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Date: 02-03-2023
Company name: Biocon
Role: ML Engineer
Topic: nodes in DT, ensemble learning, adaboost, hierarchical clustering
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.
Company name: Biocon
Role: ML Engineer
Topic: nodes in DT, ensemble learning, adaboost, hierarchical clustering
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 to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Happy Learning💙
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Date: 04-03-2023
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.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Happy Learning💙
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Date: 06-03-2023
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.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Happy Learning💙
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Given a Pandas DataFrame named "df" with columns "Name" and "Score", what code would you use to sort the DataFrame by descending order of "Score"?
Anonymous Quiz
17%
df.sort("Score", ascending=True)
60%
df.sort_values("Score", ascending=False)
10%
df.sort_values("Name", ascending=True)
13%
df.sort("Name", ascending=False)
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Date: 09-03-2023
Company name: IBM
Role: Data Analyst
Topic: Join, OLTP, Joining vs blending, worksheet excel
1. 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.
2. What is OLTP?
OLTP, or online transactional processing, allows huge groups of people to execute massive amounts of database transactions in real time, usually via the internet. A database transaction occurs when data in a database is changed, inserted, deleted, or queried.
3. What is the difference between joining and blending in Tableau?
Joining term is used when you are combining data from the same source, for example, worksheet in an Excel file or tables in Oracle databaseWhile blending requires two completely defined data sources in your report.
4. How to prevent someone from copying the cell from your worksheet in excel?
If you want to protect your worksheet from being copied, go into Menu bar > Review > Protect sheet > Password.
By entering password you can prevent your worksheet from getting copied.
Company name: IBM
Role: Data Analyst
Topic: Join, OLTP, Joining vs blending, worksheet excel
1. 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.
2. What is OLTP?
OLTP, or online transactional processing, allows huge groups of people to execute massive amounts of database transactions in real time, usually via the internet. A database transaction occurs when data in a database is changed, inserted, deleted, or queried.
3. What is the difference between joining and blending in Tableau?
Joining term is used when you are combining data from the same source, for example, worksheet in an Excel file or tables in Oracle databaseWhile blending requires two completely defined data sources in your report.
4. How to prevent someone from copying the cell from your worksheet in excel?
If you want to protect your worksheet from being copied, go into Menu bar > Review > Protect sheet > Password.
By entering password you can prevent your worksheet from getting copied.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Happy Learning💙
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👉🏻DATE - 09-03-2023
Company: Grofers
Role: Data Scientist
Exp: Freshers
Link: APPLY NOW
Company: Striker Club
Role: Data Analyst Intern
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Date: 10-03-2023
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%.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Happy Learning💙
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Date: 12-03-2023
Company name: IKEA
Role: SQL Developer
Topic: denormalization, blob, text, default constraint
1. What is denormalization?
Denormalization is the opposite of normalization; redundant data is added to speed up complex queries that have multiple tables that need to be joined. Optimization of the read performance of a database is attempted by adding or grouping redundant copies of data.
2. What are the applications of SQL?
The major applications of SQL include:
• Writing data integration scripts
• Setting and running analytical queries
• Retrieving subsets of information within a database for analytics applications and transaction processing
• Adding, updating, and deleting rows and columns of data in a databases
3. What is BLOB and TEXT in MySQL?
BLOB stands for Binary Huge Objects and can be used to store binary data, whereas TEXT may be used to store a large number of strings. BLOB may be used to store binary data, which includes images, movies, audio, and applications. TEXT values behave similarly to a character string or a non-binary string.
4. What is a DEFAULT constraint?
A default constraint is used to define a default value for a column so that it is added to all new records if no other value is specified. For example, if we assign a default constraint for the E_salary column in the following table and set the default value to 85000, then all the entries of this column will have the default value of 85000, unless no other value has been assigned during the insertion.
Company name: IKEA
Role: SQL Developer
Topic: denormalization, blob, text, default constraint
1. What is denormalization?
Denormalization is the opposite of normalization; redundant data is added to speed up complex queries that have multiple tables that need to be joined. Optimization of the read performance of a database is attempted by adding or grouping redundant copies of data.
2. What are the applications of SQL?
The major applications of SQL include:
• Writing data integration scripts
• Setting and running analytical queries
• Retrieving subsets of information within a database for analytics applications and transaction processing
• Adding, updating, and deleting rows and columns of data in a databases
3. What is BLOB and TEXT in MySQL?
BLOB stands for Binary Huge Objects and can be used to store binary data, whereas TEXT may be used to store a large number of strings. BLOB may be used to store binary data, which includes images, movies, audio, and applications. TEXT values behave similarly to a character string or a non-binary string.
4. What is a DEFAULT constraint?
A default constraint is used to define a default value for a column so that it is added to all new records if no other value is specified. For example, if we assign a default constraint for the E_salary column in the following table and set the default value to 85000, then all the entries of this column will have the default value of 85000, unless no other value has been assigned during the insertion.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Happy Learning💙
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Date: 14-03-2023
Company name: IBM
Role: ML Engineer
Topic: RoC - AuC, classification, regression, self driving cars, discriminative model
1. Explain how a ROC curve works.
Answer: The ROC curve is a graphical representation of the contrast between true positive rates and the false positive rate at various thresholds. It’s often used as a proxy for the trade-off between the sensitivity of the model (true positives) vs the fall-out or the probability it will trigger a false alarm (false positives).
2. What’s the difference between a generative and discriminative model?
Answer: A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.
3. When should you use classification over regression?
Answer: Classification produces discrete values and dataset to strict categories, while regression gives you continuous results that allow you to better distinguish differences between individual points. You would use classification over regression if you wanted your results to reflect the belongingness of data points in your dataset to certain explicit categories (ex: If you wanted to know whether a name was male or female rather than just how correlated they were with male and female names.)
4. How do you think Google is training data for self-driving cars?
Answer: Google is currently using recaptcha to source labeled data on storefronts and traffic signs. They are also building on training data collected by Sebastian Thrun at GoogleX—some of which was obtained by his grad students driving buggies on desert dunes!
Company name: IBM
Role: ML Engineer
Topic: RoC - AuC, classification, regression, self driving cars, discriminative model
1. Explain how a ROC curve works.
Answer: The ROC curve is a graphical representation of the contrast between true positive rates and the false positive rate at various thresholds. It’s often used as a proxy for the trade-off between the sensitivity of the model (true positives) vs the fall-out or the probability it will trigger a false alarm (false positives).
2. What’s the difference between a generative and discriminative model?
Answer: A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.
3. When should you use classification over regression?
Answer: Classification produces discrete values and dataset to strict categories, while regression gives you continuous results that allow you to better distinguish differences between individual points. You would use classification over regression if you wanted your results to reflect the belongingness of data points in your dataset to certain explicit categories (ex: If you wanted to know whether a name was male or female rather than just how correlated they were with male and female names.)
4. How do you think Google is training data for self-driving cars?
Answer: Google is currently using recaptcha to source labeled data on storefronts and traffic signs. They are also building on training data collected by Sebastian Thrun at GoogleX—some of which was obtained by his grad students driving buggies on desert dunes!
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Happy Learning💙
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CloudyML 100+ Data Scientist Interview QnA PDF.pdf
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DOWNLOAD 100+ DATA SCIENTIST INTERVIEW QNA PDF COLLECTION ⬇️😎
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