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Date: 28-06-2023
Company Name - Fractal Analytics
Role- Data Scientist
Topics - residual error, missing value, confusion matrix, model performance, kmeans
1. What is residual error?
A. The difference between a group of values observed and their arithmetical mean. The difference between what was expected and what was predicted is called the residual error.
2. Select an appropriate value of k in k-means?
A. The elbow approach is a popular way for determining the ideal value of K when using the K-Means Clustering Algorithm. The essential concept behind this method is that it plots various cost values as k changes. There will be fewer elements in the cluster as the value of K grows.
3. How do you deal missing values in datasets?
A. Deleting Rows with missing values, Impute missing values for continuous variable, Impute missing values for categorical variable, Using Algorithms that support missing values, Prediction of missing values, Imputation using Deep Learning Library — Datawig, Imputation using Machine Learning libraries like SimpleImputer, KNNImputer,etc.
4. What is a confusion matrix?
A. A confusion matrix is a method of summarising a classification algorithm's performance. Calculating a confusion matrix can help you understand what your classification model is getting right and where it is going wrong. It gives us: “true positive” for correctly predicted event values, “false positive” for incorrectly predicted event values, “true negative” for correctly predicted no-event values, “false negative” for incorrectly predicted no-event values.
5. Why is mean square error a bad measure of model performance?
A. A disadvantage of the mean-squared error is that it is not very interpretable because MSEs vary depending on the prediction task and thus cannot be compared across different tasks. Assume, for example, that one prediction task is concerned with estimating the weight of trucks and another is concerned with estimating the weight of apples. Then, in the first task, a good model may have an RMSE of 100 kg, while a good model for the second task may have an RMSE of 0.5 kg. Therefore, while RMSE is viable for model selection, it is rarely reported and R2 is used instead.
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Company Name - Fractal Analytics
Role- Data Scientist
Topics - residual error, missing value, confusion matrix, model performance, kmeans
1. What is residual error?
A. The difference between a group of values observed and their arithmetical mean. The difference between what was expected and what was predicted is called the residual error.
2. Select an appropriate value of k in k-means?
A. The elbow approach is a popular way for determining the ideal value of K when using the K-Means Clustering Algorithm. The essential concept behind this method is that it plots various cost values as k changes. There will be fewer elements in the cluster as the value of K grows.
3. How do you deal missing values in datasets?
A. Deleting Rows with missing values, Impute missing values for continuous variable, Impute missing values for categorical variable, Using Algorithms that support missing values, Prediction of missing values, Imputation using Deep Learning Library — Datawig, Imputation using Machine Learning libraries like SimpleImputer, KNNImputer,etc.
4. What is a confusion matrix?
A. A confusion matrix is a method of summarising a classification algorithm's performance. Calculating a confusion matrix can help you understand what your classification model is getting right and where it is going wrong. It gives us: “true positive” for correctly predicted event values, “false positive” for incorrectly predicted event values, “true negative” for correctly predicted no-event values, “false negative” for incorrectly predicted no-event values.
5. Why is mean square error a bad measure of model performance?
A. A disadvantage of the mean-squared error is that it is not very interpretable because MSEs vary depending on the prediction task and thus cannot be compared across different tasks. Assume, for example, that one prediction task is concerned with estimating the weight of trucks and another is concerned with estimating the weight of apples. Then, in the first task, a good model may have an RMSE of 100 kg, while a good model for the second task may have an RMSE of 0.5 kg. Therefore, while RMSE is viable for model selection, it is rarely reported and R2 is used instead.
————————————————————————-
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Date: 30-06-2023
Company name: Infosys
Role: Data Scientist
Topic: LTMS, F1 Score, ensemble, CTE, NumPy
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. What’s the F1 score? How would you use it?
The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst.
5. Name an example where ensemble techniques might be useful?
Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data). You could list some examples of ensemble methods (bagging, boosting, the “bucket of models” method) and demonstrate how they could increase predictive power.
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Company name: Infosys
Role: Data Scientist
Topic: LTMS, F1 Score, ensemble, CTE, NumPy
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. What’s the F1 score? How would you use it?
The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst.
5. Name an example where ensemble techniques might be useful?
Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data). You could list some examples of ensemble methods (bagging, boosting, the “bucket of models” method) and demonstrate how they could increase predictive power.
————————————————————————-
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Date: 02-07-2023
Company name: Elica
Role: Data Scientist
Topic: kmeans, KNN, LSTM, powerbi, SQL
1. 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.
2. If your Time-Series Dataset is very long, what architecture would you use?
If the dataset for time-series is very long, LSTMs are ideal for it because it can not only process single data points, but also entire sequences of data. A time-series being a sequence of data makes LSTM ideal for it.For an even stronger representational capacity, making the LSTM's multi-layered is better.Another method for long time-series dataset is to use CNNs to extract information.
3. How would you define Power BI as an effective solution ?
Power BI is a strong business analytical tool that creates useful insights and reports by collating data from unrelated sources. This data can be extracted from any source like Microsoft Excel or hybrid data warehouses. Power BI drives an extreme level of utility and purpose using interactive graphical interface and visualizations.
4. Why is the KNN Algorithm known as Lazy Learner?
When the KNN algorithm gets the training data, it does not learn and make a model, it just stores the data. Instead of finding any discriminative function with the help of the training data, it follows instance-based learning and also uses the training data when it actually needs to do some prediction on the unseen datasets. As a result, KNN does not immediately learn a model rather delays the learning thereby being referred to as Lazy Learner.
5. Explain the difference between drop and truncate.
In SQL, the DROP command is used to remove the whole database or table indexes, data, and more. Whereas the TRUNCATE command is used to remove all the rows from the table.
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Company name: Elica
Role: Data Scientist
Topic: kmeans, KNN, LSTM, powerbi, SQL
1. 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.
2. If your Time-Series Dataset is very long, what architecture would you use?
If the dataset for time-series is very long, LSTMs are ideal for it because it can not only process single data points, but also entire sequences of data. A time-series being a sequence of data makes LSTM ideal for it.For an even stronger representational capacity, making the LSTM's multi-layered is better.Another method for long time-series dataset is to use CNNs to extract information.
3. How would you define Power BI as an effective solution ?
Power BI is a strong business analytical tool that creates useful insights and reports by collating data from unrelated sources. This data can be extracted from any source like Microsoft Excel or hybrid data warehouses. Power BI drives an extreme level of utility and purpose using interactive graphical interface and visualizations.
4. Why is the KNN Algorithm known as Lazy Learner?
When the KNN algorithm gets the training data, it does not learn and make a model, it just stores the data. Instead of finding any discriminative function with the help of the training data, it follows instance-based learning and also uses the training data when it actually needs to do some prediction on the unseen datasets. As a result, KNN does not immediately learn a model rather delays the learning thereby being referred to as Lazy Learner.
5. Explain the difference between drop and truncate.
In SQL, the DROP command is used to remove the whole database or table indexes, data, and more. Whereas the TRUNCATE command is used to remove all the rows from the table.
————————————————————-
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Date - 05-07-2023
Company name: Capgemini
Role: Data Scientist
Topic: decorators, ACID, one hot encoding, KPI, Pandas
1. What are decorators in Python?
Ans: Decorators are used to add some design patterns to a function without changing its structure. Decorators generally are defined before the function they are enhancing. To apply a decorator we first define the decorator function. Then we write the function it is applied to and simply add the decorator function above the function it has to be applied to. For this, we use the @ symbol before the decorator.
2. What is the ACID property in a database?
The full form of ACID is atomicity, consistency, isolation, and durability.
• Atomicity refers that if any aspect of a transaction fails, the whole transaction fails and the database state remains unchanged.
• Consistency means that the data meets all validity guidelines.
• Concurrency management is the primary objective of isolation.
• Durability ensures that once a transaction is committed, it will occur regardless of what happens in between such as a power outage, fire, or some other kind of disturbance.
3. What is the meaning of KPI in statistics?
KPI is an acronym for a key performance indicator. It can be defined as a quantifiable measure to understand whether the goal is being achieved or not. KPI is a reliable metric to measure the performance level of an organization or individual with respect to the objectives. An example of KPI in an organization is the expense ratio.
4. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?
One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesn’t affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.
5. 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.
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Company name: Capgemini
Role: Data Scientist
Topic: decorators, ACID, one hot encoding, KPI, Pandas
1. What are decorators in Python?
Ans: Decorators are used to add some design patterns to a function without changing its structure. Decorators generally are defined before the function they are enhancing. To apply a decorator we first define the decorator function. Then we write the function it is applied to and simply add the decorator function above the function it has to be applied to. For this, we use the @ symbol before the decorator.
2. What is the ACID property in a database?
The full form of ACID is atomicity, consistency, isolation, and durability.
• Atomicity refers that if any aspect of a transaction fails, the whole transaction fails and the database state remains unchanged.
• Consistency means that the data meets all validity guidelines.
• Concurrency management is the primary objective of isolation.
• Durability ensures that once a transaction is committed, it will occur regardless of what happens in between such as a power outage, fire, or some other kind of disturbance.
3. What is the meaning of KPI in statistics?
KPI is an acronym for a key performance indicator. It can be defined as a quantifiable measure to understand whether the goal is being achieved or not. KPI is a reliable metric to measure the performance level of an organization or individual with respect to the objectives. An example of KPI in an organization is the expense ratio.
4. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?
One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesn’t affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.
5. 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.
————————————————————-
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Date - 07-07-2023
Company name: IBM
Role: Data Analyst
Topic: Clustering, excel, SQL, parameter in tableau
1. What is Density-based Clustering?
Density-Based Clustering is an unsupervised machine learning method that identifies different groups or clusters in the data space. These clustering techniques are based on the concept that a cluster in the data space is a contiguous region of high point density, separated from other such clusters by contiguous regions of low point density.
Partition-based(K-means) and Hierarchical clustering techniques are highly efficient with normal-shaped clusters while density-based techniques are efficient in arbitrary-shaped clusters or detecting outliers.
2. How to create empty tables with the same structure as another table?
To create empty tables:
Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure. As a result, SQL creates a new table with a duplicate structure to accept the fetched entries, but nothing is stored into the new table since the WHERE clause is active.
3. What is a Parameter in Tableau? Give an Example.
A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.
4. How will you write the formula for the following in Excel? - Multiply the value in cell A1 by 10, add the result by 5, and divide it by 2.
To write a formula for the above-stated question, we have to follow the PEDMAS Precedence. The correct answer is ((A1*10)+5)/2.
Answers such as =A1*10+5/2 and =(A1*10)+5/2 are not correct. We must put parentheses brackets after a particular operation.
5. 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.
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Company name: IBM
Role: Data Analyst
Topic: Clustering, excel, SQL, parameter in tableau
1. What is Density-based Clustering?
Density-Based Clustering is an unsupervised machine learning method that identifies different groups or clusters in the data space. These clustering techniques are based on the concept that a cluster in the data space is a contiguous region of high point density, separated from other such clusters by contiguous regions of low point density.
Partition-based(K-means) and Hierarchical clustering techniques are highly efficient with normal-shaped clusters while density-based techniques are efficient in arbitrary-shaped clusters or detecting outliers.
2. How to create empty tables with the same structure as another table?
To create empty tables:
Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure. As a result, SQL creates a new table with a duplicate structure to accept the fetched entries, but nothing is stored into the new table since the WHERE clause is active.
3. What is a Parameter in Tableau? Give an Example.
A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.
4. How will you write the formula for the following in Excel? - Multiply the value in cell A1 by 10, add the result by 5, and divide it by 2.
To write a formula for the above-stated question, we have to follow the PEDMAS Precedence. The correct answer is ((A1*10)+5)/2.
Answers such as =A1*10+5/2 and =(A1*10)+5/2 are not correct. We must put parentheses brackets after a particular operation.
5. 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.
————————————————————-
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Date: 09-07-2023
Company name: IKEA
Role: Data Analyst
Topics: data flows, time series analysis, NVL() function, t-test z-test, commands
1. How can we deal with problems that arise when the data flows in from a variety of sources?
There are many ways to go about dealing with multi-source problems. However, these are done primarily to solve the problems of:
Identifying the presence of similar/same records and merging them into a single recordRe-structuring the schema to ensure there is good schema integration
2. Where is Time Series Analysis used?
Since time series analysis (TSA) has a wide scope of usage, it can be used in multiple domains. Here are some of the places where TSA plays an important role:
Statistics
Signal processing
Econometrics
Weather forecasting
Earthquake prediction
Astronomy
Applied science
3. What are the ideal situations in which t-test or z-test can be used?
It is a standard practice that a t-test is used when there is a sample size less than 30 and the z-test is considered when the sample size exceeds 30 in most cases.
4. What is the usage of the NVL() function?
The NVL() function is used to convert the NULL value to the other value. The function returns the value of the second parameter if the first parameter is NULL. If the first parameter is anything other than NULL, it is left unchanged. This function is used in Oracle, not in SQL and MySQL. Instead of NVL() function, MySQL have IFNULL() and SQL Server have ISNULL() function.
5. What is the difference between DROP and TRUNCATE commands?
DROP command removes a table and it cannot be rolled back from the database whereas TRUNCATE command removes all the rows from the table.
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Company name: IKEA
Role: Data Analyst
Topics: data flows, time series analysis, NVL() function, t-test z-test, commands
1. How can we deal with problems that arise when the data flows in from a variety of sources?
There are many ways to go about dealing with multi-source problems. However, these are done primarily to solve the problems of:
Identifying the presence of similar/same records and merging them into a single recordRe-structuring the schema to ensure there is good schema integration
2. Where is Time Series Analysis used?
Since time series analysis (TSA) has a wide scope of usage, it can be used in multiple domains. Here are some of the places where TSA plays an important role:
Statistics
Signal processing
Econometrics
Weather forecasting
Earthquake prediction
Astronomy
Applied science
3. What are the ideal situations in which t-test or z-test can be used?
It is a standard practice that a t-test is used when there is a sample size less than 30 and the z-test is considered when the sample size exceeds 30 in most cases.
4. What is the usage of the NVL() function?
The NVL() function is used to convert the NULL value to the other value. The function returns the value of the second parameter if the first parameter is NULL. If the first parameter is anything other than NULL, it is left unchanged. This function is used in Oracle, not in SQL and MySQL. Instead of NVL() function, MySQL have IFNULL() and SQL Server have ISNULL() function.
5. What is the difference between DROP and TRUNCATE commands?
DROP command removes a table and it cannot be rolled back from the database whereas TRUNCATE command removes all the rows from the table.
————————————————————-
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Date: 04-04-2023
Company: Reliance JIO
Role: Data Scientist
Topic: svm kernels, cross validation, relationship types, data types in tableau , NLP
1. What are Different Kernels in SVM?
Linear kernel - used when data is linearly separable.
Polynomial kernel - When you have discrete data that has no natural notion of smoothness.
Radial basis kernel - Create a decision boundary able to do a much better job of separating two classes than the linear kernel.
Sigmoid kernel - used as an activation function for neural networks.
2. What is Cross-Validation?
Cross-validation is a method of splitting all your data into three parts: training, testing, and validation data. Data is split into k subsets, and the model has trained on k-1of those datasets.
The last subset is held for testing. This is done for each of the subsets. This is k-fold cross-validation. Finally, the scores from all the k-folds are averaged to produce the final score.
3. List the different types of relationships in SQL.
One-to-One - This can be defined as the relationship between two tables where each record in one table is associated with the maximum of one record in the other table.
One-to-Many & Many-to-One - This is the most commonly used relationship where a record in a table is associated with multiple records in the other table.
Many-to-Many - This is used in cases when multiple instances on both sides are needed for defining a relationship.
Self-Referencing Relationships - This is used when a table needs to define a relationship with itself.
4. What Are the Data Types Supported in Tableau?
Following data types are supported in Tableau:
Text (string) values
Date values
Date and time values
Numerical values
Boolean values (relational only)
Geographical values (used with maps)
5. What are the uses of using RNN in NLP?
The RNN is a stateful neural network, which means that it not only retains information from the previous layer but also from the previous pass. Thus, this neuron is said to have connections between passes, and through time.
For the RNN the order of the input matters due to being stateful. The same words with different orders will yield different outputs.
RNN can be used for unsegmented, connected applications such as handwriting recognition or speech recognition.
————————————————————-
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Company: Reliance JIO
Role: Data Scientist
Topic: svm kernels, cross validation, relationship types, data types in tableau , NLP
1. What are Different Kernels in SVM?
Linear kernel - used when data is linearly separable.
Polynomial kernel - When you have discrete data that has no natural notion of smoothness.
Radial basis kernel - Create a decision boundary able to do a much better job of separating two classes than the linear kernel.
Sigmoid kernel - used as an activation function for neural networks.
2. What is Cross-Validation?
Cross-validation is a method of splitting all your data into three parts: training, testing, and validation data. Data is split into k subsets, and the model has trained on k-1of those datasets.
The last subset is held for testing. This is done for each of the subsets. This is k-fold cross-validation. Finally, the scores from all the k-folds are averaged to produce the final score.
3. List the different types of relationships in SQL.
One-to-One - This can be defined as the relationship between two tables where each record in one table is associated with the maximum of one record in the other table.
One-to-Many & Many-to-One - This is the most commonly used relationship where a record in a table is associated with multiple records in the other table.
Many-to-Many - This is used in cases when multiple instances on both sides are needed for defining a relationship.
Self-Referencing Relationships - This is used when a table needs to define a relationship with itself.
4. What Are the Data Types Supported in Tableau?
Following data types are supported in Tableau:
Text (string) values
Date values
Date and time values
Numerical values
Boolean values (relational only)
Geographical values (used with maps)
5. What are the uses of using RNN in NLP?
The RNN is a stateful neural network, which means that it not only retains information from the previous layer but also from the previous pass. Thus, this neuron is said to have connections between passes, and through time.
For the RNN the order of the input matters due to being stateful. The same words with different orders will yield different outputs.
RNN can be used for unsegmented, connected applications such as handwriting recognition or speech recognition.
————————————————————-
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