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Tredence Analytics interview experience Real time questions & tips from candidates to crack your interview
Business Analyst
Tredence Analytics
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1 rounds | 7 Coding problems
Interview preparation journey
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Preparation
Duration: 5 months
Topics: Python, DSA, Machine Learning, SQL, DBMS
Tip
Tip

Tip 1 : Must do Previously asked Interview as well as Online Test Questions.
Tip 2 : Go through all the previous interview experiences from Codestudio and Leetcode.
Tip 3 : Do at-least 2 good projects and you must know every bit of them.

Application process
Where: Other
Eligibility: Above 7 CGPA
Resume Tip
Resume tip

Tip 1 : Have at-least 2 good projects explained in short with all important points covered.
Tip 2 : Every skill must be mentioned.
Tip 3 : Focus on skills, projects and experiences more.

Interview rounds
01
Round
Medium
Video Call
Duration60 minutes
Interview date23 Aug 2021
Coding problem7

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

1. Check if a number is palindrome
Easy
0/40
Asked in companies
EXL ServiceThalesOptum

You're given an alphabetical string ‘S’.


Determine whether it is palindrome or not. A palindrome is a string that is equal to itself upon reversing it.


For example:
...
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Problem approach

One approach could be to first reverse digits of n, then compare the reverse of n with n. If both are same, then return true, else false.

Pseudo code :

 

reverseDigits(num)
{
	Initialise a variable rev_num to 0
	while (num is greater than 0)...
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2. Nth Fibonacci
Easy
0/40
Asked in companies
HCL TechnologiesAccentureIBM

The n-th term of Fibonacci series F(n), where F(n) is a function, is calculated using the following formula -

    F(n) = F(n - 1) + F(n - 2), 
    Where, F(1) = 1, F(2) = 1
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Problem approach

The recursive approach involves direct implementation of mathematical recurrence formula. 
F(n) = F(n-1)+F(n-2)

 

Pseudocode :

fibonacci(n):
	if(n<=1)
		return n;
	return fibonacci(n-1) + fibonacci(n-2)

 

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3. Pandas Question

Get month and Year from Date Column in Pandas

Problem approach

Use datetime.month attribute to find the month and use datetime.year attribute to find the year present in the Date .
df['year'] = df['Date Attribute'].dt.year
df['month'] = df['Date Attribute'].dt.month
Here ‘df’ is the object of the dataframe of pandas, pandas is callable as ‘pd’ (as imported), datetime is callable as ‘dt’ (as imported). ‘Date Attribute’ is the date column in your da...

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4. Machine Learning Question

Relationship between R-squared and p-value in linear regression.

Problem approach

There is no established association/relationship between p-value and R-square. This all depends on the data (i.e.; contextual).
R-square value tells you how much variation is explained by your model. So 0.1 R-square means that your model explains 10% of variation within the data. The greater R-square the better the model. Whereas p-value tells you about the F statistic hypothesis testing of ...

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5. Machine Learning Question

What is underfitting and overfitting?

Problem approach

1) Overfitting refers to the scenario where a machine learning model can’t generalize or fit well on unseen dataset. A clear sign of machine learning overfitting is if its error on the testing or validation dataset is much greater than the error on training dataset. 

 

2) Overfitting is a term used in statistics that refers to a modeling error that occurs when a function c...

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6. Machine Learning Question

What is a confusion matrix?

Problem approach

It is a performance measurement for machine learning classification problem where output can be two or more classes. It is a table with 4 different combinations of predicted and actual values. It is extremely useful for measuring Recall, Precision, Specificity, Accuracy, and most importantly AUC-ROC curves.


TP, FP, FN, TN in terms of pregnancy analogy : 
True Positive:
Inte...

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7. Machine Learning Question

Difference between Random forest and XG Boost.

Problem approach

1. Random Forest and XGBoost are decision tree algorithms where the training data is taken in a different manner. XGBoost trains specifically the gradient boost data and gradient boost decision trees. The training methods used by both algorithms is different. We can use XGBoost to train the Random Forest algorithm if it has high gradient data or we can use Random Forest algorithm to train XGBoo...

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