EarlySalary Services Data Analyst Intern Interview Questions and Answers

Q1. Question regarding my resume .It is technical interview
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Case study-this is about loan default prediction assignment

Pyspark, Python and Machine learning questions

Q1. Moderate level questions
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Q2. Moderate level questions from past work experience
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Q1. Projects Mentioned in Resume
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Q2. Related to Python, Pyspark
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3 DSA Que was asked on hackerrank

Q1. 3 DSA que asked by interviewer wirh code
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Q1. They asked about writing some SQL codes
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Q2. They asked about some case studies
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Q1. SQL questions regarding window functions
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Q2. Calc retained revived new customer per month basis what are joins in sql what is dense rank
Ans. 
To calculate retained, revived, and new customers per month basis, joins in SQL and dense rank are used.
• To calculate retained, revived, and new customers per month basis, you would typically use SQL queries to join different tables containing customer data.

• Joins in SQL are used to combine rows from two or more tables based on a related column between them.

• Dense rank is a function in SQL that assigns a rank to each row ...read more
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Q3. How sql query work behind the scenes
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SQL queries work by parsing and executing commands to retrieve data from databases.
• SQL query is written in a specific syntax to communicate with databases.

• The query is parsed by the database management system to understand the command.

• The database engine then executes the query to retrieve the requested data.

• Queries can involve selecting, updating, inserting, or deleting data from tables.

• Example: SELECT * FROM table_na
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Tv commercial case study regarding if we run campaign of product over television how can attribute it to spike or increase in tration which we see in our data.

Q1. Cultural fit round
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Q1. What is clustering
Ans. 
Clustering is a technique used in data analysis to group similar data points together based on their characteristics.
• Clustering is an unsupervised learning method.

• It helps in identifying patterns and relationships in data.

• Common clustering algorithms include K-means, hierarchical clustering, and DBSCAN.

• Example: Grouping customers based on their purchasing behavior.

• Example: Identifying different species of flowers based
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Q2. How to avoid overfitting
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To avoid overfitting, use techniques like cross-validation, regularization, and increasing training data.
• Use cross-validation to evaluate model performance on unseen data

• Apply regularization techniques like L1 or L2 regularization to penalize complex models

• Increase the size of the training dataset to provide more diverse examples

• Use feature selection or dimensionality reduction methods to reduce the complexity of the m...read more
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