Date: 02-02-2023
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 doesn’t have any value, it is said to be having a NULL value. Simply put, NULL is the blank field in a table. It can be considered as an unassigned, unknown, or unavailable value. On the contrary, zero is a number, and it is an available, assigned, and known value.
Company name: Cognizant
Role: SQL Developer
Topic: superkey, buffer pool value, candidate key, null values
1. What are Query and Query language?
A query is nothing but a request sent to a database to retrieve data or information. The required data can be retrieved from a table or many tables in the database.
Query languages use various types of queries to retrieve data from databases. SQL, Datalog, and AQL are a few examples of query languages; however, SQL is known to be the widely used query language.
2. What are Superkey and candidate key?
A super key may be a single or a combination of keys that help to identify a record in a table. Know that Super keys can have one or more attributes, even though all the attributes are not necessary to identify the records.
A candidate key is the subset of Superkey, which can have one or more than one attributes to identify records in a table. Unlike Superkey, all the attributes of the candidate key must be helpful to identify the records.
3. What do you mean by buffer pool and mention its benefits?
A buffer pool in SQL is also known as a buffer cache. All the resources can store their cached data pages in a buffer pool. The size of the buffer pool can be defined during the configuration of an instance of SQL Server.
The following are the benefits of a buffer pool:
Increase in I/O performance
Reduction in I/O latency
Increase in transaction throughput
Increase in reading performance
4. What is the difference between Zero and NULL values in SQL?
When a field in a column doesn’t have any value, it is said to be having a NULL value. Simply put, NULL is the blank field in a table. It can be considered as an unassigned, unknown, or unavailable value. On the contrary, zero is a number, and it is an available, assigned, and known value.
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Date: 03-02-2023
Company name: Mindtree
Role: Database developer
Topic: Checkpoint, relation, schema
1. What is a checkpoint in DBMS?
The Checkpoint is a type of mechanism where all the previous logs are removed from the system and permanently stored in the storage disk. There are two ways which can help the DBMS in recovering and maintaining the ACID properties, and they are- maintaining the log of each transaction and maintaining shadow pages. So, when it comes to log based recovery systems, checkpoints come into existence. Checkpoints are those points to which the database engine can recover after a crash as a specified minimal point from which the transaction log record can be used to recover all the committed data up to the point of the crash.
2. What do you mean by transparent DBMS?
The transparent DBMS is a type of DBMS which keeps its physical structure hidden from users. Physical structure or physical storage structure implies to the memory manager of the DBMS, and it describes how the data is stored on disk.
3. Define a Relation Schema and a Relation.
A Relation Schema is specified as a set of attributes. It is also known as table schema. It defines what the name of the table is. Relation schema is known as the blueprint with the help of which we can explain how the data is organized into tables. This blueprint contains no data.
A relation is specified as a set of tuples. A relation is the set of related attributes with identifying key attributes
4. Explain the functionality of DML Compiler.
The DML Compiler translates DML statements in a query language that the query evaluation engine can understand. A DML Compiler is required because the DML is the family of syntax elements which is very similar to the other programming language which requires compilation. So, it is essential to compile the code in the language which the query evaluation engine can understand and then work on those queries with proper output.
Company name: Mindtree
Role: Database developer
Topic: Checkpoint, relation, schema
1. What is a checkpoint in DBMS?
The Checkpoint is a type of mechanism where all the previous logs are removed from the system and permanently stored in the storage disk. There are two ways which can help the DBMS in recovering and maintaining the ACID properties, and they are- maintaining the log of each transaction and maintaining shadow pages. So, when it comes to log based recovery systems, checkpoints come into existence. Checkpoints are those points to which the database engine can recover after a crash as a specified minimal point from which the transaction log record can be used to recover all the committed data up to the point of the crash.
2. What do you mean by transparent DBMS?
The transparent DBMS is a type of DBMS which keeps its physical structure hidden from users. Physical structure or physical storage structure implies to the memory manager of the DBMS, and it describes how the data is stored on disk.
3. Define a Relation Schema and a Relation.
A Relation Schema is specified as a set of attributes. It is also known as table schema. It defines what the name of the table is. Relation schema is known as the blueprint with the help of which we can explain how the data is organized into tables. This blueprint contains no data.
A relation is specified as a set of tuples. A relation is the set of related attributes with identifying key attributes
4. Explain the functionality of DML Compiler.
The DML Compiler translates DML statements in a query language that the query evaluation engine can understand. A DML Compiler is required because the DML is the family of syntax elements which is very similar to the other programming language which requires compilation. So, it is essential to compile the code in the language which the query evaluation engine can understand and then work on those queries with proper output.
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Date:- 04-02-2023
Company name: Musigma
Role: Data Scientist
Topic : decision tree, loss function, cost function, root cause analysis, array vs list
1. How does a Decision Tree handle continuous(numerical) features?
Ans.
Decision Trees handle continuous features by converting these continuous features to a threshold-based boolean feature.
To decide The threshold value, we use the concept of Information Gain, choosing that threshold that maximizes the information gain.
2. What are Loss Function and Cost Functions?
Ans. the loss function is to capture the difference between the actual and predicted values for a single record whereas cost functions aggregate the difference for the entire training dataset.
The Most commonly used loss functions are Mean-squared error and Hinge loss.
3. What is the difference between Python Arrays and lists?
Ans. Arrays in python can only contain elements of same data types i.e., data type of array should be homogeneous. It is a thin wrapper around C language arrays and consumes far less memory than lists.
Lists in python can contain elements of different data types i.e., data type of lists can be heterogeneous. It has the disadvantage of consuming large memory.
4. What is root cause analysis? What is a causation vs. a correlation?
Ans. Root cause analysis: a method of problem-solving used for identifying the root cause(s) of a problem [5]
Correlation measures the relationship between two variables, range from -1 to 1. Causation is when a first event appears to have caused a second event. Causation essentially looks at direct relationships while correlation can look at both direct and indirect relationships.
Company name: Musigma
Role: Data Scientist
Topic : decision tree, loss function, cost function, root cause analysis, array vs list
1. How does a Decision Tree handle continuous(numerical) features?
Ans.
Decision Trees handle continuous features by converting these continuous features to a threshold-based boolean feature.
To decide The threshold value, we use the concept of Information Gain, choosing that threshold that maximizes the information gain.
2. What are Loss Function and Cost Functions?
Ans. the loss function is to capture the difference between the actual and predicted values for a single record whereas cost functions aggregate the difference for the entire training dataset.
The Most commonly used loss functions are Mean-squared error and Hinge loss.
3. What is the difference between Python Arrays and lists?
Ans. Arrays in python can only contain elements of same data types i.e., data type of array should be homogeneous. It is a thin wrapper around C language arrays and consumes far less memory than lists.
Lists in python can contain elements of different data types i.e., data type of lists can be heterogeneous. It has the disadvantage of consuming large memory.
4. What is root cause analysis? What is a causation vs. a correlation?
Ans. Root cause analysis: a method of problem-solving used for identifying the root cause(s) of a problem [5]
Correlation measures the relationship between two variables, range from -1 to 1. Causation is when a first event appears to have caused a second event. Causation essentially looks at direct relationships while correlation can look at both direct and indirect relationships.
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❤15🆒8🔥4
Date: 05-02-2023
Company name: Wipro
Role: Data Analyst
Topic: concurrency, checkpoint, groups, nested if
1. What is concurrency control in DBMS?
This is a process of managing simultaneous operations in a database so that database integrity is not compromised. The following are the two approaches involved in concurrency control:
Optimistic approach – Involves versioning
Pessimistic approach – Involves locking
2. What is a checkpoint in DBMS and when does it occur?
A checkpoint is a mechanism where all the previous logs are removed from the system and are permanently stored on the storage disk. So, basically, checkpoints are those points from where the transaction log record can be used to recover all the committed data up to the point of crash.
3. What are groups in Tableau?
A group is a combination of dimension members that make higher level categories. For example, if you are working with a view that shows average test scores by major, you may want to group certain majors together to create major categories.
4. How are nested IF statements used in Excel?
The function IF() can be nested when we have multiple conditions to meet. The FALSE value in the first IF function is replaced by another IF function to make a further test.
Company name: Wipro
Role: Data Analyst
Topic: concurrency, checkpoint, groups, nested if
1. What is concurrency control in DBMS?
This is a process of managing simultaneous operations in a database so that database integrity is not compromised. The following are the two approaches involved in concurrency control:
Optimistic approach – Involves versioning
Pessimistic approach – Involves locking
2. What is a checkpoint in DBMS and when does it occur?
A checkpoint is a mechanism where all the previous logs are removed from the system and are permanently stored on the storage disk. So, basically, checkpoints are those points from where the transaction log record can be used to recover all the committed data up to the point of crash.
3. What are groups in Tableau?
A group is a combination of dimension members that make higher level categories. For example, if you are working with a view that shows average test scores by major, you may want to group certain majors together to create major categories.
4. How are nested IF statements used in Excel?
The function IF() can be nested when we have multiple conditions to meet. The FALSE value in the first IF function is replaced by another IF function to make a further test.
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Date: 06-02-2023
Company name: Swiggy
Role: ML Engineer
Topic: ensemble, kernel, regularisation
1. Explain the difference between L1 and L2 regularization.
Answer: L2 regularization tends to spread error among all the terms, while L1 is more binary/sparse, with many variables either being assigned a 1 or 0 in weighting. L1 corresponds to setting a Laplacean prior on the terms, while L2 corresponds to a Gaussian prior.
2. What is deep learning, and how does it contrast with other machine learning algorithms?
Answer: Deep learning is a subset of machine learning that is concerned with neural networks: how to use backpropagation and certain principles from neuroscience to more accurately model large sets of unlabelled or semi-structured data. In that sense, deep learning represents an unsupervised learning algorithm that learns representations of data through the use of neural nets.
3. Name an example where ensemble techniques might be useful.
Answer: 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.
4. What’s the “kernel trick” and how is it useful?
Answer: The Kernel trick involves kernel functions that can enable in higher-dimension spaces without explicitly calculating the coordinates of points within that dimension: instead, kernel functions compute the inner products between the images of all pairs of data in a feature space. This allows them the very useful attribute of calculating the coordinates of higher dimensions while being computationally cheaper than the explicit calculation of said coordinates.
Company name: Swiggy
Role: ML Engineer
Topic: ensemble, kernel, regularisation
1. Explain the difference between L1 and L2 regularization.
Answer: L2 regularization tends to spread error among all the terms, while L1 is more binary/sparse, with many variables either being assigned a 1 or 0 in weighting. L1 corresponds to setting a Laplacean prior on the terms, while L2 corresponds to a Gaussian prior.
2. What is deep learning, and how does it contrast with other machine learning algorithms?
Answer: Deep learning is a subset of machine learning that is concerned with neural networks: how to use backpropagation and certain principles from neuroscience to more accurately model large sets of unlabelled or semi-structured data. In that sense, deep learning represents an unsupervised learning algorithm that learns representations of data through the use of neural nets.
3. Name an example where ensemble techniques might be useful.
Answer: 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.
4. What’s the “kernel trick” and how is it useful?
Answer: The Kernel trick involves kernel functions that can enable in higher-dimension spaces without explicitly calculating the coordinates of points within that dimension: instead, kernel functions compute the inner products between the images of all pairs of data in a feature space. This allows them the very useful attribute of calculating the coordinates of higher dimensions while being computationally cheaper than the explicit calculation of said coordinates.
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🆒10
Date: 07-02-2023
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.
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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🆒15👏8🤩5❤4🙏3
Date: 08-02-2023
Company name: Numerator
Role: Data Analyst
Topic: lambda, filters, SQL vs mysql
1. What is the lambda function in Python?
Python Lambda Functions are anonymous function means that the function is without a name. As we already know that the def keyword is used to define a normal function in Python. Similarly, the lambda keyword is used to define an anonymous function in Python.
Eg. lambda_cube = lambda y: y*y*y
2. What is the difference between SQL and MySQL?
SQL is a query programming language that manages RDBMS. MySQL is a relational database management system that uses SQL. SQL is primarily used to query and operate database systems. MySQL allows you to handle, store, modify and delete data and store data in an organized way.
3. What are Filters in Power BI?
The term "Filter" is self-explanatory. Filters are mathematical and logical conditions applied to data to filter out essential information in rows and columns. The following are the variety of filters available in Power BI:
Manual filters
Auto filters
Include/Exclude filters
Drill-down filters
Cross Drill filters
Company name: Numerator
Role: Data Analyst
Topic: lambda, filters, SQL vs mysql
1. What is the lambda function in Python?
Python Lambda Functions are anonymous function means that the function is without a name. As we already know that the def keyword is used to define a normal function in Python. Similarly, the lambda keyword is used to define an anonymous function in Python.
Eg. lambda_cube = lambda y: y*y*y
2. What is the difference between SQL and MySQL?
SQL is a query programming language that manages RDBMS. MySQL is a relational database management system that uses SQL. SQL is primarily used to query and operate database systems. MySQL allows you to handle, store, modify and delete data and store data in an organized way.
3. What are Filters in Power BI?
The term "Filter" is self-explanatory. Filters are mathematical and logical conditions applied to data to filter out essential information in rows and columns. The following are the variety of filters available in Power BI:
Manual filters
Auto filters
Include/Exclude filters
Drill-down filters
Cross Drill filters
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🆒32🙏6🤩2
Date: 09-02-2023
Company name: Kenstar
Role: Data Scientist
Topic: auto encoders, kmeans, powerbi, recursive stored procedure
1. What are autoencoders?
Autoencoders are artificial neural networks that learn without any supervision. Here, these networks have the ability to automatically learn by mapping the inputs to the corresponding outputs.
Autoencoders, as the name suggests, consist of two entities:
Encoder: Used to fit the input into an internal computation state
Decoder: Used to convert the computational state back into the output
2. Compare K-means and KNN Algorithms.
K-Means is unsupervised. K-Means is a clustering algorithm. The points in each cluster are similar to each other, and each cluster is different from its neighboring clusters. KNN is supervised in nature. KNN is a classification algorithm It classifies an unlabeled observation based on its K (can be any number) surrounding neighbors.
3. What is a Recursive Stored Procedure in SQL?
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. Some SQL programming languages limit the recursion depth to prevent an infinite loop of procedure calls from causing a stack overflow, which slows down the system and may lead to system crashes.
4. SUM() vs SUMX(): What is the difference between the two DAX functions in Power BI?
The sum function (Sum()) takes the data columns and aggregates them totally but the SumX function (SumX()) lets you filter the data which you are adding.
SUMX(Table, Expression), where the table contains the rows for calculation. Expression is a calculation that will be evaluated on each row of the table.
Company name: Kenstar
Role: Data Scientist
Topic: auto encoders, kmeans, powerbi, recursive stored procedure
1. What are autoencoders?
Autoencoders are artificial neural networks that learn without any supervision. Here, these networks have the ability to automatically learn by mapping the inputs to the corresponding outputs.
Autoencoders, as the name suggests, consist of two entities:
Encoder: Used to fit the input into an internal computation state
Decoder: Used to convert the computational state back into the output
2. Compare K-means and KNN Algorithms.
K-Means is unsupervised. K-Means is a clustering algorithm. The points in each cluster are similar to each other, and each cluster is different from its neighboring clusters. KNN is supervised in nature. KNN is a classification algorithm It classifies an unlabeled observation based on its K (can be any number) surrounding neighbors.
3. What is a Recursive Stored Procedure in SQL?
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. Some SQL programming languages limit the recursion depth to prevent an infinite loop of procedure calls from causing a stack overflow, which slows down the system and may lead to system crashes.
4. SUM() vs SUMX(): What is the difference between the two DAX functions in Power BI?
The sum function (Sum()) takes the data columns and aggregates them totally but the SumX function (SumX()) lets you filter the data which you are adding.
SUMX(Table, Expression), where the table contains the rows for calculation. Expression is a calculation that will be evaluated on each row of the table.
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🤩24🔥4🙏4🎉3❤2
Date: 10-02-2023
Company name: TCS
Role: ML Engineer
Topic: KNN, Logistic Regression, RNN, LSTM
1. What is the Impact of Outliers on Logistic Regression?
The estimates of the Logistic Regression are sensitive to unusual observations such as outliers, high leverage, and influential observations. Therefore, to solve the problem of outliers, a sigmoid function is used in Logistic Regression.
2. What is the difference between vanilla RNNs and LSTMs?
The main difference between vanilla RNNs and LSTMs is that LSTMs are able to better remember long-term dependencies, while vanilla RNNs tend to forget them. This is due to the fact that LSTMs have a special type of memory cell that can retain information for longer periods of time, while vanilla RNNs only have a single layer of memory cells.
3. What is Masked Language Model in NLP?
Masked language models help learners to understand deep representations in downstream tasks by taking an output from the corrupt input. This model is often used to predict the words to be used in a sentence.
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.
Company name: TCS
Role: ML Engineer
Topic: KNN, Logistic Regression, RNN, LSTM
1. What is the Impact of Outliers on Logistic Regression?
The estimates of the Logistic Regression are sensitive to unusual observations such as outliers, high leverage, and influential observations. Therefore, to solve the problem of outliers, a sigmoid function is used in Logistic Regression.
2. What is the difference between vanilla RNNs and LSTMs?
The main difference between vanilla RNNs and LSTMs is that LSTMs are able to better remember long-term dependencies, while vanilla RNNs tend to forget them. This is due to the fact that LSTMs have a special type of memory cell that can retain information for longer periods of time, while vanilla RNNs only have a single layer of memory cells.
3. What is Masked Language Model in NLP?
Masked language models help learners to understand deep representations in downstream tasks by taking an output from the corrupt input. This model is often used to predict the words to be used in a sentence.
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.
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Date: 11-02-2023
Company name: Blinkit
Role: SQL Developer
Topic : trigger, order by, aggregate function
1. Does SQL support programming language features?
It is true that SQL is a language, but it does not support programming as it is not a programming language, it is a command language. We do not have conditional statements in SQL like for loops or if..else, we only have commands which we can use to query, update, delete, etc. data in the database. SQL allows us to manipulate data in a database.
2. What is a trigger?
Trigger is a statement that a system executes automatically when there is any modification to the database. In a trigger, we first specify when the trigger is to be executed and then the action to be performed when the trigger executes. Triggers are used to specify certain integrity constraints and referential constraints that cannot be specified using the constraint mechanism of SQL.
3. What are aggregate and scalar functions?
For doing operations on data SQL has many built-in functions, they are categorized into two categories and further sub-categorized into seven different functions under each category. The categories are:
Aggregate functions:
These functions are used to do operations from the values of the column and a single value is returned.
Scalar functions:
These functions are based on user input, these too return a single value.
4. Define SQL Order by the statement?
The ORDER BY statement in SQL is used to sort the fetched data in either ascending or descending according to one or more columns.
By default ORDER BY sorts the data in ascending order.
We can use the keyword DESC to sort the data in descending order and the keyword ASC to sort in ascending order.
Company name: Blinkit
Role: SQL Developer
Topic : trigger, order by, aggregate function
1. Does SQL support programming language features?
It is true that SQL is a language, but it does not support programming as it is not a programming language, it is a command language. We do not have conditional statements in SQL like for loops or if..else, we only have commands which we can use to query, update, delete, etc. data in the database. SQL allows us to manipulate data in a database.
2. What is a trigger?
Trigger is a statement that a system executes automatically when there is any modification to the database. In a trigger, we first specify when the trigger is to be executed and then the action to be performed when the trigger executes. Triggers are used to specify certain integrity constraints and referential constraints that cannot be specified using the constraint mechanism of SQL.
3. What are aggregate and scalar functions?
For doing operations on data SQL has many built-in functions, they are categorized into two categories and further sub-categorized into seven different functions under each category. The categories are:
Aggregate functions:
These functions are used to do operations from the values of the column and a single value is returned.
Scalar functions:
These functions are based on user input, these too return a single value.
4. Define SQL Order by the statement?
The ORDER BY statement in SQL is used to sort the fetched data in either ascending or descending according to one or more columns.
By default ORDER BY sorts the data in ascending order.
We can use the keyword DESC to sort the data in descending order and the keyword ASC to sort in ascending order.
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V6 - Piechart , Donut chart | Complete PowerBI Course (in hindi)
If you wanna make your career in Data Science domain, then check our course now. We provide hands-on practical learning experience to our learners with 1-1 Live Doubt Clearance Support over Chat everyday and also you will learn Job Hunting Hacks. You will…
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Date: 14-02-2023
Company name : Walmart
Role: ML Engineer
Topic: cross validation, market basket, bagging boosting , sampling
1. What is Cross-validation in Machine Learning?
Cross-validation allows a system to increase the performance of the given Machine Learning algorithm. This sampling process is done to break the dataset into smaller parts that have the same number of rows, out of which a random part is selected as a test set and the rest of the parts are kept as train sets. Cross-validation consists of the following techniques:
• Holdout method
• K-fold cross-validation
• Stratified k-fold cross-validation
• Leave p-out cross-validation
2. What is bagging and boosting in Machine Learning?
Bagging is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average.
Boosting is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm.
3.What is systematic sampling and cluster sampling ?
Systematic sampling is a type of probability sampling method. The sample members are selected from a larger population with a random starting point but a fixed periodic interval. This interval is known as the sampling interval. The sampling interval is calculated by dividing the population size by the desired sample size.
Cluster sampling involves dividing the sample population into separate groups, called clusters. Then, a simple random sample of clusters is selected from the population. Analysis is conducted on data from the sampled clusters.
4.What is market basket analysis?
Market Basket Analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.
Company name : Walmart
Role: ML Engineer
Topic: cross validation, market basket, bagging boosting , sampling
1. What is Cross-validation in Machine Learning?
Cross-validation allows a system to increase the performance of the given Machine Learning algorithm. This sampling process is done to break the dataset into smaller parts that have the same number of rows, out of which a random part is selected as a test set and the rest of the parts are kept as train sets. Cross-validation consists of the following techniques:
• Holdout method
• K-fold cross-validation
• Stratified k-fold cross-validation
• Leave p-out cross-validation
2. What is bagging and boosting in Machine Learning?
Bagging is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average.
Boosting is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm.
3.What is systematic sampling and cluster sampling ?
Systematic sampling is a type of probability sampling method. The sample members are selected from a larger population with a random starting point but a fixed periodic interval. This interval is known as the sampling interval. The sampling interval is calculated by dividing the population size by the desired sample size.
Cluster sampling involves dividing the sample population into separate groups, called clusters. Then, a simple random sample of clusters is selected from the population. Analysis is conducted on data from the sampled clusters.
4.What is market basket analysis?
Market Basket Analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.
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Date: 15-02-2023
Company name: MuSigma
Role: Data Analyst
Topic: Gantt chart, macro, Powerpivot, Fourier transform
1. What’s a Fourier transform?
A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. The Fourier transform finds the set of cycle speeds, amplitudes, and phases to match any time signal. A Fourier transform converts a signal from time to frequency domain—it’s a very common way to extract features from audio signals or other time series such as sensor data.
2. What is x-velocity in Power Pivot?
X-Velocity is the in-memory analytics engine behind Power Pivot that loads and handles huge data in Power BI. It stores data in columnar storage that results in faster processing.
3. Gantt chart in Tableau ?
A Tableau Gantt chart illustrates the duration of events as well as the progression of value across the period. Along with the time axis, it has bars. The Gantt chart is primarily used as a project management tool, with each bar representing a project job.
4. What in Excel is a macro?
An Excel macro is an algorithm or a group of steps that helps automate an operation by capturing and replaying the steps needed to finish it. Once the steps have been saved, you may construct a Macro that the user can alter and replay as often as they like.
Company name: MuSigma
Role: Data Analyst
Topic: Gantt chart, macro, Powerpivot, Fourier transform
1. What’s a Fourier transform?
A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. The Fourier transform finds the set of cycle speeds, amplitudes, and phases to match any time signal. A Fourier transform converts a signal from time to frequency domain—it’s a very common way to extract features from audio signals or other time series such as sensor data.
2. What is x-velocity in Power Pivot?
X-Velocity is the in-memory analytics engine behind Power Pivot that loads and handles huge data in Power BI. It stores data in columnar storage that results in faster processing.
3. Gantt chart in Tableau ?
A Tableau Gantt chart illustrates the duration of events as well as the progression of value across the period. Along with the time axis, it has bars. The Gantt chart is primarily used as a project management tool, with each bar representing a project job.
4. What in Excel is a macro?
An Excel macro is an algorithm or a group of steps that helps automate an operation by capturing and replaying the steps needed to finish it. Once the steps have been saved, you may construct a Macro that the user can alter and replay as often as they like.
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Happy Learning 💙