Curl Analytics Jr. Data Scientist Interview Questions and Answers

Q1. Explain any ML model.
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Q2. Create Dataframe from two lists.
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Q1. Tell me about yourself
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I am a Senior Data Analyst with 5+ years of experience in analyzing complex data sets and providing actionable insights.
• 5+ years of experience in data analysis

• Proficient in statistical analysis and data visualization tools such as Python, R, and Tableau

• Strong problem-solving skills and ability to communicate findings to non-technical stakeholders
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Q2. Asked about recent project
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Q3. Types of join in sql
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Types of join in SQL include inner join, outer join, left join, right join, and full join.
• Inner join: Returns rows when there is a match in both tables

• Outer join: Returns all rows when there is a match in one of the tables

• Left join: Returns all rows from the left table and the matched rows from the right table

• Right join: Returns all rows from the right table and the matched rows from the left table

• Full join: Returns ro
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Q4. Case when statement questions
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Q1. Explain the RAG pipeline?
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RAG pipeline is a data processing pipeline used in data science to categorize data into Red, Amber, and Green based on certain criteria.
• RAG stands for Red, Amber, Green which are used to categorize data based on certain criteria

• Red category typically represents data that needs immediate attention or action

• Amber category represents data that requires monitoring or further investigation

• Green category represents data that...read more
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Q2. Explain Confusion metrics
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Confusion metrics are used to evaluate the performance of a classification model by comparing predicted values with actual values.
• Confusion matrix is a table that describes the performance of a classification model.

• It consists of four different metrics: True Positive, True Negative, False Positive, and False Negative.

• These metrics are used to calculate other evaluation metrics like accuracy, precision, recall, and F1 s...read more
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DSA and ML, AI, Coding question

Q1. Case study which was easy
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Q1. In depth questions on ML
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Excel data analytics case

Q1. Data analytics, data visualisation tools
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SQL, Python coding …