Episource Data Analyst Interview Questions and Answers

Q1. Introduce yourself
Add your answer

DURATION 1.3 HRS WITH REASONING AND 2 CODING QUESTION

Q1. SQL QUESTION FIND N TH HIGHEST SALARY
Ans. 
Use SQL query with ORDER BY and LIMIT to find the Nth highest salary.
• Use ORDER BY clause to sort salaries in descending order

• Use LIMIT to specify the Nth highest salary

• Example: SELECT salary FROM employees ORDER BY salary DESC LIMIT N,1
Answered by AI
Add your answer
Q2. SQL QUESTION PIVOT TABLE QUESTION
Add your answer

It was a test of 90 minutes containing almost every thing .Quantitative+Logical+Computer fundamentals+2 coding questions

It was an on campus recruitment drive with questions mostly on basic statistics and algebra.

In round 2 the interviewer asked to explain one of my project and then write code for longest common substring problem.

One Simple coding problem followed with couple of probability questions and explaining the Simple Linear model in detail.

2nd round consists of 10 MCQ and 2 python coding Question

1. 2 coding questions - easy to medium type.
2. Basic machine learning/data science MCQ's

Q1. 1. Resume based questions. 3. Machine learning/deep learning/data science related common questions. 4.Data proprocessing, data cleaning common interview questions.
Add your answer
Q2. Questions related to projects done.
Add your answer

Basic data structures, puzzles questions and statistics problems

Q1. What was your previous company project
Add your answer
Q2. What are different types of indexing
Ans. 
Different types of indexing include primary indexing, secondary indexing, clustered indexing, and non-clustered indexing.
• Primary indexing: Index based on the primary key of a table, typically implemented using a B-tree structure.

• Secondary indexing: Index based on a non-primary key column, allowing for faster retrieval of data based on that column.

• Clustered indexing: Physically reorders the table based on the indexed co...read more
Answered by AI
Add your answer
Q3. How does random forest work
Ans. 
Random forest is an ensemble learning method that builds multiple decision trees and merges their predictions.
• Random forest creates a set of decision trees from randomly selected subsets of the training data.

• Each tree in the random forest independently predicts the outcome, and the final prediction is made by averaging the predictions of all the trees.

• Random forest is effective in handling high-dimensional data and can...read more
Answered by AI
Add your answer
Q4. Explain over fitting
Ans. 
Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern.
• Overfitting happens when a model is too complex and captures noise in the training data.

• It leads to poor generalization to new, unseen data.

• Regularization techniques like L1/L2 regularization can help prevent overfitting.

• Cross-validation can be used to detect and prevent overfitting.

• Example: A decision tree with too...read more
Answered by AI
Add your answer
Q5. Explain macros inExcel
Ans. 
Macros in Excel are automated sequences of commands that can be created to perform repetitive tasks.
• Macros can be recorded or written using Visual Basic for Applications (VBA)

• They can automate tasks such as formatting, data manipulation, and calculations

• Macros can be assigned to buttons or keyboard shortcuts for easy access

• They can save time and reduce errors in repetitive tasks
Answered by AI
Add your answer