G2.com Data Scientist Interview Questions and Answers

Q1. Questions related to the projects in the resume and some questions on machine learning concepts.
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Q1. Tell me about yourself?
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Q2. Projects you have done?
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Q3. Technical Skills You have?
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Q4. They asked about Any Certification course i have done?
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Q5. Could you tell me about our company?
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Assignment is about IBM COGNOS!

They want to Know about my behaviour and How can I solve critical Conditions of company!

The session lasted three hours and covered a wide range of topics, including DBMS, operating systems, SQL, computer networks, command-line interface commands, and data science topics such as Python, Pandas, and NumPy. Advanced topics included error handling and various algorithms like classification and clustering, along with deep learning concepts. Additionally, two data structures and algorithms questions were addressed, one being moderate and the other challenging, focusing on dynamic programming with bitmasking.

Q1. What are the basics of data science?
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Q2. What are the best practices for handling large data sets ?
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Q3. What is the purpose of a confusion matrix in data science?
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Q4. Discussion around Kubernetes ?
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Q1. Can you provide an overview of yourself and your past experiences?
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Q2. How effective do you find it when interviewers check your confidence by asking apparent questions that may lead to hesitation in your responses?
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It is an online coding test and the duration is 3 hours. They test your python skills here. Questions are related to Python and Pandas. You have to write code for the given questions.

Q1. Few basic python programs related to lists, data structures, strings, Fibonacci series etc
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Q2. Asked ML related questions like regression, classification algorithms, NLP questions, evaluation metrics, how to handle if there are many attributes in dataset etc
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Q1. What is tokenization in NLP? and, to get raw tokens for a sentence with words seperated by space, why use tokenizers from nltk instead of str.split()?
Ans. 
Tokenization in NLP is the process of breaking down text into smaller units called tokens.
• Tokenization is a fundamental step in NLP for text preprocessing.

• Tokens can be words, phrases, or even individual characters.

• Tokenization helps in preparing text data for further analysis or modeling.

• NLTK tokenizers provide additional functionalities like handling contractions, punctuation, etc.

• str.split() may not handle complex t...read more
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Q2. While tokenzing, if you want to not break some word pairs(or triplets), for ex, to not tokenize the words 'first' and 'name' when they occur together and consider them as a single token, how would you do i...read more
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Q3. For a data with 1000 samples and 700 dimensions, how would you find a line that best fits the data, to be able to extrapolate? this is not a supervised ML problem, there's no target. and how would you do i...read more
Ans. 
To find a line that best fits the data with 1000 samples and 700 dimensions, we can use linear regression.
• For unsupervised ML approach, we can use Principal Component Analysis (PCA) to reduce dimensions and then fit a line using linear regression.

• For supervised ML approach, we need to select a target column. We can choose any of the 700 dimensions as the target and treat it as a regression problem.

• Potential problems of...read more
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Q1. This round was entirely based on my projects on the resume!!!!
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Q2. Explain project starting from the business problem to the deployment? [I was being interrupted for clarifications and questions]
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Q3. [all of the follow up questions were baed on my answers] (some questions on businesss problem and rationale behing it, end users, how success will be measured and data collection that I can't disclose) a...read more
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Normal mental ability question

Q1. Live coding and python question asked(like what is list , set)
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Q1. Gradient descent
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Q2. Precision Recall
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Q1. Where you see yourslef in next 5 year
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Q2. Difficult situation which you face in ur last job
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Q1. Heap concept,insert 10 values in list
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