Tredence Business Analyst Interview Questions, Process, and Tips

Easy one practice rs agarwal

Case study on Power BI + SQL

Q1. Why do you want to leave your last organisation
Ans. 
Seeking new challenges and growth opportunities
• Desire for professional growth and development

• Looking for new challenges and experiences

• Seeking a better work-life balance

• Company restructuring or changes in management
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Q2. Why you move into Data Analytics
Ans. 
I moved into Data Analytics to leverage my analytical skills and passion for uncovering insights from data.
• Passion for uncovering insights from data

• Strong analytical skills

• Interest in utilizing data to drive business decisions
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Round duration - 60 minutes
Round difficulty - Medium

Technical round with questions on Python, basic coding questions and Machine Learning.

Q1. 
Palindrome String Check
Given an alphabetical string S, determine whether it is a palindrome. A palindrome is a string that reads the same backward as forward.
Input:
The first line contains an integer ...read more
Ans. 
Check if a given string is a palindrome or not.
• Iterate through the string from both ends and compare characters.

• If all characters match, the string is a palindrome.

• Consider handling cases where spaces or special characters are present.

• Example: 'racecar' is a palindrome, 'hello' is not.
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Q2. 
Nth Fibonacci Problem Statement
Calculate the Nth term of the Fibonacci series, denoted as F(n), using the formula: F(n) = F(n-1) + F(n-2) where F(1) = 1 and F(2) = 1.
Input:
The first line of each test...read more
Ans. 
Calculate the Nth term of the Fibonacci series using a recursive formula.
• Use recursion to calculate the Nth Fibonacci number by summing the previous two numbers.

• Base cases are F(1) = 1 and F(2) = 1.

• Handle edge cases like N = 1 separately.

• Optimize the solution using memoization to avoid redundant calculations.

• Ensure the input N is within the constraints 1 ≤ N ≤ 10000.
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Q3. How can you extract the month and year from a date column in Pandas?
Ans. 
You can extract the month and year from a date column in Pandas using the dt accessor.
• Use the dt accessor to access the date components

• Use dt.month to extract the month and dt.year to extract the year

• Example: df['date_column'].dt.month will give you the month values

• Example: df['date_column'].dt.year will give you the year values
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Q4. What is the relationship between R-squared and p-value in linear regression?
Ans. 
R-squared measures the goodness of fit of a regression model, while p-value indicates the significance of the relationship between the independent variable and the dependent variable.
• R-squared is a measure of how well the independent variable(s) explain the variability of the dependent variable in a regression model.

• A high R-squared value close to 1 indicates a good fit, meaning the model explains a large portion of th...read more
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Q5. What are underfitting and overfitting in machine learning?
Ans. 
Underfitting and overfitting are common problems in machine learning where the model is either too simple or too complex.
• Underfitting occurs when the model is too simple to capture the underlying patterns in the data.

• Overfitting occurs when the model is too complex and learns noise in the training data as if it were a pattern.

• Underfitting can be addressed by increasing the model complexity or adding more features.

• Overf...read more
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Q6. What is a confusion matrix?
Ans. 
A confusion matrix is a table that is often used to describe the performance of a classification model.
• It is a matrix with rows representing the actual class and columns representing the predicted class.

• It helps in evaluating the performance of a classification model by showing the number of correct and incorrect predictions.

• It is commonly used in machine learning and statistics to assess the quality of a classificatio...read more
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Q7. What is the difference between Random Forest and XGBoost?
Ans. 
Random Forest is an ensemble learning method that builds multiple decision trees and combines their predictions, while XGBoost is a gradient boosting algorithm that builds trees sequentially.
• Random Forest builds multiple decision trees independently and combines their predictions through averaging or voting.

• XGBoost builds trees sequentially, with each tree correcting errors made by the previous ones.

• Random Forest is le...read more
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Q1. 1)Tell me about yourself. 2)What Technical Tools i know and skills i have. 3)What did i do in my previous company(in detail)
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