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From knowledge to celebrations— CloudyML made it all BIG at Crest 2K25 at Arka Jain University, Jharkhand! 🎉🚀
From knowledge to celebrations— CloudyML made it all BIG at Crest 2K25 at Arka Jain University, Jharkhand! 🎉🚀
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CloudyML made it all BIG at Crest 2K25, Arka Jain University, Jharkhand
From knowledge to celebrations— CloudyML made it all BIG at Crest 2K25! 🎉🚀Kudos to the @department_of_mba at @arkajainuniversity Jharkhand for organizing s...
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Date: 09-04-2025
Company name: Capgemini
Role: Data Scientist
01. What are decorators in Python?
Answer- Decorators are used to add some design patterns to a function without changing its structure. Decorators generally are defined before the function they are enhancing. To apply a decorator we first define the decorator function. Then we write the function it is applied to and simply add the decorator function above the function it has to be applied to. For this, we use the @ symbol before the decorator.
02. What is the ACID property in a database?
Answer- The full form of ACID is atomicity, consistency, isolation, and durability.
•Atomicity refers to the fact that if any aspect of a transaction fails, the whole transaction fails and the database state remains unchanged.
•Consistency means that the data meets all validity guidelines.
•Concurrency management is the primary objective of isolation.
•Durability ensures that once a transaction is committed, it will occur regardless of what happens in between such as a power outage, fire, or some other kind of disturbance.
03. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?
Answer- One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesn’t affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.
04. Given a table Employee having columns empName and empId, what will be the result of the SQL query below?
Answer- Select empName from Employee order by 2 asc;
“Order by 2” is valid when there are at least 2 columns used in SELECT statement. Here this query will throw error because only one column is used in the SELECT statement.
————————————————————
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Company name: Capgemini
Role: Data Scientist
01. What are decorators in Python?
Answer- Decorators are used to add some design patterns to a function without changing its structure. Decorators generally are defined before the function they are enhancing. To apply a decorator we first define the decorator function. Then we write the function it is applied to and simply add the decorator function above the function it has to be applied to. For this, we use the @ symbol before the decorator.
02. What is the ACID property in a database?
Answer- The full form of ACID is atomicity, consistency, isolation, and durability.
•Atomicity refers to the fact that if any aspect of a transaction fails, the whole transaction fails and the database state remains unchanged.
•Consistency means that the data meets all validity guidelines.
•Concurrency management is the primary objective of isolation.
•Durability ensures that once a transaction is committed, it will occur regardless of what happens in between such as a power outage, fire, or some other kind of disturbance.
03. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?
Answer- One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesn’t affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.
04. Given a table Employee having columns empName and empId, what will be the result of the SQL query below?
Answer- Select empName from Employee order by 2 asc;
“Order by 2” is valid when there are at least 2 columns used in SELECT statement. Here this query will throw error because only one column is used in the SELECT statement.
————————————————————
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⏰ Just a quick reminder — our Data Analytics Online Live Program is starting in only 2 days, on 12th April 2025!
🎓 This is your final chance to join the upcoming batch and kickstart your journey toward a career in Data Analytics with
✅ Live expert-led classes
✅ Hands-on projects
✅ 1:1 mentorship & career support
✅ Industrial Internship
✅ 100% Placement Assistance
🔥 Seats are filling up fast — don’t miss out!
Enroll Now:
https://ds.cloudyml.com/data-analytics-live-course
Reply to this email or contact your counselor now to secure your spot 📝
Looking forward to having you onboard!
Best regards,
Team CloudyML ✨
🎓 This is your final chance to join the upcoming batch and kickstart your journey toward a career in Data Analytics with
✅ Live expert-led classes
✅ Hands-on projects
✅ 1:1 mentorship & career support
✅ Industrial Internship
✅ 100% Placement Assistance
🔥 Seats are filling up fast — don’t miss out!
Enroll Now:
https://ds.cloudyml.com/data-analytics-live-course
Reply to this email or contact your counselor now to secure your spot 📝
Looking forward to having you onboard!
Best regards,
Team CloudyML ✨
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❤1👏1
Date: 12-04-2025
Company name: Flipcart
Role: Data Scientist
01. What are Support Vectors in SVM?
Answer- Support Vector Machine (SVM) is an algorithm that tries to fit a line (or plane or hyperplane) between the different classes that maximizes the distance from the line to the points of the classes.
In this way, it tries to find a robust separation between the classes. The Support Vectors are the points of the edge of the dividing hyperplane.
02. Explain Correlation and Covariance?
Answer- Covariance signifies the direction of the linear relationship between two variables, whereas correlation indicates both the direction and strength of the linear relationship between variables.
03. What is P-value?
Answer- P-values are used to make a decision about a hypothesis test. P-value is the minimum significant level at which you can reject the null hypothesis. The lower the p-value, the more likely you reject the null hypothesis.
04. Does Random Forest need Pruning? Why or why not?
Answer-
(a) Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances.
(b) Random Forest usually does not require pruning because it will not over-fit like a single decision tree. This happens due to the fact that the trees are bootstrapped and that multiple random trees use random features so the individual trees are strong without being correlated with each other.
————————————————————
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Company name: Flipcart
Role: Data Scientist
01. What are Support Vectors in SVM?
Answer- Support Vector Machine (SVM) is an algorithm that tries to fit a line (or plane or hyperplane) between the different classes that maximizes the distance from the line to the points of the classes.
In this way, it tries to find a robust separation between the classes. The Support Vectors are the points of the edge of the dividing hyperplane.
02. Explain Correlation and Covariance?
Answer- Covariance signifies the direction of the linear relationship between two variables, whereas correlation indicates both the direction and strength of the linear relationship between variables.
03. What is P-value?
Answer- P-values are used to make a decision about a hypothesis test. P-value is the minimum significant level at which you can reject the null hypothesis. The lower the p-value, the more likely you reject the null hypothesis.
04. Does Random Forest need Pruning? Why or why not?
Answer-
(a) Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances.
(b) Random Forest usually does not require pruning because it will not over-fit like a single decision tree. This happens due to the fact that the trees are bootstrapped and that multiple random trees use random features so the individual trees are strong without being correlated with each other.
————————————————————
Make Your Career In Data Science Domain with our Industry-Oriented Hands-on Course @ Most Affordable Price 🤩
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Stay Safe & Happy Learning 💙
❤3
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All The Best 💙
👏3❤1
🙏 This Good Friday, take a moment to reflect, reset, and rise.
At CloudyML, we believe in second chances, new beginnings, and the power of learning to transform lives. 🌱✨
On this sacred day of hope and renewal, we invite you to invest in your future.
🎁 On behalf of Good Friday, we’re running a special offer on all our courses.
Learn live. Learn practical. Learn with CloudyML.
Visit: www.cloudyml.com/courses
📚 Because every great future begins with the right step
At CloudyML, we believe in second chances, new beginnings, and the power of learning to transform lives. 🌱✨
On this sacred day of hope and renewal, we invite you to invest in your future.
🎁 On behalf of Good Friday, we’re running a special offer on all our courses.
Learn live. Learn practical. Learn with CloudyML.
Visit: www.cloudyml.com/courses
📚 Because every great future begins with the right step
❤4
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All The Best 💙
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All The Best 💙
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Celebrating 4 Amazing Years of CloudyML 🎉
On this very day, 22nd April 2021, we launched our very first course, AI-For-ALL and I still remember the excitement and nervousness of that moment. We had one goal – to make industry-ready AI & Data Science education accessible and affordable for everyone.
Fast forward to today… and I’m filled with immense gratitude. From one course to a complete ecosystem of hands-on recorded self-pace and live interactive programs, trusted by over 30,000+ learners across the world, this journey has been nothing short of transformational.
To our learners, mentors, team members, and supporters – thank you for being a part of this journey. You are the reason CloudyML continues to grow, evolve, and create impact💙
Here’s to empowering more lives, breaking more barriers, and celebrating many more milestones together 🚀
~ Akash Raj ( Founder & MD, CloudyML)
On this very day, 22nd April 2021, we launched our very first course, AI-For-ALL and I still remember the excitement and nervousness of that moment. We had one goal – to make industry-ready AI & Data Science education accessible and affordable for everyone.
Fast forward to today… and I’m filled with immense gratitude. From one course to a complete ecosystem of hands-on recorded self-pace and live interactive programs, trusted by over 30,000+ learners across the world, this journey has been nothing short of transformational.
To our learners, mentors, team members, and supporters – thank you for being a part of this journey. You are the reason CloudyML continues to grow, evolve, and create impact💙
Here’s to empowering more lives, breaking more barriers, and celebrating many more milestones together 🚀
~ Akash Raj ( Founder & MD, CloudyML)
❤6🎉5
CloudyML - Data Science & Analytics pinned «Celebrating 4 Amazing Years of CloudyML 🎉 On this very day, 22nd April 2021, we launched our very first course, AI-For-ALL and I still remember the excitement and nervousness of that moment. We had one goal – to make industry-ready AI & Data Science education…»
YouTube
Vihaan 8.0 Wasn’t Ready for This Energy - CloudyML × DTU
From ideas to impact — CloudyML lit up the stage at Vihaan 8.0! 🌟🔥...
https://youtube.com/shorts/n5pNvaqG_VU?feature=share
From ideas to impact — CloudyML lit up the stage at Vihaan 8.0! 🌟🔥
Grateful to Delhi Technological University and the entire team behind Vihaan 8.0 for curating such an inspiring hackathon.
Innovation was in the air, energy was next-level, and memories? Absolutely legendary! 💡🎊
From ideas to impact — CloudyML lit up the stage at Vihaan 8.0! 🌟🔥
Grateful to Delhi Technological University and the entire team behind Vihaan 8.0 for curating such an inspiring hackathon.
Innovation was in the air, energy was next-level, and memories? Absolutely legendary! 💡🎊
❤3
🚨 JOB OPENING ALERT 🚨
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