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I applied via Walk-in and was interviewed before Mar 2023. There was 1 interview round.
Raw data was shared and I was asked to map the variables into two tables related to customer demographics. Also, if any of the variables were not suitable, give explanation. I was asked to provide the data type and constraints for the table structure.
I applied via Naukri.com and was interviewed in Mar 2024. There was 1 interview round.
To find customers who have not placed any order, use a LEFT JOIN to match customers with orders and filter out those with NULL values in the order column.
Use a LEFT JOIN to combine the customers table with the orders table.
Filter out the rows where the order column is NULL, indicating no orders placed.
Select the customer information from the resulting dataset.
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posted on 18 Sep 2024
I am a detail-oriented data analyst with a strong background in statistics and programming.
Graduated with a degree in Statistics
Proficient in programming languages such as Python and R
Experience in analyzing and interpreting data sets
Strong attention to detail and problem-solving skills
I have a strong analytical background, excellent problem-solving skills, and a passion for data-driven decision making.
I have a degree in Statistics and experience with data analysis tools such as Python and SQL.
I have successfully completed projects where I identified trends and patterns in data to drive business decisions.
I am a quick learner and have a strong attention to detail, ensuring accuracy in my analysis.
posted on 1 Oct 2023
I applied via campus placement at Lokmanya Tilak College of Engineering, Navi Mumbai and was interviewed in Apr 2023. There were 3 interview rounds.
Questions related to web development and quantitative aptitude. It was one hour test. Based on the score of this test, the position is allotted to you for which you are eligible to give the interview.
Coding round didn't had compilers and I had to write the pseudo code for the given problems. Few DSA and SQL questions were there in this round.
I applied via Referral and was interviewed before Aug 2023. There was 1 interview round.
Overfitting occurs when a model learns the training data too well, leading to poor performance on new, unseen data.
Overfitting occurs when a model is too complex and captures noise in the training data.
It can be identified when a model performs well on training data but poorly on test data.
Techniques to prevent overfitting include cross-validation, regularization, and early stopping.
Example: A decision tree with too ma...
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