Date - 13-01-2023
Company name: Walmart
Role: Data Scientist
Topic: kmean, cnn layers, constraint in SQL, tableau
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. Explain the different layers in CNN.
The different layers involved in the architecture of CNN are as follows:
1. Input Layer: The input layer in CNN should contain image data. Image data is represented by a three-dimensional matrix. We have to reshape the image into a single column.
For Example, Suppose we have an MNIST dataset and you have an image of dimension 28 x 28 =784, you need to convert it into 784 x 1 before feeding it into the input. If we have “k” training examples in the dataset, then the dimension of input will be (784, k).
2. Convolutional Layer: To perform the convolution operation, this layer is used which creates several smaller picture windows to go over the data.
3. ReLU Layer: This layer introduces the non-linearity to the network and converts all the negative pixels to zero. The final output is a rectified feature map.
3. What Would You Do If Some Countries/Provinces (Any Geographical Entity) are Missing and Displaying a Null When You Use Map View in Tableau?
When working with maps and geographical fields, unknown or ambiguous locations are identified by the indicator in the lower right corner of the view.
Click the indicator and choose from the following options:
Edit Locations - correct the locations by mapping your data to known locations
Filter Data - exclude the unknown locations from the view using a filter. The locations will not be included in calculations
Show Data at Default Position - show the values at the default position of (0, 0) on the map.
4. What are Constraints in SQL?
Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command. The constraints are:
NOT NULL - Restricts NULL value from being inserted into a column.
CHECK - Verifies that all values in a field satisfy a condition.
DEFAULT - Automatically assigns a default value if no value has been specified for the field.
UNIQUE - Ensures unique values to be inserted into the field.
INDEX - Indexes a field providing faster retrieval of records.
PRIMARY KEY - Uniquely identifies each record in a table.
FOREIGN KEY - Ensures referential integrity for a record in another table.
Company name: Walmart
Role: Data Scientist
Topic: kmean, cnn layers, constraint in SQL, tableau
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. Explain the different layers in CNN.
The different layers involved in the architecture of CNN are as follows:
1. Input Layer: The input layer in CNN should contain image data. Image data is represented by a three-dimensional matrix. We have to reshape the image into a single column.
For Example, Suppose we have an MNIST dataset and you have an image of dimension 28 x 28 =784, you need to convert it into 784 x 1 before feeding it into the input. If we have “k” training examples in the dataset, then the dimension of input will be (784, k).
2. Convolutional Layer: To perform the convolution operation, this layer is used which creates several smaller picture windows to go over the data.
3. ReLU Layer: This layer introduces the non-linearity to the network and converts all the negative pixels to zero. The final output is a rectified feature map.
3. What Would You Do If Some Countries/Provinces (Any Geographical Entity) are Missing and Displaying a Null When You Use Map View in Tableau?
When working with maps and geographical fields, unknown or ambiguous locations are identified by the indicator in the lower right corner of the view.
Click the indicator and choose from the following options:
Edit Locations - correct the locations by mapping your data to known locations
Filter Data - exclude the unknown locations from the view using a filter. The locations will not be included in calculations
Show Data at Default Position - show the values at the default position of (0, 0) on the map.
4. What are Constraints in SQL?
Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command. The constraints are:
NOT NULL - Restricts NULL value from being inserted into a column.
CHECK - Verifies that all values in a field satisfy a condition.
DEFAULT - Automatically assigns a default value if no value has been specified for the field.
UNIQUE - Ensures unique values to be inserted into the field.
INDEX - Indexes a field providing faster retrieval of records.
PRIMARY KEY - Uniquely identifies each record in a table.
FOREIGN KEY - Ensures referential integrity for a record in another table.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Stay Safe & Happy Learning💙
❤9👏5
🔥47🆒35
What will be the output of this Python code?
Anonymous Quiz
13%
27 81 343
15%
6 9 12
60%
9 27 81
12%
None
🔥43👏7🎉3
Date: 14-01-2023
Company name: Adecco
Role: SQL Developer
Topic: case, cursors, aggregate function , update statement
1. What is the case when statement in SQL?
The CASE expression goes through conditions and returns a value when the first condition is met (like an if-then-else statement). So, once a condition is true, it will stop reading and return the result. If no conditions are true, it returns the value in the ELSE clause. If there is no ELSE part and no conditions are true, it returns NULL.
2. Explain the update statement in SQL.
The SQL UPDATE Query is used to modify the existing records in a table. You can use the WHERE clause with the UPDATE query to update the selected rows, otherwise all the rows would be affected.
Syntax
The basic syntax of the UPDATE query with a WHERE clause is as follows −
UPDATE table_name
SET column1 = value1, column2 = value2...., columnN = valueN
WHERE [condition];
3. What are the two types of cursors?
There are 2 types of Cursors: Implicit Cursors, and Explicit Cursors. These are explained as following below.
Implicit Cursors:
Implicit Cursors are also known as Default Cursors of SQL SERVER. These Cursors are allocated by SQL SERVER when the user performs DML operations.
Explicit Cursors :
Explicit Cursors are Created by Users whenever the user requires them. Explicit Cursors are used for Fetching data from Table in Row-By-Row Manner.
4. What are the aggregate functions in SQL?
In database management an aggregate function is a function where the values of multiple rows are grouped together as input on certain criteria to form a single value of more significant meaning.
Various Aggregate Functions
1) Count()
2) Sum()
3) Avg()
4) Min()
5) Max()
Company name: Adecco
Role: SQL Developer
Topic: case, cursors, aggregate function , update statement
1. What is the case when statement in SQL?
The CASE expression goes through conditions and returns a value when the first condition is met (like an if-then-else statement). So, once a condition is true, it will stop reading and return the result. If no conditions are true, it returns the value in the ELSE clause. If there is no ELSE part and no conditions are true, it returns NULL.
2. Explain the update statement in SQL.
The SQL UPDATE Query is used to modify the existing records in a table. You can use the WHERE clause with the UPDATE query to update the selected rows, otherwise all the rows would be affected.
Syntax
The basic syntax of the UPDATE query with a WHERE clause is as follows −
UPDATE table_name
SET column1 = value1, column2 = value2...., columnN = valueN
WHERE [condition];
3. What are the two types of cursors?
There are 2 types of Cursors: Implicit Cursors, and Explicit Cursors. These are explained as following below.
Implicit Cursors:
Implicit Cursors are also known as Default Cursors of SQL SERVER. These Cursors are allocated by SQL SERVER when the user performs DML operations.
Explicit Cursors :
Explicit Cursors are Created by Users whenever the user requires them. Explicit Cursors are used for Fetching data from Table in Row-By-Row Manner.
4. What are the aggregate functions in SQL?
In database management an aggregate function is a function where the values of multiple rows are grouped together as input on certain criteria to form a single value of more significant meaning.
Various Aggregate Functions
1) Count()
2) Sum()
3) Avg()
4) Min()
5) Max()
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Stay Safe & Happy Learning💙
🔥7
Do you wanna meet Subhayan, who switched his career to Data Analytics from Electronics Background.
Know about his journey from our Latest Podcast Episode on YouTube. Link given below
Video Link - https://youtu.be/0SorXvpRZho
Know about his journey from our Latest Podcast Episode on YouTube. Link given below
Video Link - https://youtu.be/0SorXvpRZho
🙏2❤1
Date: 15-01-2023
Company name: Swiggy
Role: ML Engineer
Topic: softmax vs signmoid, kmeans vs knn,
Random forest, PCA, Lda
1. What's the difference between Softmax and Sigmoid functions?
Softmax function:
Is used for multi-class classification in logistic regression models, when we have only one right answer or mutually exclusive outputs.
Its probabilities sum will be 1.
Is used in different layers of neural networks.
Sigmoid function:
Is used for multi-label classification in logistic regression models, when we have more than one right answer or non-exclusive outputs.
Its probabilities sum does not need to be 1.
Is used as an activation function while building neural networks.
2. What is the main difference between k-Means and k-Nearest Neighbours?
k-Means is a clustering algorithm that tries to partition a set of points into k sets such that the points in each cluster tend to be near each other. It is unsupervised because the points have no external classification.
k-Nearest Neighbors is a classification (or regression) algorithm that, in order to determine the classification of a point, combines the classification of the k nearest points. It is supervised because it is trying to classify a point based on the known classification of other points.
3. How would you improve the performance of Random Forest?
Some things to try to improve the performance of Random Forest are:
Using a higher quality dataset and feature engineering. Using too many features and data are not good for the model so sometimes it is important to perform some feature reduction too.
Tuning the hyperparameters of the algorithm.
Trying different algorithms.
4. What is the difference between PCA and LDA?
Unlike Linear Discriminant Analysis (LDA), Primary Component Analysis (PCA) captures more variation between the data points but it does not separate them very well.
PCA is a linear combination that accounts for as much variability as possible. For LDA, it maximizes the separation between two or more groups i.e it tries to increase the distance between two or more groups.
5. Do you wanna make your career in Data Science & Analytics but don't know how to start ?
Get a complete roadmap from scratch that will make you technically strong enough to crack any Data Scientist / Analyst and also learn Pro Career Growth Hacks to land on your Dream Job.
Check India's Most Affordable Industry Oriented Course with Job Guarantee, chosen & trusted by 12000+ happy learners. Link Below👇🏻
➡️https://www.cloudyml.com/all-in-one-super-combo/
Happy Learning 💙
Company name: Swiggy
Role: ML Engineer
Topic: softmax vs signmoid, kmeans vs knn,
Random forest, PCA, Lda
1. What's the difference between Softmax and Sigmoid functions?
Softmax function:
Is used for multi-class classification in logistic regression models, when we have only one right answer or mutually exclusive outputs.
Its probabilities sum will be 1.
Is used in different layers of neural networks.
Sigmoid function:
Is used for multi-label classification in logistic regression models, when we have more than one right answer or non-exclusive outputs.
Its probabilities sum does not need to be 1.
Is used as an activation function while building neural networks.
2. What is the main difference between k-Means and k-Nearest Neighbours?
k-Means is a clustering algorithm that tries to partition a set of points into k sets such that the points in each cluster tend to be near each other. It is unsupervised because the points have no external classification.
k-Nearest Neighbors is a classification (or regression) algorithm that, in order to determine the classification of a point, combines the classification of the k nearest points. It is supervised because it is trying to classify a point based on the known classification of other points.
3. How would you improve the performance of Random Forest?
Some things to try to improve the performance of Random Forest are:
Using a higher quality dataset and feature engineering. Using too many features and data are not good for the model so sometimes it is important to perform some feature reduction too.
Tuning the hyperparameters of the algorithm.
Trying different algorithms.
4. What is the difference between PCA and LDA?
Unlike Linear Discriminant Analysis (LDA), Primary Component Analysis (PCA) captures more variation between the data points but it does not separate them very well.
PCA is a linear combination that accounts for as much variability as possible. For LDA, it maximizes the separation between two or more groups i.e it tries to increase the distance between two or more groups.
5. Do you wanna make your career in Data Science & Analytics but don't know how to start ?
Get a complete roadmap from scratch that will make you technically strong enough to crack any Data Scientist / Analyst and also learn Pro Career Growth Hacks to land on your Dream Job.
Check India's Most Affordable Industry Oriented Course with Job Guarantee, chosen & trusted by 12000+ happy learners. Link Below👇🏻
➡️https://www.cloudyml.com/all-in-one-super-combo/
Happy Learning 💙
❤6🎉1
#QUIZ
Suppose there is a list such that: l=[2,3,4]. If we want to print this list in reverse order, which of the following methods should be used?
Suppose there is a list such that: l=[2,3,4]. If we want to print this list in reverse order, which of the following methods should be used?
Anonymous Quiz
41%
reverse(l)
27%
list(reverse[(l)])
15%
reversed(l)
17%
list(reversed(l))
🔥33❤12🤩1
Date: 16-01-2023
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.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Stay Safe & Happy Learning💙
❤14
Wanna test your knowledge in Python🧐 ?
Attempt a Free Python Quiz Test Online by following the link below
➡️ https://courses.cloudyml.com/courses/CloudyML-Python-Quiz-Test-FREE-63c42a7de4b0dee88f4edbca
Also get Bonus Prize 🏆 🤩 by passing this test.
All the best 💙
Attempt a Free Python Quiz Test Online by following the link below
➡️ https://courses.cloudyml.com/courses/CloudyML-Python-Quiz-Test-FREE-63c42a7de4b0dee88f4edbca
Also get Bonus Prize 🏆 🤩 by passing this test.
All the best 💙
🤩20
Date - 17-01-2023
Company name: Cloudera
Role: Tableau developer
Topic: aggregation, filters, data sources, fact dimensions
1. Give an overview of the fact and dimensions of the table?
Facts are numeric measures of data. They are stored in fact tables. Fact tables store that type of data that will be analyzed by dimension tables. Fact tables have foreign keys associating with dimension tables.
Dimensions are descriptive attributes of data. Those will be stored in the dimensions table. For example, customer’s information like name, number, and email will be stored in the dimension table.
2. Explain the limitation of context filters in Tableau?
Whenever we set a context filter, Tableau generates a temp table that needs to refresh each and every time, whenever the view is triggered. So, if the context filter is changed in the database, it needs to recompute the temp table, so the performance will be decreased.
3. What is the difference between published data and embedded data sources?
The published data source contains connection information that is independent of workbooks and can be used by multiple workbooks.
The embedded data source contains connection information but it is associated with the workbooks.
4. Explain the disaggregation and aggregation of data in Tableau?
Aggregation → The process of summarizing the data and viewing a single numeric value is called aggregation. Example – sum/avg of salary for each employee
Disaggregation →The process of viewing each transaction for analyzing all the measures both dependently and independently. Example – individual salary transactions for each employee.
Company name: Cloudera
Role: Tableau developer
Topic: aggregation, filters, data sources, fact dimensions
1. Give an overview of the fact and dimensions of the table?
Facts are numeric measures of data. They are stored in fact tables. Fact tables store that type of data that will be analyzed by dimension tables. Fact tables have foreign keys associating with dimension tables.
Dimensions are descriptive attributes of data. Those will be stored in the dimensions table. For example, customer’s information like name, number, and email will be stored in the dimension table.
2. Explain the limitation of context filters in Tableau?
Whenever we set a context filter, Tableau generates a temp table that needs to refresh each and every time, whenever the view is triggered. So, if the context filter is changed in the database, it needs to recompute the temp table, so the performance will be decreased.
3. What is the difference between published data and embedded data sources?
The published data source contains connection information that is independent of workbooks and can be used by multiple workbooks.
The embedded data source contains connection information but it is associated with the workbooks.
4. Explain the disaggregation and aggregation of data in Tableau?
Aggregation → The process of summarizing the data and viewing a single numeric value is called aggregation. Example – sum/avg of salary for each employee
Disaggregation →The process of viewing each transaction for analyzing all the measures both dependently and independently. Example – individual salary transactions for each employee.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Stay Safe & Happy Learning💙
❤4
Date: 18-01-2023
Company name: IBM
Role: Data Analyst
Topic: Join, OLTP, Joining vs blending, worksheet excel
1. What is a Self-Join?
A self-join is a type of join that can be used to connect two tables. As a result, it is a unary relationship. Each row of the table is attached to itself and all other rows of the same table in a self-join. As a result, a self-join is mostly used to combine and compare rows from the same database table.
2. What is OLTP?
OLTP, or online transactional processing, allows huge groups of people to execute massive amounts of database transactions in real time, usually via the internet. A database transaction occurs when data in a database is changed, inserted, deleted, or queried.
3. What is the difference between joining and blending in Tableau?
Joining term is used when you are combining data from the same source, for example, worksheet in an Excel file or tables in Oracle databaseWhile blending requires two completely defined data sources in your report.
4. How to prevent someone from copying the cell from your worksheet in excel?
If you want to protect your worksheet from being copied, go into Menu bar > Review > Protect sheet > Password.
By entering password you can prevent your worksheet from getting copied.
Company name: IBM
Role: Data Analyst
Topic: Join, OLTP, Joining vs blending, worksheet excel
1. What is a Self-Join?
A self-join is a type of join that can be used to connect two tables. As a result, it is a unary relationship. Each row of the table is attached to itself and all other rows of the same table in a self-join. As a result, a self-join is mostly used to combine and compare rows from the same database table.
2. What is OLTP?
OLTP, or online transactional processing, allows huge groups of people to execute massive amounts of database transactions in real time, usually via the internet. A database transaction occurs when data in a database is changed, inserted, deleted, or queried.
3. What is the difference between joining and blending in Tableau?
Joining term is used when you are combining data from the same source, for example, worksheet in an Excel file or tables in Oracle databaseWhile blending requires two completely defined data sources in your report.
4. How to prevent someone from copying the cell from your worksheet in excel?
If you want to protect your worksheet from being copied, go into Menu bar > Review > Protect sheet > Password.
By entering password you can prevent your worksheet from getting copied.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Stay Safe & Happy Learning💙
❤6🔥1
Date - 19-01-2023
Company name: LTI
Role: ML Engineer
Topic: cart, cross validation, knn, pandas data structures
1. 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.
2. Why is KNN a non-parametric Algorithm?
The term “non-parametric” refers to not making any assumptions on the underlying data distribution. These methods do not have any fixed numbers of parameters in the model.
Similarly in KNN, the model parameters grow with the training data by considering each training case as a parameter of the model. So, KNN is a non-parametric algorithm.
3. Explain the CART Algorithm for Decision Trees.
CART is a variation of the decision tree algorithm. It can handle both classification and regression tasks.The CART stands for Classification and Regression Trees is a greedy algorithm that greedily searches for an optimum split at the top level, then repeats the same process at each of the subsequent levels. Moreover, it does verify whether the split will lead to the lowest impurity or not as well as the solution provided by the greedy algorithm is not guaranteed to be optimal, it often produces a solution that’s reasonably good since finding the optimal Tree is an NP-Complete problem that requires exponential time complexity.
4. Explain leave-p-out cross validation.
When using this exhaustive method, we take p number of points out from the total number of data points in the dataset(say n). While training the model we train it on these (n – p) data points and test the model on p data points. We repeat this process for all the possible combinations of p from the original dataset. Then to get the final accuracy, we average the accuracies from all these iterations.
Stay Safe & Happy Learning 💙
Company name: LTI
Role: ML Engineer
Topic: cart, cross validation, knn, pandas data structures
1. 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.
2. Why is KNN a non-parametric Algorithm?
The term “non-parametric” refers to not making any assumptions on the underlying data distribution. These methods do not have any fixed numbers of parameters in the model.
Similarly in KNN, the model parameters grow with the training data by considering each training case as a parameter of the model. So, KNN is a non-parametric algorithm.
3. Explain the CART Algorithm for Decision Trees.
CART is a variation of the decision tree algorithm. It can handle both classification and regression tasks.The CART stands for Classification and Regression Trees is a greedy algorithm that greedily searches for an optimum split at the top level, then repeats the same process at each of the subsequent levels. Moreover, it does verify whether the split will lead to the lowest impurity or not as well as the solution provided by the greedy algorithm is not guaranteed to be optimal, it often produces a solution that’s reasonably good since finding the optimal Tree is an NP-Complete problem that requires exponential time complexity.
4. Explain leave-p-out cross validation.
When using this exhaustive method, we take p number of points out from the total number of data points in the dataset(say n). While training the model we train it on these (n – p) data points and test the model on p data points. We repeat this process for all the possible combinations of p from the original dataset. Then to get the final accuracy, we average the accuracies from all these iterations.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.Stay Safe & Happy Learning 💙
🆒19
Date - 20-01-2023
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?
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. 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.
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?
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. 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.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.
Stay Safe & Happy Learning 💙🆒4🙏1
🚨LATEST JOB OPENING ALERT🚨
Company: AKS Pro talent
Role: Data analyst
Exp: 0-2 years
Link: APPLY NOW
Company: Escalent
Role: Data Scientist
Exp: 3+ years
Link: APPLY NOW
Company: Neoris
Role: Data scientist Consultant
Exp: 2-4 years
Link: APPLY NOW
Company: cognizant
Role: Data Scientist
Exp: 2+years
Link: APPLY NOW
Company: Ford motor company
Role: Machine Learning Engineer
Exp: 2+ years
Link: APPLY NOW
Company: TransOrg
Role: Associate Data Scientist
Exp: Fresher
Link: APPLY NOW
Company: Boring brand
Role: Data analyst
Exp: Internship
Link: APPLY NOW
ALL THE BEST💙
Company: AKS Pro talent
Role: Data analyst
Exp: 0-2 years
Link: APPLY NOW
Company: Escalent
Role: Data Scientist
Exp: 3+ years
Link: APPLY NOW
Company: Neoris
Role: Data scientist Consultant
Exp: 2-4 years
Link: APPLY NOW
Company: cognizant
Role: Data Scientist
Exp: 2+years
Link: APPLY NOW
Company: Ford motor company
Role: Machine Learning Engineer
Exp: 2+ years
Link: APPLY NOW
Company: TransOrg
Role: Associate Data Scientist
Exp: Fresher
Link: APPLY NOW
Company: Boring brand
Role: Data analyst
Exp: Internship
Link: APPLY NOW
ALL THE BEST💙
❤22🙏1
Check 3 main components to become a skilled Job Searcher ⬇️
https://www.linkedin.com/posts/akashraj001_linkedin-resume-interview-activity-7022189143015186433-fjTQ?utm_source=share&utm_medium=member_desktop
https://www.linkedin.com/posts/akashraj001_linkedin-resume-interview-activity-7022189143015186433-fjTQ?utm_source=share&utm_medium=member_desktop
Linkedin
Akash Raj on LinkedIn: #linkedin #resume #interview #jobhunt #networking
There are 3 main components to become a skilled Job Searcher:
1. Good Network: This comes by doing networking through LinkedIn
2. Good Resume: This needs the…
1. Good Network: This comes by doing networking through LinkedIn
2. Good Resume: This needs the…
🆒6👏1
Date - 21-01-2023
Company name: Elica
Role: ML Engineer
Topic: PCA, SGD, decision tree, ARM
1. What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?
Gradient Descent and Stochastic Gradient Descent are the algorithms that find the set of parameters that will minimize a loss function.
The difference is that in Gradient Descent, all training samples are evaluated for each set of parameters. While in Stochastic Gradient Descent only one training sample is evaluated for the set of parameters identified.
2. Can you mention some advantages and disadvantages of decision trees?
The advantages of decision trees are that they are easier to interpret, are nonparametric and hence robust to outliers, and have relatively few parameters to tune.
On the other hand, the disadvantage is that they are prone to overfitting.
3. What do you mean by Associative Rule Mining (ARM)?
Associative Rule Mining is one of the techniques to discover patterns in data like features (dimensions) which occur together and features (dimensions) which are correlated. It is mostly used in Market-based Analysis to find how frequently an itemset occurs in a transaction. Association rules have to satisfy minimum support and minimum confidence at the very same time.
4. What is the Principle Component Analysis?
The idea here is to reduce the dimensionality of the data set by reducing the number of variables that are correlated with each other. Although the variation needs to be retained to the maximum extent.
The variables are transformed into a new set of variables that are known as Principal Components’. These PCs are the eigenvectors of a covariance matrix and therefore are orthogonal.
Company name: Elica
Role: ML Engineer
Topic: PCA, SGD, decision tree, ARM
1. What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?
Gradient Descent and Stochastic Gradient Descent are the algorithms that find the set of parameters that will minimize a loss function.
The difference is that in Gradient Descent, all training samples are evaluated for each set of parameters. While in Stochastic Gradient Descent only one training sample is evaluated for the set of parameters identified.
2. Can you mention some advantages and disadvantages of decision trees?
The advantages of decision trees are that they are easier to interpret, are nonparametric and hence robust to outliers, and have relatively few parameters to tune.
On the other hand, the disadvantage is that they are prone to overfitting.
3. What do you mean by Associative Rule Mining (ARM)?
Associative Rule Mining is one of the techniques to discover patterns in data like features (dimensions) which occur together and features (dimensions) which are correlated. It is mostly used in Market-based Analysis to find how frequently an itemset occurs in a transaction. Association rules have to satisfy minimum support and minimum confidence at the very same time.
4. What is the Principle Component Analysis?
The idea here is to reduce the dimensionality of the data set by reducing the number of variables that are correlated with each other. Although the variation needs to be retained to the maximum extent.
The variables are transformed into a new set of variables that are known as Principal Components’. These PCs are the eigenvectors of a covariance matrix and therefore are orthogonal.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.✌🏻👏6🆒1
#OUIZ👀
Given a list of numbers, return the second highest number in the list. If there are no second highest number, return None.
——————————————
Option 1: Using the sort() method to sort the list in descending order and then returning the second element in the list.
Option 2: Using the set() method to remove any duplicates from the list, then sorting the list and returning the second last element.
Option 3: Using the set() method to remove any duplicates from the list, then using the max() method with the key argument set to the count of each element in the list to return the second most common element in the list.
Option 4: Using the sorted() method to sort the list in ascending order and then returning the second last element in the list.
Given a list of numbers, return the second highest number in the list. If there are no second highest number, return None.
def second_highest(numbers):
# option 1
# numbers.sort(reverse=True)
# return numbers[1]
# option 2
# return sorted(list(set(numbers)))[-2]
# option 3
# return max(set(numbers), key=numbers.count)
# option 4
# return sorted(numbers)[-2]
numbers = [1, 2, 3, 4, 5, 5, 7]
print(second_highest(numbers))
➡️Output - 5——————————————
Option 1: Using the sort() method to sort the list in descending order and then returning the second element in the list.
Option 2: Using the set() method to remove any duplicates from the list, then sorting the list and returning the second last element.
Option 3: Using the set() method to remove any duplicates from the list, then using the max() method with the key argument set to the count of each element in the list to return the second most common element in the list.
Option 4: Using the sorted() method to sort the list in ascending order and then returning the second last element in the list.
🆒17👏3
🆒32🔥9🤩3🙏2
Date: 22-01-2023
Company name: Elica
Role: Data Scientist
Topic: kmeans, LSTM, SQL, Excel
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. What are some common Data Preparation Operations you would use for Time Series Data?
Parsing time series information from various sources and formats.
Generating sequences of fixed-frequency dates and time spans.
Manipulating and converting date times with time zone information.
Resampling or converting a time series to a particular frequency.
4. Describe the Difference Between Window Functions and Aggregate Functions in SQL.
The main difference between window functions and aggregate functions is that aggregate functions group multiple rows into a single result row; all the individual rows in the group are collapsed and their individual data is not shown. On the other hand, window functions produce a result for each individual row. This result is usually shown as a new column value in every row within the window.
5. What is Ribbon in Excel and where does it appear?
The Ribbon is basically your key interface with Excel and it appears at the top of the Excel window. It allows users to access many of the most important commands directly. It consists of many tabs such as File, Home, View, Insert, etc. You can also customize the ribbon to suit your preferences. To customize the Ribbon, right-click on it and select the “Customize the Ribbon” option.
Company name: Elica
Role: Data Scientist
Topic: kmeans, LSTM, SQL, Excel
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. What are some common Data Preparation Operations you would use for Time Series Data?
Parsing time series information from various sources and formats.
Generating sequences of fixed-frequency dates and time spans.
Manipulating and converting date times with time zone information.
Resampling or converting a time series to a particular frequency.
4. Describe the Difference Between Window Functions and Aggregate Functions in SQL.
The main difference between window functions and aggregate functions is that aggregate functions group multiple rows into a single result row; all the individual rows in the group are collapsed and their individual data is not shown. On the other hand, window functions produce a result for each individual row. This result is usually shown as a new column value in every row within the window.
5. What is Ribbon in Excel and where does it appear?
The Ribbon is basically your key interface with Excel and it appears at the top of the Excel window. It allows users to access many of the most important commands directly. It consists of many tabs such as File, Home, View, Insert, etc. You can also customize the ribbon to suit your preferences. To customize the Ribbon, right-click on it and select the “Customize the Ribbon” option.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain.
🆒1