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.
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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.
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Date: 17-05-2023
Company name: Capgemini
Role: Data Scientist
Topic: decorators, acid, one hot encoding, kpi
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.
β’ Atomicity refers that if any aspect of a transaction fails, the whole transaction fails and the database state remains unchanged.
β’ Consistency means that the data meets all validity guidelines.
β’ Concurrency management is the primary objective of isolation.
β’ 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 doesnβt 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.
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Company name: Capgemini
Role: Data Scientist
Topic: decorators, acid, one hot encoding, kpi
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.
β’ Atomicity refers that if any aspect of a transaction fails, the whole transaction fails and the database state remains unchanged.
β’ Consistency means that the data meets all validity guidelines.
β’ Concurrency management is the primary objective of isolation.
β’ 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 doesnβt 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.
If you want to learn Data Science & Analytics from scratch and get placed then check our Industry-Oriented Placement Assurance Programπ
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Date: 18-05-2023
Company name: Snapdeal
role: SQL Developer
Topic: constraint, clustered index, subsets of SQL
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. Find the Constraint information from the table?
There are so many times where user needs to find out the specific constraint information of the table. The following queries are useful, SELECT * From User_Constraints; SELECT * FROM User_Cons_Columns;
3. What is the difference between clustered and non-clustered indexes?
Clustered indexes can be read rapidly rather than non-clustered indexes.
Clustered indexes store data physically in the table or view whereas, non-clustered indexes do not store data in the table as it has separate structure from the data row.
4. What are the subsets of SQL?
DDL (Data Definition Language): Used to define the data structure it consists of the commands like CREATE, ALTER, DROP, etc.
DML (Data Manipulation Language): Used to manipulate already existing data in the database, commands like SELECT, UPDATE, INSERT
DCL (Data Control Language): Used to control access to data in the database, commands like GRANT, REVOKE.
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Company name: Snapdeal
role: SQL Developer
Topic: constraint, clustered index, subsets of SQL
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. Find the Constraint information from the table?
There are so many times where user needs to find out the specific constraint information of the table. The following queries are useful, SELECT * From User_Constraints; SELECT * FROM User_Cons_Columns;
3. What is the difference between clustered and non-clustered indexes?
Clustered indexes can be read rapidly rather than non-clustered indexes.
Clustered indexes store data physically in the table or view whereas, non-clustered indexes do not store data in the table as it has separate structure from the data row.
4. What are the subsets of SQL?
DDL (Data Definition Language): Used to define the data structure it consists of the commands like CREATE, ALTER, DROP, etc.
DML (Data Manipulation Language): Used to manipulate already existing data in the database, commands like SELECT, UPDATE, INSERT
DCL (Data Control Language): Used to control access to data in the database, commands like GRANT, REVOKE.
Top companies are currently seeking exceptional individuals who possess comprehensive skills in data science, data analysis, and data engineering. These companies are willing to offer lucrative salaries for such highly talented professionals.π₯
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β€6
Date: 19-05-2023
Company name: Musigma
Role: ML Engineer
Topic: random forest, linear regression, naive bayes, gradient descent
1. How is the Error calculated in a Linear Regression model?
Measuring the distance of the observed y-values from the predicted y-values at each value of x.
Squaring each of these distances.
Calculating the mean of each of the squared distances.
MSE = (1/n) * Ξ£(actual β forecast)2
The smaller the Mean Squared Error, the closer you are to finding the line of best fit
How bad or good is this final value always depends on the context of the problem, but the main goal is that its value is as minimal as possible.
2. Explain the intuition behind the Gradient Descent algorithm.
Gradient descent is an optimization algorithm thatβs used when training a machine learning model and is based on a convex function and tweaks its parameters iteratively to minimize a given function to its local minimum (that is, slope = 0).
For a start, we have to select a random bias and weights, and then iterate over the slope function to get a slope of 0.
The way we change update the value of the bias and weights is through a variable called the learning rate. We have to be wise on the learning rate because choosing:
A small leaning rate may lead to the model to take some time to learn
A large learning rate will make the model converge as our pointer will shoot and weβll not be able to get to minima.
3. How is a Random Forest related to Decision Trees?
Random forest is an ensemble learning method that works by constructing a multitude of decision trees. A random forest can be constructed for both classification and regression tasks.
Random forest outperforms decision trees, and it also does not have the habit of overfitting the data as decision trees do.
A decision tree trained on a specific dataset will become very deep and cause overfitting. To create a random forest, decision trees can be trained on different subsets of the training dataset, and then the different decision trees can be averaged with the goal of decreasing the variance.
4. What are some disadvantages of using Naive Bayes Algorithm?
Some disadvantages of using Naive Bayes Algorithm are:
It relies on a very big assumption that the independent variables are not related to each other.
It is generally not suitable for datasets with large numbers of numerical attributes.
It has been observed that if a rare case is not in the training dataset but is in the testing dataset, then it will most definitely be wrong.
Top companies are currently seeking exceptional individuals who possess comprehensive skills in data science, data analysis, and data engineering. These companies are willing to offer lucrative salaries for such highly talented professionals.π₯
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Company name: Musigma
Role: ML Engineer
Topic: random forest, linear regression, naive bayes, gradient descent
1. How is the Error calculated in a Linear Regression model?
Measuring the distance of the observed y-values from the predicted y-values at each value of x.
Squaring each of these distances.
Calculating the mean of each of the squared distances.
MSE = (1/n) * Ξ£(actual β forecast)2
The smaller the Mean Squared Error, the closer you are to finding the line of best fit
How bad or good is this final value always depends on the context of the problem, but the main goal is that its value is as minimal as possible.
2. Explain the intuition behind the Gradient Descent algorithm.
Gradient descent is an optimization algorithm thatβs used when training a machine learning model and is based on a convex function and tweaks its parameters iteratively to minimize a given function to its local minimum (that is, slope = 0).
For a start, we have to select a random bias and weights, and then iterate over the slope function to get a slope of 0.
The way we change update the value of the bias and weights is through a variable called the learning rate. We have to be wise on the learning rate because choosing:
A small leaning rate may lead to the model to take some time to learn
A large learning rate will make the model converge as our pointer will shoot and weβll not be able to get to minima.
3. How is a Random Forest related to Decision Trees?
Random forest is an ensemble learning method that works by constructing a multitude of decision trees. A random forest can be constructed for both classification and regression tasks.
Random forest outperforms decision trees, and it also does not have the habit of overfitting the data as decision trees do.
A decision tree trained on a specific dataset will become very deep and cause overfitting. To create a random forest, decision trees can be trained on different subsets of the training dataset, and then the different decision trees can be averaged with the goal of decreasing the variance.
4. What are some disadvantages of using Naive Bayes Algorithm?
Some disadvantages of using Naive Bayes Algorithm are:
It relies on a very big assumption that the independent variables are not related to each other.
It is generally not suitable for datasets with large numbers of numerical attributes.
It has been observed that if a rare case is not in the training dataset but is in the testing dataset, then it will most definitely be wrong.
Top companies are currently seeking exceptional individuals who possess comprehensive skills in data science, data analysis, and data engineering. These companies are willing to offer lucrative salaries for such highly talented professionals.π₯
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Date: 20-05-2023
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 doesnβt 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 doesnβt 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.
Become A Data Superstar π€©ππ» https://ds.cloudyml.com/data-superstar
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β€10π3π€©1
Date: 23-05-2023
Company name: Infosys
Role: Data Scientist
Topic: LSTM, kmeans, 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?
Pythonβs 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.
Become A Data Superstar π€©ππ» https://ds.cloudyml.com/data-superstar
Company name: Infosys
Role: Data Scientist
Topic: LSTM, kmeans, 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?
Pythonβs 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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β€1π1
π¨JOB OPENING UPDATEπ¨
Company: 3I-infotech Limited
Role: Data Scientist
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Date - 24-05-2023
Company name: Tredence
Role: Data Analyst
Topic: data integrity, joining vs blending, slicing, now and current date
1. What is Data Integrity?
Data Integrity is the assurance of accuracy and consistency of data over its entire life-cycle and is a critical aspect of the design, implementation, and usage of any system which stores, processes, or retrieves data. It also defines integrity constraints to enforce business rules on the data when it is entered into an application or a database.
2. What is the Difference Between Joining and Blending in Tableau?
Combining the data from two or more different sources is data blending, such as Oracle, Excel, and SQL Server. In data blending, each data source contains its own set of dimensions and measures. Combining the data between two or more tables or sheets within the same data source is data joining. All the combined tables or sheets contain a common set of dimensions and measures.
3. What is slicing in Python?
As the name suggests, βslicingβ is taking parts of.
Syntax for slicing is [start : stop : step]
start is the starting index from where to slice a list or tuple
stop is the ending index or where to stop.
step is the number of steps to jump.
Default value for start is 0, stop is number of items, step is 1.
Slicing can be done on strings, arrays, lists, and tuples.
4. What is the difference between NOW() and CURRENT_DATE() in SQL?
NOW() returns a constant time that indicates the time at which the statement began to execute. (Within a stored function or trigger, NOW() returns the time at which the function or triggering statement began to execute.
The simple difference between NOW() and CURRENT_DATE() is that NOW() will fetch the current date and time both in format βYYYY-MM_DD HH:MM:SSβ while CURRENT_DATE() will fetch the date of the current day βYYYY-MM_DDβ.
Company name: Tredence
Role: Data Analyst
Topic: data integrity, joining vs blending, slicing, now and current date
1. What is Data Integrity?
Data Integrity is the assurance of accuracy and consistency of data over its entire life-cycle and is a critical aspect of the design, implementation, and usage of any system which stores, processes, or retrieves data. It also defines integrity constraints to enforce business rules on the data when it is entered into an application or a database.
2. What is the Difference Between Joining and Blending in Tableau?
Combining the data from two or more different sources is data blending, such as Oracle, Excel, and SQL Server. In data blending, each data source contains its own set of dimensions and measures. Combining the data between two or more tables or sheets within the same data source is data joining. All the combined tables or sheets contain a common set of dimensions and measures.
3. What is slicing in Python?
As the name suggests, βslicingβ is taking parts of.
Syntax for slicing is [start : stop : step]
start is the starting index from where to slice a list or tuple
stop is the ending index or where to stop.
step is the number of steps to jump.
Default value for start is 0, stop is number of items, step is 1.
Slicing can be done on strings, arrays, lists, and tuples.
4. What is the difference between NOW() and CURRENT_DATE() in SQL?
NOW() returns a constant time that indicates the time at which the statement began to execute. (Within a stored function or trigger, NOW() returns the time at which the function or triggering statement began to execute.
The simple difference between NOW() and CURRENT_DATE() is that NOW() will fetch the current date and time both in format βYYYY-MM_DD HH:MM:SSβ while CURRENT_DATE() will fetch the date of the current day βYYYY-MM_DDβ.
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π8β€2
Date: 26-05-2023
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 Β± (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 Β± (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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Date: 29-05-2023
Company name: Cloudera
Role: Data Analyst
Topic: rank vs dense rank, one hot encoding , label encoding, Excel, PowerBI, Tableau
1. What is the difference between the RANK() and DENSE_RANK() functions?
The RANK() function in the result set defines the rank of each row within your ordered partition. If both rows have the same rank, the next number in the ranking will be the previous rank plus a number of duplicates. If we have three records at rank 4, for example, the next level indicated is 7. The DENSE_RANK() function assigns a distinct rank to each row within a partition based on the provided column value, with no gaps. If we have three records at rank 4, for example, the next level indicated is 5.
2. 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 doesnβt 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.
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.
5. 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 β students having grades of more than 70%.
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Company name: Cloudera
Role: Data Analyst
Topic: rank vs dense rank, one hot encoding , label encoding, Excel, PowerBI, Tableau
1. What is the difference between the RANK() and DENSE_RANK() functions?
The RANK() function in the result set defines the rank of each row within your ordered partition. If both rows have the same rank, the next number in the ranking will be the previous rank plus a number of duplicates. If we have three records at rank 4, for example, the next level indicated is 7. The DENSE_RANK() function assigns a distinct rank to each row within a partition based on the provided column value, with no gaps. If we have three records at rank 4, for example, the next level indicated is 5.
2. 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 doesnβt 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.
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.
5. 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 β students having grades of more than 70%.
Become A Data Superstar π€©ππ» https://ds.cloudyml.com/data-superstar
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Date: 30-05-2023
Company name: Twitter
Role: Data Scientist
Company: Twitter
Topic: bias variance, neural network, tableau, SQL missing values
1. What is bias-variance tradeoff in machine learning?
The goal in machine learning is to strike a balance between bias and variance to achieve good generalization performance. Ideally, we want a model that can capture the underlying patterns and complexity of the data while avoiding overfitting or underfitting. This balance is known as the bias-variance trade-off. Increasing the complexity of a model, such as using more features or increasing the model's capacity (e.g., adding more layers in a neural network), typically reduces bias and increases variance. Conversely, reducing the complexity of a model, such as using simpler algorithms or reducing the number of features, generally increases bias and reduces variance. To find the optimal trade-off, one can employ various techniques such as cross-validation, regularization, or ensemble methods. Cross-validation helps evaluate the model's performance on unseen data and aids in selecting the appropriate complexity level. Regularization techniques add constraints to the model to reduce overfitting. Ensemble methods combine multiple models to leverage the strengths of each model while mitigating their weaknesses. Understanding the bias-variance trade-off is crucial for selecting the appropriate models, optimizing hyperparameters, and ensuring the model's ability to generalize well to unseen data.
2. What are the differences between shallow neural networks and deep neural networks?
Shallow Neural Networks: Shallow neural networks have a small number of hidden layers (typically one or two). They are relatively simpler and have fewer parameters to learn. Shallow networks are suitable for simpler tasks or datasets with fewer complex patterns. Deep Neural Networks: Deep neural networks have a large number of hidden layers (more than two). They are more complex and have a significantly higher number of parameters. Deep networks can learn intricate patterns and hierarchies of features from data. They are more suitable for complex tasks such as image recognition, natural language processing, and speech recognition.
3. How do you handle missing values in SQL queries?
To handle missing values in SQL queries, you can use the COALESCE or ISNULL function to replace NULL values with a specified default value, or you can use the WHERE clause to filter out rows with missing values using the IS NULL or IS NOT NULL condition.
4. How would you create a dashboard in Tableau to visualize sales performance over time?
To create a dashboard in Tableau to visualize sales performance over time, you would:
Connect to your sales data source.
- Drag a date field to the Columns or Rows shelf to represent time.
- Drag a sales measure to the Rows or Columns shelf to represent the sales value.
- Choose an appropriate visualization type, such as a line chart or area chart.
- Add any additional dimensions or measures to the visualization for more detailed insights.
- Customize the visual appearance, including axis labels, titles, and formatting.
- Create a new dashboard and add the sales performance visualization to it.
- Add other relevant visualizations or filters to the dashboard to provide a comprehensive view of sales performance.
- Arrange and resize the visualizations on the dashboard to create an intuitive and visually appealing layout.
- Save and publish the dashboard for others to view and interact with.
Become A Data Superstar π€©ππ» https://ds.cloudyml.com/data-superstar
Company name: Twitter
Role: Data Scientist
Company: Twitter
Topic: bias variance, neural network, tableau, SQL missing values
1. What is bias-variance tradeoff in machine learning?
The goal in machine learning is to strike a balance between bias and variance to achieve good generalization performance. Ideally, we want a model that can capture the underlying patterns and complexity of the data while avoiding overfitting or underfitting. This balance is known as the bias-variance trade-off. Increasing the complexity of a model, such as using more features or increasing the model's capacity (e.g., adding more layers in a neural network), typically reduces bias and increases variance. Conversely, reducing the complexity of a model, such as using simpler algorithms or reducing the number of features, generally increases bias and reduces variance. To find the optimal trade-off, one can employ various techniques such as cross-validation, regularization, or ensemble methods. Cross-validation helps evaluate the model's performance on unseen data and aids in selecting the appropriate complexity level. Regularization techniques add constraints to the model to reduce overfitting. Ensemble methods combine multiple models to leverage the strengths of each model while mitigating their weaknesses. Understanding the bias-variance trade-off is crucial for selecting the appropriate models, optimizing hyperparameters, and ensuring the model's ability to generalize well to unseen data.
2. What are the differences between shallow neural networks and deep neural networks?
Shallow Neural Networks: Shallow neural networks have a small number of hidden layers (typically one or two). They are relatively simpler and have fewer parameters to learn. Shallow networks are suitable for simpler tasks or datasets with fewer complex patterns. Deep Neural Networks: Deep neural networks have a large number of hidden layers (more than two). They are more complex and have a significantly higher number of parameters. Deep networks can learn intricate patterns and hierarchies of features from data. They are more suitable for complex tasks such as image recognition, natural language processing, and speech recognition.
3. How do you handle missing values in SQL queries?
To handle missing values in SQL queries, you can use the COALESCE or ISNULL function to replace NULL values with a specified default value, or you can use the WHERE clause to filter out rows with missing values using the IS NULL or IS NOT NULL condition.
4. How would you create a dashboard in Tableau to visualize sales performance over time?
To create a dashboard in Tableau to visualize sales performance over time, you would:
Connect to your sales data source.
- Drag a date field to the Columns or Rows shelf to represent time.
- Drag a sales measure to the Rows or Columns shelf to represent the sales value.
- Choose an appropriate visualization type, such as a line chart or area chart.
- Add any additional dimensions or measures to the visualization for more detailed insights.
- Customize the visual appearance, including axis labels, titles, and formatting.
- Create a new dashboard and add the sales performance visualization to it.
- Add other relevant visualizations or filters to the dashboard to provide a comprehensive view of sales performance.
- Arrange and resize the visualizations on the dashboard to create an intuitive and visually appealing layout.
- Save and publish the dashboard for others to view and interact with.
Become A Data Superstar π€©ππ» https://ds.cloudyml.com/data-superstar
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