TransUnion Financial Analyst Interview Questions and Answers

Q1. Questions about your experience
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Q2. It will depend your experience
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Statistics Questions

Q1. Assumptions of Machine Learning
Ans. 
Assumptions of Machine Learning include data availability, feature relevance, and model interpretability.
• Data availability is crucial for training machine learning models.

• Assumptions about the relevance of features in the data must be made.

• Model interpretability is important for understanding the decisions made by the machine learning model.
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Q1. Previous work discussion
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Q2. Relevant work experience
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Q3. Tools knowledge
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On python and sql coding.they ask simple to complex questions

Q1. On data science machine learning
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There were 3 rounds for data science consultant position at Equifax. 1st round consists of Python and SQL questions and also in this round they have tested analytical thinking as well.

In 2nd round they have asked me questions from my resume and asked me to explain the projects which I have worked on in previous company. In this round they have asked me pandas related questions and asked me write code.

Q1. In Managerial round again here I have been asked questions related my projects. and no coding round in this stage.
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Q1. What is Tuples? How do you convert a string to a tuple?
Ans. 
Tuples are immutable sequences of elements. A string can be converted to a tuple using the tuple() function.
• Tuples are similar to lists but are immutable

• Elements of a tuple are enclosed in parentheses ()

• A string can be converted to a tuple using the tuple() function

• Each character in the string becomes an element in the tuple
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Q2. What is Left Join? Show one Left join for 2 data frames on Python
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Left join is a type of join operation in SQL that returns all the rows from the left table and matching rows from the right table.
• Left join is performed using the 'left join' keyword in SQL

• In Python, left join can be performed using the 'merge' function from the pandas library

• Syntax: pd.merge(left_dataframe, right_dataframe, how='left', on='key_column')

• Example: pd.merge(df1, df2, how='left', on='id')
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