original object are reflected in the copy. copy.copy creates a Shallow Copy.
2. How can you remove duplicate values in a range of cells?
1. To delete duplicate values in a column, select the highlighted cells, and press the delete button. After deleting the values, go to the ‘Conditional Formatting’ option present in the Home tab. Choose ‘Clear Rules’ to remove the rules from the sheet. 2. You can also delete duplicate values by selecting the ‘Remove Duplicates’ option under Data Tools present in the Data tab.
3. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.
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Date: 24-01-2023
Company name: Elica
Role: Database Developer
Topic: views, record, field, table
1.Define RDBMS.
Answer: Relational Database Management System(RDBMS) is based on a relational model of data that is stored in databases in separate tables and they are related to the use of a common column. Data can be accessed easily from the relational database using Structured Query Language (SQL).
2.Define DML Compiler.
Answer: DML compiler translates DML statements in a query language into a low-level instruction and the generated instruction can be understood by Query Evaluation Engine.
3.Explain the terms ‘Record’, ‘Field’ and ‘Table’ in terms of database.
Answer:
Record: Record is a collection of values or fields of a specific entity. For Example, An employee, Salary account, etc.
Field: A field refers to an area within a record that is reserved for specific data. For Example, Employee ID.
Table: Table is the collection of records of specific types. For Example, the Employee table is a collection of records related to all the employees.
4.Define the relationship between ‘View’ and ‘Data Independence’.
Answer: View is a virtual table that does not have its data on its own rather the data is defined from one or more underlying base tables.
Views account for logical data independence as the growth and restructuring of base tables are not reflected in views.
Company name: Elica
Role: Database Developer
Topic: views, record, field, table
1.Define RDBMS.
Answer: Relational Database Management System(RDBMS) is based on a relational model of data that is stored in databases in separate tables and they are related to the use of a common column. Data can be accessed easily from the relational database using Structured Query Language (SQL).
2.Define DML Compiler.
Answer: DML compiler translates DML statements in a query language into a low-level instruction and the generated instruction can be understood by Query Evaluation Engine.
3.Explain the terms ‘Record’, ‘Field’ and ‘Table’ in terms of database.
Answer:
Record: Record is a collection of values or fields of a specific entity. For Example, An employee, Salary account, etc.
Field: A field refers to an area within a record that is reserved for specific data. For Example, Employee ID.
Table: Table is the collection of records of specific types. For Example, the Employee table is a collection of records related to all the employees.
4.Define the relationship between ‘View’ and ‘Data Independence’.
Answer: View is a virtual table that does not have its data on its own rather the data is defined from one or more underlying base tables.
Views account for logical data independence as the growth and restructuring of base tables are not reflected in views.
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Date: 25-01-23
Company name: Infosys
Role: ML Engineer
Topic: gradient descent, random forest, kmean vs knn, svm
1. Explain how does the Gradient descent work in Linear Regression
The Gradient Descent works by starting with random values for each coefficient in the linear regression model.
After this, the sum of the squared errors is calculated for each pair of input and output values (loss function), using a learning rate as a scale factor.
For each iteration, the coefficients are updated in the direction towards minimizing the error,
then we keep repeating the iteration process until a minimum sum squared error is achieved or no further improvement is possible.
2. What does Random refer to in Random Forest?
Random forest is an extension of the bagging method as it utilizes both bagging and feature randomness to create an uncorrelated forest of decision trees. Hence, random forest is Random in the following ways:
Each tree is trained on a random subset of features, which ensures low correlation among decision trees.
Each tree in the forest is trained in 2/3-rd of the total training data and data points are drawn at random from the original dataset.
3. What is the main difference between k-Means and k-Nearest Neighbours?
k-Means is a clustering algorithm that tries to partition a set of points into k sets such that the points in each cluster tend to be near each other. It is unsupervised because the points have no external classification.
k-Nearest Neighbors is a classification (or regression) algorithm that, in order to determine the classification of a point, combines the classification of the k nearest points. It is supervised because it is trying to classify a point based on the known classification of other points.
4. What are Support Vectors in SVMs?
Support vectors are the data points nearest to the hyperplane, the points of a data set that, if removed, would alter the position of the dividing hyperplane.
Using these support vectors, we maximize the margin of the classifier.
For computing predictions, only the support vectors are used.
Company name: Infosys
Role: ML Engineer
Topic: gradient descent, random forest, kmean vs knn, svm
1. Explain how does the Gradient descent work in Linear Regression
The Gradient Descent works by starting with random values for each coefficient in the linear regression model.
After this, the sum of the squared errors is calculated for each pair of input and output values (loss function), using a learning rate as a scale factor.
For each iteration, the coefficients are updated in the direction towards minimizing the error,
then we keep repeating the iteration process until a minimum sum squared error is achieved or no further improvement is possible.
2. What does Random refer to in Random Forest?
Random forest is an extension of the bagging method as it utilizes both bagging and feature randomness to create an uncorrelated forest of decision trees. Hence, random forest is Random in the following ways:
Each tree is trained on a random subset of features, which ensures low correlation among decision trees.
Each tree in the forest is trained in 2/3-rd of the total training data and data points are drawn at random from the original dataset.
3. What is the main difference between k-Means and k-Nearest Neighbours?
k-Means is a clustering algorithm that tries to partition a set of points into k sets such that the points in each cluster tend to be near each other. It is unsupervised because the points have no external classification.
k-Nearest Neighbors is a classification (or regression) algorithm that, in order to determine the classification of a point, combines the classification of the k nearest points. It is supervised because it is trying to classify a point based on the known classification of other points.
4. What are Support Vectors in SVMs?
Support vectors are the data points nearest to the hyperplane, the points of a data set that, if removed, would alter the position of the dividing hyperplane.
Using these support vectors, we maximize the margin of the classifier.
For computing predictions, only the support vectors are used.
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Which SQL statement is used to sort the results of a query in ascending order?
Anonymous Quiz
62%
ORDER BY
30%
SORT BY
5%
GROUP BY
3%
ARRANGE BY
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Date: 27-01-2023
Company name - PWC
Role : ML Engineer
Topic: dbscan, joins in SQL, gridsearch vs random search, deployment
1. What is DBSCAN Clustering?
DBSCAN groups ‘densely grouped’ data points into a single cluster. It can identify clusters in large spatial datasets by looking at the local density of the data points. The most exciting feature of DBSCAN clustering is that it is robust to outliers. It also does not require the number of clusters to be told beforehand, unlike K-Means, where we have to specify the number of centroids.
2. What are the different forms of joins in a table?
SQL has many kinds of different joins including INNER JOIN, SELF JOIN, CROSS JOIN, and OUTER JOIN. In fact, each join type defines the way two tables are related in a query. OUTER JOINS can further be divided into LEFT OUTER JOINS, RIGHT OUTER JOINS, and FULL OUTER JOINS.
3.How is the grid search parameter different from the random search?
Model Hyperparameter tuning is very useful to enhance the performance of a machine learning model. The only difference between both the approaches is in grid search we define the combinations and do training of the model whereas in RandomizedSearchCV the model selects the combinations randomly. Both are very effective ways of tuning the parameters that increase the model generalizability.
Random search is a technique where random combinations of the hyperparameters are used to find the best solution for the built model. The drawback of random search is that it yields high variance during computing. Since the selection of parameters is completely random; and since no intelligence is used to sample these combinations, luck plays its role.
4.How should you maintain a deployed model?
A deployed model needs to be retrained after a while so as to improve the performance of the model. Since deployment, a track should be kept of the predictions made by the model and the truth values. Later this can be used to retrain the model with the new data. Also, root cause analysis for wrong predictions should be done.
Company name - PWC
Role : ML Engineer
Topic: dbscan, joins in SQL, gridsearch vs random search, deployment
1. What is DBSCAN Clustering?
DBSCAN groups ‘densely grouped’ data points into a single cluster. It can identify clusters in large spatial datasets by looking at the local density of the data points. The most exciting feature of DBSCAN clustering is that it is robust to outliers. It also does not require the number of clusters to be told beforehand, unlike K-Means, where we have to specify the number of centroids.
2. What are the different forms of joins in a table?
SQL has many kinds of different joins including INNER JOIN, SELF JOIN, CROSS JOIN, and OUTER JOIN. In fact, each join type defines the way two tables are related in a query. OUTER JOINS can further be divided into LEFT OUTER JOINS, RIGHT OUTER JOINS, and FULL OUTER JOINS.
3.How is the grid search parameter different from the random search?
Model Hyperparameter tuning is very useful to enhance the performance of a machine learning model. The only difference between both the approaches is in grid search we define the combinations and do training of the model whereas in RandomizedSearchCV the model selects the combinations randomly. Both are very effective ways of tuning the parameters that increase the model generalizability.
Random search is a technique where random combinations of the hyperparameters are used to find the best solution for the built model. The drawback of random search is that it yields high variance during computing. Since the selection of parameters is completely random; and since no intelligence is used to sample these combinations, luck plays its role.
4.How should you maintain a deployed model?
A deployed model needs to be retrained after a while so as to improve the performance of the model. Since deployment, a track should be kept of the predictions made by the model and the truth values. Later this can be used to retrain the model with the new data. Also, root cause analysis for wrong predictions should be done.
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Date: 30-01-2023
Company name: Capgemini
Role: Data Analyst
Topic: python, tableau
1. What is the Difference Between a Shallow Copy and Deep Copy in python?
Deepcopy creates a different object and populates it with the child objects of the original object. Therefore, changes in the original object are not reflected in the copy. copy.deepcopy() creates a Deep Copy. Shallow copy creates a different object and populates it with the references of the child objects within the original object. Therefore, changes in the original object are reflected in the copy. copy.copy creates a Shallow Copy.
2. How can you remove duplicate values in a range of cells?
1. To delete duplicate values in a column, select the highlighted cells, and press the delete button. After deleting the values, go to the ‘Conditional Formatting’ option present in the Home tab. Choose ‘Clear Rules’ to remove the rules from the sheet. 2. You can also delete duplicate values by selecting the ‘Remove Duplicates’ option under Data Tools present in the Data tab.
3. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.
Company name: Capgemini
Role: Data Analyst
Topic: python, tableau
1. What is the Difference Between a Shallow Copy and Deep Copy in python?
Deepcopy creates a different object and populates it with the child objects of the original object. Therefore, changes in the original object are not reflected in the copy. copy.deepcopy() creates a Deep Copy. Shallow copy creates a different object and populates it with the references of the child objects within the original object. Therefore, changes in the original object are reflected in the copy. copy.copy creates a Shallow Copy.
2. How can you remove duplicate values in a range of cells?
1. To delete duplicate values in a column, select the highlighted cells, and press the delete button. After deleting the values, go to the ‘Conditional Formatting’ option present in the Home tab. Choose ‘Clear Rules’ to remove the rules from the sheet. 2. You can also delete duplicate values by selecting the ‘Remove Duplicates’ option under Data Tools present in the Data tab.
3. Define shelves and sets in Tableau?
Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.
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#QUIZ
Observe the code given below:
def foo(x, y):
if x > y:
return x
elif x < y:
return y
else:
return 0
def bar(x, y):
return x + y
x = foo(2, 3)
y = bar(x, 4)
print(y)
Observe the code given below:
def foo(x, y):
if x > y:
return x
elif x < y:
return y
else:
return 0
def bar(x, y):
return x + y
x = foo(2, 3)
y = bar(x, 4)
print(y)
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🆒47❤45🤩11
Date: 31-01-2023
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.
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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