Date: 30-11-2023
Company: Facebook
Role - Data Scientist
Topics - Machine Learning, Behavioral, Security, Real-time Systems
Question 1 : How would you approach building a recommendation system for personalized content on Facebook? Consider factors like scalability and user privacy.
- Answer: Building a recommendation system for personalized content on Facebook would involve collaborative filtering or content-based methods. Scalability can be achieved using distributed computing, and user privacy can be preserved through techniques like federated learning.
Question 2 : Describe a situation where you had to navigate conflicting opinions within your team. How did you facilitate resolution and maintain team cohesion?
- Answer: In navigating conflicting opinions within a team, I facilitated resolution through open communication, active listening, and finding common ground. Prioritizing team cohesion was key to achieving consensus.
Question 3 : How would you enhance the security of user data on Facebook, considering the evolving landscape of cybersecurity threats?
- Answer: Enhancing the security of user data on Facebook involves implementing robust encryption mechanisms, access controls, and regular security audits. Ensuring compliance with privacy regulations and proactive threat monitoring are essential.
Question 4 : Design a real-time notification system for Facebook, ensuring timely delivery of notifications to users across various platforms.
- Answer: Designing a real-time notification system for Facebook requires technologies like WebSocket for real-time communication and push notifications. Ensuring scalability and reliability through distributed systems is crucial for timely delivery.
Company: Facebook
Role - Data Scientist
Topics - Machine Learning, Behavioral, Security, Real-time Systems
Question 1 : How would you approach building a recommendation system for personalized content on Facebook? Consider factors like scalability and user privacy.
- Answer: Building a recommendation system for personalized content on Facebook would involve collaborative filtering or content-based methods. Scalability can be achieved using distributed computing, and user privacy can be preserved through techniques like federated learning.
Question 2 : Describe a situation where you had to navigate conflicting opinions within your team. How did you facilitate resolution and maintain team cohesion?
- Answer: In navigating conflicting opinions within a team, I facilitated resolution through open communication, active listening, and finding common ground. Prioritizing team cohesion was key to achieving consensus.
Question 3 : How would you enhance the security of user data on Facebook, considering the evolving landscape of cybersecurity threats?
- Answer: Enhancing the security of user data on Facebook involves implementing robust encryption mechanisms, access controls, and regular security audits. Ensuring compliance with privacy regulations and proactive threat monitoring are essential.
Question 4 : Design a real-time notification system for Facebook, ensuring timely delivery of notifications to users across various platforms.
- Answer: Designing a real-time notification system for Facebook requires technologies like WebSocket for real-time communication and push notifications. Ensuring scalability and reliability through distributed systems is crucial for timely delivery.
❤14🆒2🎉1
Date: 01-12-2023
Company Name: JPMorgan Chase
Role: Data Analyst
Topics asked from: SQL, Data Warehousing , Financial Analysis, Risk Assessment, Data Security
Q1: How do you ensure data consistency and integrity in a data warehousing environment?
Ans: I implement data validation checks, use constraints like primary and foreign keys, and ensure that ETL processes have error-handling mechanisms. Regular audits and data reconciliation processes are also set up to ensure data accuracy and consistency.
Q2: Describe a situation where you had to design a star schema for a data warehousing project.
Ans: For a retail sales data warehousing project, I designed a star schema with a central fact table containing sales transactions. Surrounding this were dimension tables like Products, Stores, Time, and Customers. This structure allowed for efficient querying and reporting of sales metrics across various dimensions.
Q3: How would you use data analytics to assess credit risk for loan applicants?
Ans: I'd analyze the applicant's financial history, including credit score, income, employment stability, and existing debts. Using predictive modeling, I'd assess the probability of default based on historical data of similar applicants. This would help in making informed lending decisions.
Q4: Describe a situation where you had to ensure data security for sensitive financial data.
Ans: While working on a project involving customer transaction data, I ensured that all data was encrypted both at rest and in transit. I also implemented role-based access controls, ensuring that only authorized personnel could access specific data sets. Regular audits and penetration tests were conducted to identify and rectify potential vulnerabilities.
Company Name: JPMorgan Chase
Role: Data Analyst
Topics asked from: SQL, Data Warehousing , Financial Analysis, Risk Assessment, Data Security
Q1: How do you ensure data consistency and integrity in a data warehousing environment?
Ans: I implement data validation checks, use constraints like primary and foreign keys, and ensure that ETL processes have error-handling mechanisms. Regular audits and data reconciliation processes are also set up to ensure data accuracy and consistency.
Q2: Describe a situation where you had to design a star schema for a data warehousing project.
Ans: For a retail sales data warehousing project, I designed a star schema with a central fact table containing sales transactions. Surrounding this were dimension tables like Products, Stores, Time, and Customers. This structure allowed for efficient querying and reporting of sales metrics across various dimensions.
Q3: How would you use data analytics to assess credit risk for loan applicants?
Ans: I'd analyze the applicant's financial history, including credit score, income, employment stability, and existing debts. Using predictive modeling, I'd assess the probability of default based on historical data of similar applicants. This would help in making informed lending decisions.
Q4: Describe a situation where you had to ensure data security for sensitive financial data.
Ans: While working on a project involving customer transaction data, I ensured that all data was encrypted both at rest and in transit. I also implemented role-based access controls, ensuring that only authorized personnel could access specific data sets. Regular audits and penetration tests were conducted to identify and rectify potential vulnerabilities.
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Date: 04-12-2023
Company Name: Accenture
Role: Data Scientist
Topic: Silhouette, trend seasonality, bag of words, bagging boosting , F1 Score
1. What do you understand by the term silhouette coefficient?
The silhouette coefficient is a measure of how well clustered together a data point is with respect to the other points in its cluster. It is a measure of how similar a point is to the points in its own cluster, and how dissimilar it is to the points in other clusters. The silhouette coefficient ranges from -1 to 1, with 1 being the best possible score and -1 being the worst possible score.
2. What is the difference between trend and seasonality in time series?
Trends and seasonality are two characteristics of time series metrics that break many models. Trends are continuous increases or decreases in a metric’s value. Seasonality, on the other hand, reflects periodic (cyclical) patterns that occur in a system, usually rising above a baseline and then decreasing again.
3. What is Bag of Words in NLP?
Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences irrespective of its grammatical structure or word order.
4. What is the difference between bagging and boosting?
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
5. What do you understand by the F1 score?
The F1 score represents the measurement of a model's performance. It is referred to as a weighted average of the precision and recall of a model. The results tending to 1 are considered as the best, and those tending to 0 are the worst. It could be used in classification tests, where true negatives don't matter much.
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Company Name: Accenture
Role: Data Scientist
Topic: Silhouette, trend seasonality, bag of words, bagging boosting , F1 Score
1. What do you understand by the term silhouette coefficient?
The silhouette coefficient is a measure of how well clustered together a data point is with respect to the other points in its cluster. It is a measure of how similar a point is to the points in its own cluster, and how dissimilar it is to the points in other clusters. The silhouette coefficient ranges from -1 to 1, with 1 being the best possible score and -1 being the worst possible score.
2. What is the difference between trend and seasonality in time series?
Trends and seasonality are two characteristics of time series metrics that break many models. Trends are continuous increases or decreases in a metric’s value. Seasonality, on the other hand, reflects periodic (cyclical) patterns that occur in a system, usually rising above a baseline and then decreasing again.
3. What is Bag of Words in NLP?
Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences irrespective of its grammatical structure or word order.
4. What is the difference between bagging and boosting?
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
5. What do you understand by the F1 score?
The F1 score represents the measurement of a model's performance. It is referred to as a weighted average of the precision and recall of a model. The results tending to 1 are considered as the best, and those tending to 0 are the worst. It could be used in classification tests, where true negatives don't matter much.
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Date: 05-12-2023
Company Name: JPMorgan Chase
Role: Data Analyst
Topics : SQL, Data Warehousing , Financial Analysis, Risk Assessment, Data Security
Q1: How do you ensure data consistency and integrity in a data warehousing environment?
Ans: I implement data validation checks, use constraints like primary and foreign keys, and ensure that ETL processes have error-handling mechanisms. Regular audits and data reconciliation processes are also set up to ensure data accuracy and consistency.
Q2: Describe a situation where you had to design a star schema for a data warehousing project.
Ans: For a retail sales data warehousing project, I designed a star schema with a central fact table containing sales transactions. Surrounding this were dimension tables like Products, Stores, Time, and Customers. This structure allowed for efficient querying and reporting of sales metrics across various dimensions.
Q3: How would you use data analytics to assess credit risk for loan applicants?
Ans: I'd analyze the applicant's financial history, including credit score, income, employment stability, and existing debts. Using predictive modeling, I'd assess the probability of default based on historical data of similar applicants. This would help in making informed lending decisions.
Q4: Describe a situation where you had to ensure data security for sensitive financial data.
Ans: While working on a project involving customer transaction data, I ensured that all data was encrypted both at rest and in transit. I also implemented role-based access controls, ensuring that only authorized personnel could access specific data sets. Regular audits and penetration tests were conducted to identify and rectify potential vulnerabilities.
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Company Name: JPMorgan Chase
Role: Data Analyst
Topics : SQL, Data Warehousing , Financial Analysis, Risk Assessment, Data Security
Q1: How do you ensure data consistency and integrity in a data warehousing environment?
Ans: I implement data validation checks, use constraints like primary and foreign keys, and ensure that ETL processes have error-handling mechanisms. Regular audits and data reconciliation processes are also set up to ensure data accuracy and consistency.
Q2: Describe a situation where you had to design a star schema for a data warehousing project.
Ans: For a retail sales data warehousing project, I designed a star schema with a central fact table containing sales transactions. Surrounding this were dimension tables like Products, Stores, Time, and Customers. This structure allowed for efficient querying and reporting of sales metrics across various dimensions.
Q3: How would you use data analytics to assess credit risk for loan applicants?
Ans: I'd analyze the applicant's financial history, including credit score, income, employment stability, and existing debts. Using predictive modeling, I'd assess the probability of default based on historical data of similar applicants. This would help in making informed lending decisions.
Q4: Describe a situation where you had to ensure data security for sensitive financial data.
Ans: While working on a project involving customer transaction data, I ensured that all data was encrypted both at rest and in transit. I also implemented role-based access controls, ensuring that only authorized personnel could access specific data sets. Regular audits and penetration tests were conducted to identify and rectify potential vulnerabilities.
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Date: 06-12-2023
Company Name: Airbnb
Role: Data Analyst
Topics: Data Cleaning, Python , Geospatial Analysis, User Experience, Pricing Strategy, Market Analysis
Q1: Describe a situation where you had to clean a messy dataset. What steps did you take?
Ans: I encountered a dataset with missing values, duplicates, and inconsistent formats. I used Python's Pandas library to identify and handle missing values, standardized data formats using regular expressions, and removed duplicates. I also validated the cleaned data against known benchmarks to ensure accuracy.
Q2: How do you handle outliers in a dataset?
Ans: I start by visualizing the data using box plots or scatter plots to identify potential outliers. Then, depending on the nature of the data and the problem context, I might cap the outliers, transform the data, or even remove them if they're due to errors.
Q3: How would you use data to suggest optimal pricing strategies to Airbnb hosts?
Ans: I'd analyze factors like location, property type, amenities, local events, and historical booking rates. Using regression analysis, I'd model the relationship between these factors and pricing to suggest an optimal price range. Additionally, analyzing competitor pricing in the area can provide insights into market rates.
Q4: Describe a situation where you used data to improve the user experience on the Airbnb platform.
Ans: While analyzing user feedback and platform interaction data, I noticed that users often had difficulty navigating the booking process. Based on this, I suggested streamlining the booking steps and providing clearer instructions. A/B testing confirmed that these changes led to a higher conversion rate and improved user feedback.
Company Name: Airbnb
Role: Data Analyst
Topics: Data Cleaning, Python , Geospatial Analysis, User Experience, Pricing Strategy, Market Analysis
Q1: Describe a situation where you had to clean a messy dataset. What steps did you take?
Ans: I encountered a dataset with missing values, duplicates, and inconsistent formats. I used Python's Pandas library to identify and handle missing values, standardized data formats using regular expressions, and removed duplicates. I also validated the cleaned data against known benchmarks to ensure accuracy.
Q2: How do you handle outliers in a dataset?
Ans: I start by visualizing the data using box plots or scatter plots to identify potential outliers. Then, depending on the nature of the data and the problem context, I might cap the outliers, transform the data, or even remove them if they're due to errors.
Q3: How would you use data to suggest optimal pricing strategies to Airbnb hosts?
Ans: I'd analyze factors like location, property type, amenities, local events, and historical booking rates. Using regression analysis, I'd model the relationship between these factors and pricing to suggest an optimal price range. Additionally, analyzing competitor pricing in the area can provide insights into market rates.
Q4: Describe a situation where you used data to improve the user experience on the Airbnb platform.
Ans: While analyzing user feedback and platform interaction data, I noticed that users often had difficulty navigating the booking process. Based on this, I suggested streamlining the booking steps and providing clearer instructions. A/B testing confirmed that these changes led to a higher conversion rate and improved user feedback.
🆒19❤7👏1
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Date: 07-12-2023
Company Name: Tesla
Role: Data Analyst
Topics: Time Series Analysis, Data Warehousing, Production Efficiency
Q1: How would you analyze time series data to forecast production rates for a manufacturing unit?
Ans: I'd use tools like Prophet for time series forecasting. After decomposing the data to identify trends and seasonality, I'd build a model to forecast production rates.
Q2: Describe a situation where you had to design a data warehousing solution for large-scale manufacturing data.
Ans: For a project with multiple manufacturing units, I designed a star schema with a central fact table and surrounding dimension tables to allow for efficient querying.
Q3: How would you use data to identify bottlenecks in a production line?
Ans: I'd analyze production metrics, time logs, and machine efficiency data to identify stages in the production line with delays or reduced output, pinpointing potential bottlenecks.
Q4: How do you ensure data accuracy and consistency in a manufacturing environment with multiple data sources?
Ans: I'd implement data validation checks, use standardized data collection protocols across units, and set up regular data reconciliation processes to ensure accuracy and consistency.
Company Name: Tesla
Role: Data Analyst
Topics: Time Series Analysis, Data Warehousing, Production Efficiency
Q1: How would you analyze time series data to forecast production rates for a manufacturing unit?
Ans: I'd use tools like Prophet for time series forecasting. After decomposing the data to identify trends and seasonality, I'd build a model to forecast production rates.
Q2: Describe a situation where you had to design a data warehousing solution for large-scale manufacturing data.
Ans: For a project with multiple manufacturing units, I designed a star schema with a central fact table and surrounding dimension tables to allow for efficient querying.
Q3: How would you use data to identify bottlenecks in a production line?
Ans: I'd analyze production metrics, time logs, and machine efficiency data to identify stages in the production line with delays or reduced output, pinpointing potential bottlenecks.
Q4: How do you ensure data accuracy and consistency in a manufacturing environment with multiple data sources?
Ans: I'd implement data validation checks, use standardized data collection protocols across units, and set up regular data reconciliation processes to ensure accuracy and consistency.
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Company- CGI
Role- Data Engineer
Experience- 3 years
Apply now- https://www.linkedin.com/jobs/view/3780544525
Company- Planview
Role- Data Scientist
Experience- 1.5years
Apply now- https://www.linkedin.com/jobs/view/3764182097
Company- CRIF India
Role- Data Analyst
Experience- 2 years
Apply now- https://www.linkedin.com/jobs/view/3779670117
Company- Nissan Motor Corporation
Role- Data Scientist
Experience- 3years
Apply now- https://www.linkedin.com/jobs/view/3764775621
Company- RTI International
Role- Data Scientist
Experience- 2 years
Apply now- https://www.linkedin.com/jobs/view/3775359479
All the best💙
Company- Ericsson
Role- Data Scientist
Experience- Fresher
Apply now- https://www.naukri.com/job-listings-data-scientist-ericsson-india-global-services-pvt-ltd-bangalore-bengaluru-0-to-3-years-051223500314?src=jobsearchDesk&sid=17019635492387521&xp=1&px=1&nignbevent_src=jobsearchDeskGNB
Company- Ninja Van
Role- Data Scientist
Experience- Fresher
Apply now- https://www.linkedin.com/jobs/view/3778396247
Company- SAZ India
Role- Junior Data Analyst
Experience- Fresher
Apply now- https://in.indeed.com/cmp/Saz-India/jobs?jk=8ceac79b5ccb2be4&start=0&clearPrefilter=1
Company- Innovaccer
Role- Data Engineer
Experience- Fresher
Apply now- https://www.naukri.com/job-listings-apprentice-data-engineer-innovaccer-noida-0-to-1-years-221123500970?utmcampaign=androidjd&utmsource=share&src=sharedjd
Company- Godrej Agrovet
Role- Data Scientist
Experience- Fresher
Apply now- https://www.naukri.com/job-listings-data-scientist-godrej-agrovet-ltd-mumbai-0-to-2-years-281123500382?utmcampaign=androidjd&utmsource=share&src=sharedjd
Company- Linxon
Role- Data Analyst
Experience- 1 year
Apply now- https://www.linkedin.com/jobs/view/3774813132
Company- Thomson Reuters
Role- Data Scientist
Experience- 1 year
Apply now- https://www.linkedin.com/jobs/view/3778457698
Company- ExxonMobil
Role- Data Scientist
Experience- 3 years
Apply now- https://www.linkedin.com/jobs/view/3779802465
Company- Quantzig
Role- Data Engineer
Experience- 3 years
Apply now- https://www.linkedin.com/jobs/view/3723300032
Company- CGI
Role- Data Engineer
Experience- 3 years
Apply now- https://www.linkedin.com/jobs/view/3780544525
Company- Planview
Role- Data Scientist
Experience- 1.5years
Apply now- https://www.linkedin.com/jobs/view/3764182097
Company- CRIF India
Role- Data Analyst
Experience- 2 years
Apply now- https://www.linkedin.com/jobs/view/3779670117
Company- Nissan Motor Corporation
Role- Data Scientist
Experience- 3years
Apply now- https://www.linkedin.com/jobs/view/3764775621
Company- RTI International
Role- Data Scientist
Experience- 2 years
Apply now- https://www.linkedin.com/jobs/view/3775359479
All the best💙
🆒17🙏3❤1🤩1
#Quiz
Observe the Python Code given below:
class MyClass:
def _ _init_ _ (self, list):
self.list = list
def _ _ getitem_ _ (self, index):
return self.list[index - 1]
obj = MyClass([1, 2, 3, 4, 5])
result = obj[3] + obj[1]
Observe the Python Code given below:
class MyClass:
def _ _init_ _ (self, list):
self.list = list
def _ _ getitem_ _ (self, index):
return self.list[index - 1]
obj = MyClass([1, 2, 3, 4, 5])
result = obj[3] + obj[1]
🔥3🆒1
👏13🔥7❤1
Date: 09-12-2023
Company Name: Spotify
Role: Data Analyst
Topics : Data Streaming, Python, User Behavior Analysis
Q1: How would you handle real-time data streaming for analyzing user listening patterns?
Ans: I'd use platforms like Apache Kafka for real-time data ingestion. Using Python, I'd process this stream to identify real-time patterns and store aggregated data for further analysis.
Q2: Describe a situation where you had to use time series analysis to forecast a trend.
Ans: I analyzed monthly active users to forecast future growth. Using Python's statsmodels, I applied ARIMA modeling to the time series data and provided a forecast for the next six months.
Q3: How would you segment and analyze user behavior based on their music preferences?
Ans: I'd cluster users based on their listening history using unsupervised machine learning techniques like K-means clustering. This would help in creating personalized playlists or recommendations.
Q4: How do you handle missing or incomplete data in user listening logs?
Ans: I'd use imputation methods based on the nature of the missing data. For instance, if a user's listening time is missing, I might impute it based on their average listening time or use collaborative filtering methods to estimate it based on similar users.
————————————————————-
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Company Name: Spotify
Role: Data Analyst
Topics : Data Streaming, Python, User Behavior Analysis
Q1: How would you handle real-time data streaming for analyzing user listening patterns?
Ans: I'd use platforms like Apache Kafka for real-time data ingestion. Using Python, I'd process this stream to identify real-time patterns and store aggregated data for further analysis.
Q2: Describe a situation where you had to use time series analysis to forecast a trend.
Ans: I analyzed monthly active users to forecast future growth. Using Python's statsmodels, I applied ARIMA modeling to the time series data and provided a forecast for the next six months.
Q3: How would you segment and analyze user behavior based on their music preferences?
Ans: I'd cluster users based on their listening history using unsupervised machine learning techniques like K-means clustering. This would help in creating personalized playlists or recommendations.
Q4: How do you handle missing or incomplete data in user listening logs?
Ans: I'd use imputation methods based on the nature of the missing data. For instance, if a user's listening time is missing, I might impute it based on their average listening time or use collaborative filtering methods to estimate it based on similar users.
————————————————————-
Make Your Career In Data Science Domain with our Industry-Oriented Hands-on Course @ Most Affordable Price 🤩
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Date: 10-12-2023
Company Name: LinkedIn
Role: Data Analyst
Topics asked from: Social Network Analysis, Data Visualization, User Engagement
Q1: How would you analyze data to understand user connection patterns on a professional network?
Ans: I'd use graph databases like Neo4j for social network analysis. By analyzing connection patterns, I can identify influencers or isolated communities.
Q2: Describe a challenging data visualization you created to represent user engagement metrics.
Ans: I visualized multi-dimensional data showing user engagement across features, regions, and time using tools like D3.js, creating an interactive dashboard with drill-down capabilities.
Q3: How would you identify and target passive job seekers on LinkedIn?
Ans: I'd analyze user behavior patterns, like increased profile updates, frequent visits to job postings, or engagement with career-related content, to identify potential passive job seekers.
Q4: How do you measure the effectiveness of a new feature launched on LinkedIn?
Ans: I'd set up A/B tests, comparing user engagement metrics between those who have access to the new feature and a control group. I'd then analyze metrics like time spent, feature usage frequency, and overall platform engagement to measure effectiveness.
Company Name: LinkedIn
Role: Data Analyst
Topics asked from: Social Network Analysis, Data Visualization, User Engagement
Q1: How would you analyze data to understand user connection patterns on a professional network?
Ans: I'd use graph databases like Neo4j for social network analysis. By analyzing connection patterns, I can identify influencers or isolated communities.
Q2: Describe a challenging data visualization you created to represent user engagement metrics.
Ans: I visualized multi-dimensional data showing user engagement across features, regions, and time using tools like D3.js, creating an interactive dashboard with drill-down capabilities.
Q3: How would you identify and target passive job seekers on LinkedIn?
Ans: I'd analyze user behavior patterns, like increased profile updates, frequent visits to job postings, or engagement with career-related content, to identify potential passive job seekers.
Q4: How do you measure the effectiveness of a new feature launched on LinkedIn?
Ans: I'd set up A/B tests, comparing user engagement metrics between those who have access to the new feature and a control group. I'd then analyze metrics like time spent, feature usage frequency, and overall platform engagement to measure effectiveness.
❤5👏1🙏1
Date: 11-12-2023
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.
❤9👏1
🚨JOB OPENINGS 🚨
Company- Crossover
Role- Data Scientist
Experience- 2+ years
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Company- Quantzig
Role- Data Engineer
Experience- 3 years
Apply now- https://www.linkedin.com/jobs/view/3723300032
Company- Crossover
Role- Data Scientist
Experience- 2+ years
Apply now- https://www.linkedin.com/jobs/view/3778765863
Company- Mayhem Studios
Role- Sr. Data Analyst
Experience- 2 years
Apply now- https://www.linkedin.com/jobs/view/3778805764
Company- Dotdash Meredith
Role- Data Science- Analyst
Experience- 1 year
Apply now- https://www.linkedin.com/jobs/view/3703885126
Company- HARMAN International
Role- Data Scientist
Experience- 3 years
Apply now- https://www.linkedin.com/jobs/view/3743808131
Company- ExxonMobil
Role- Data Scientist
Experience- 3 years
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Company- Makonis
Role- Data Engineer
Experience- 2 years
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Company- BCG X
Role- Data Engineer
Experience- 2 years
Apply now- https://www.linkedin.com/jobs/view/3693017810
Company- ZURU Tech
Role- Data Engineer
Experience- 2+ years
Apply now- https://www.linkedin.com/jobs/view/3724793856
Company- Lowe's India
Role- Data Engineer
Experience- 3 years
Apply now- https://www.linkedin.com/jobs/view/3780734174
Company- Quantzig
Role- Data Engineer
Experience- 3 years
Apply now- https://www.linkedin.com/jobs/view/3723300032
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114,000+ Data Scientist jobs in India (5,312 new)
Today’s top 114,000+ Data Scientist jobs in India. Leverage your professional network, and get hired. New Data Scientist jobs added daily.
Date: 12-12-2023
Company name: Numerator
Role: Data Scientist
Topic : RNN, pop & remove, calculated field, SQL, Excel
1. What are the uses of using RNN in NLP?
The RNN is a stateful neural network, which means that it not only retains information from the previous layer but also from the previous pass. Thus, this neuron is said to have connections between passes, and through time.
For the RNN the order of the input matters due to being stateful. The same words with different orders will yield different outputs.
RNN can be used for unsegmented, connected applications such as handwriting recognition or speech recognition.
2. How to remove values to a python array?
Ans: Array elements can be removed using pop() or remove() method. The difference between these two functions is that the former returns the deleted value whereas the latter does not.
3. What are the advantages and disadvantages of views in the database?
Answer: Advantages of Views:
As there is no physical location where the data in the view is stored, it generates output without wasting resources.
Data access is restricted as it does not allow commands like insertion, updation, and deletion.
Disadvantages of Views:
The view becomes irrelevant if we drop a table related to that view.
Much memory space is occupied when the view is created for large tables.
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.
————————————————————-
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Company name: Numerator
Role: Data Scientist
Topic : RNN, pop & remove, calculated field, SQL, Excel
1. What are the uses of using RNN in NLP?
The RNN is a stateful neural network, which means that it not only retains information from the previous layer but also from the previous pass. Thus, this neuron is said to have connections between passes, and through time.
For the RNN the order of the input matters due to being stateful. The same words with different orders will yield different outputs.
RNN can be used for unsegmented, connected applications such as handwriting recognition or speech recognition.
2. How to remove values to a python array?
Ans: Array elements can be removed using pop() or remove() method. The difference between these two functions is that the former returns the deleted value whereas the latter does not.
3. What are the advantages and disadvantages of views in the database?
Answer: Advantages of Views:
As there is no physical location where the data in the view is stored, it generates output without wasting resources.
Data access is restricted as it does not allow commands like insertion, updation, and deletion.
Disadvantages of Views:
The view becomes irrelevant if we drop a table related to that view.
Much memory space is occupied when the view is created for large tables.
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.
————————————————————-
Make Your Career In Data Science Domain with our Industry-Oriented Hands-on Course @ Most Affordable Price 🤩
Visit 👉🏻 https://www.cloudyml.com/courses
Stay Safe & Happy Learning💙
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Offer guidance on assignments, projects, and problem solving strategies.
Monitor discussion forums and online platforms to identify common issues and address them proactively.
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Currently pursuing graduation are welcome
Good understanding of English Language
Good written communication
Knowledge of Python, Powerbi, Tableau, Excel, Sql, ML and DL
A PC or Laptop with decent internet speed.
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Join our team as a Teaching Assistance and be a mentor that helps our learners.
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Responsibilities-
Respond to student inquiries and doubts related to data science via chat and virtual meetings.
Provide clear and concise explanations to help students understand data science concepts, algorithms, programming languages, and related topics.
Offer guidance on assignments, projects, and problem solving strategies.
Monitor discussion forums and online platforms to identify common issues and address them proactively.
Eligibility-
Currently pursuing graduation are welcome
Good understanding of English Language
Good written communication
Knowledge of Python, Powerbi, Tableau, Excel, Sql, ML and DL
A PC or Laptop with decent internet speed.
Benefits-
- Internship Certificate
- Stipend 6- 9 k
- Work from Home
- Part time (3hrs per day)
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