Associate Data Scientist

Associate Data Scientist Interview Questions

Updated 16 May 2024

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Interview Questions

  • Q1. Why do you think the objective of predictive modeling is minimizing the cost function? How would you define a cost function after all?

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  • Q2. How can a string be reversed without affecting memory size?

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  • Q3. What is the difference between XGBoost and AdaBoost algorithms?

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  • Q4. What is the cost function for linear and logistic regression?

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  • Q5. Explain the concept of hypothesis testing intuitively using distribution curves for null and alternate hypotheses

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  • Q6. What is principal component analysis? When would you use it?

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  • Q7. What would you do if the training data is skewed?

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  • Q8. What is regularization? Why is it used?

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  • Q9. What is gradient boosting?

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  • Q10. A simple probability puzzle was asked

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Interview Questions

  • Q1. Explain statistical concepts like Hypothesis testing, and type 1 and type 2 errors.

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  • Q2. About a small scenario-based case study, how will you perform

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  • Q3. Asked about scenario-based Small case study questions

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  • Q4. Questions about Machine learning algorithms, AUC ROC, Classification metrics

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  • Q5. Project explanation from the resume, NLP, Python, Supervised/Unsupervised/Boosting Algorithms

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  • Q6. Coding questions about Python and SQL

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  • Q7. Resume Screening.....

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  • Q8. Logical thinking.....

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  • Q9. All ML ALGORITHMS with guesstimate and business case study

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Interview Questions

  • Q1. What Multiple Functions in terms of the Data can be Performed in R programming and What are the major challenges when you Import large Data sets in R or Python ?

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  • Q2. This is really a Good company People and Management Team is highly Experienced & Qualified. They asked lot of basic Stuff Related to Concepts of Data science. Concept Understanding is Major requirement in Global IT Edge.

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  • Q3. Explain the Concept of Data import ways and Variance in R or Python Language.

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Interview Questions

  • Q1. It was a technical round based on Resume. It also consisted of python coding questions, sql questions and Guesstimates. It all depends on the candidate on how to direct this round.

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  • Q2. The test was based on statistics related to data science, Questions related to SQL (med Difficulty), Python Code output options, ML algorithms related questions.

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Interview Questions

  • Q1. Concepts of Machine Learning and their impact Case Study F1-score gamma values in-depth questions Model Evaluation Confusion Metrics What will happen if we don't treat imbalanced data set? Python dataset coding python list coding SQL windows function SQL Joins

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Interview Questions

  • Q1. All technical questions related to ML & DL. The truth is if you know your stuff you can easily get a job. Well if you are reading this review few years down the line then the interview process might change. So be prepared.

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Interview Questions

  • Q1. Every details from resume. From data structures to web development to data analysis tools which I mentioned in my resume, the interviewer was asking about everything.

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  • Q2. Basic he questions

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Interview Questions

  • Q1. The EAD part, the technique I used , why I used the technique, the challenges I faced during solving or making the projects

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  • Q2. The Eda part and specially the graphs and diagrams

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Interview Questions

  • Q1. 1. What is the role of beta value in Logistic regression? 2. What is bias variance trade off? 3. How did you decide on the list of variables that would be used in a model?

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Interview Questions

  • Q1. Questions were basically related to the projects done previously. If you have knowledge about statistics and model interviews you can clear interviews.

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