ZS Data Science Associate Interview Questions and Answers

I was goven an assignment to work on - code the machine learning solution and present it in slides why I did it, how I did it, the rationale behind my steps and so on.

It was a technical discussion with questions from statistics, my previous projects and so on.

Q1. It was general HR questions.
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Q2. General answers
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Around 30 MCQs on the Aptitude test plus 2 basic coding questions.

Q1. I was asked to give a short presentation on any ML project I worked on and then follow-up questions
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Q1. Types of supervised learning problems.
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Supervised learning problems involve predicting an output variable based on input data with labeled examples.
• Classification: Predicting a categorical label (e.g. spam/not spam)

• Regression: Predicting a continuous value (e.g. house prices)

• Ranking: Predicting the order of a set of items (e.g. search engine results)
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Q2. Explain Logistic regression.
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Logistic regression is a statistical model used to predict the probability of a binary outcome based on one or more predictor variables.
• Logistic regression is used when the dependent variable is binary (0/1, True/False, Yes/No, etc.).

• It estimates the probability that a given observation belongs to a particular category.

• The output of logistic regression is a probability score between 0 and 1.

• It uses the logistic functio...read more
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Q3. Explain Decision Trees.
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Decision Trees are a popular machine learning algorithm used for classification and regression tasks.
• Decision Trees are a tree-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome.

• They are easy to interpret and visualize, making them popular for exploratory data analysis.

• Decision Trees can handle both numerical and c...read more
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Q4. Asked 2 puzzles from InterviewBit.
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A test of 10+ questions was conducted online- mostly mcq based but last question had a problem which needed to be solved via coding

Q1. Showed data and asked what analysis can be made from this
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Q2. Asked about the features that can be build on particular healthcare data
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Q1. Asked questions about hobbies
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Q2. Strength and weaknesses
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