Global It Edge Associate Data Scientist Interview Questions and Answers

This was good aptitude test computer based

Coding round share screen and code

Q1. Explains OOPs concept
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Q2. Explain SOLID principles
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There were verbal, non verbal, reasoning , English and maths questions

Q1. Tell me about your project.
Ans. 
I worked on a project analyzing customer behavior using machine learning algorithms.
• Used Python for data preprocessing and analysis

• Implemented machine learning models such as decision trees and logistic regression

• Performed feature engineering to improve model performance
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Q2. What programming knowledge you have ?
Ans. 
Proficient in Python, R, and SQL with experience in data manipulation, visualization, and machine learning algorithms.
• Proficient in Python for data analysis and machine learning tasks

• Experience with R for statistical analysis and visualization

• Knowledge of SQL for querying databases and extracting data

• Familiarity with libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn
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Q1. Where do you stay ?
Ans. 
I currently stay in an apartment in downtown area.
• I stay in an apartment in downtown area

• My current residence is in a city

• I live close to my workplace
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Q2. Tell me about you
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I am a data science enthusiast with a strong background in statistics and machine learning.
• Background in statistics and machine learning

• Passionate about data science

• Experience with data analysis tools like Python and R
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Q1. Difference between bagging and boosting
Ans. 
Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.
• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model c...read more
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Q2. Parameters of Decision Tree
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Parameters of a Decision Tree include max depth, min samples split, criterion, and splitter.
• Max depth: maximum depth of the tree

• Min samples split: minimum number of samples required to split an internal node

• Criterion: function to measure the quality of a split (e.g. 'gini' or 'entropy')

• Splitter: strategy used to choose the split at each node (e.g. 'best' or 'random')
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Q3. Explain any one of your project in detail
Ans. 
Developed a predictive model to forecast customer churn in a telecom company
• Collected and cleaned customer data including usage patterns and demographics

• Used machine learning algorithms such as logistic regression and random forest to build the model

• Evaluated model performance using metrics like accuracy, precision, and recall

• Provided actionable insights to the company to reduce customer churn rate
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Q1. Project related questions from your CV
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Q1. Question on transformers
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Q2. Comparison of transfer learning and fintuning.
Ans. 
Transfer learning involves using pre-trained models on a different task, while fine-tuning involves further training a pre-trained model on a specific task.
• Transfer learning uses knowledge gained from one task to improve learning on a different task.

• Fine-tuning involves adjusting the parameters of a pre-trained model to better fit a specific task.

• Transfer learning is faster and requires less data compared to training a...read more
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*****, arjumpudi satyanarayana

Q1. What is the python language
Ans. 
Python is a high-level programming language known for its simplicity and readability.
• Python is widely used for web development, data analysis, artificial intelligence, and scientific computing.

• It emphasizes code readability and uses indentation for block delimiters.

• Python has a large standard library and a vibrant community of developers.

• Example: print('Hello, World!')

• Example: import pandas as pd
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Q2. What is the code problems
Ans. 
Code problems refer to issues or errors in the code that need to be identified and fixed.
• Code problems can include syntax errors, logical errors, or performance issues.

• Examples of code problems include missing semicolons, incorrect variable assignments, or inefficient algorithms.

• Identifying and resolving code problems is a key skill for data scientists to ensure accurate and efficient data analysis.
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Q3. What is the python code
Ans. 
Python code is a programming language used for data analysis, machine learning, and scientific computing.
• Python code is written in a text editor or an integrated development environment (IDE)

• Python code is executed using a Python interpreter

• Python code can be used for data manipulation, visualization, and modeling
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Q4. What is the project
Ans. 
The project is a machine learning model to predict customer churn for a telecommunications company.
• Developing predictive models using machine learning algorithms

• Analyzing customer data to identify patterns and trends

• Evaluating model performance and making recommendations for reducing customer churn
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Q5. What is the lnderssip
Ans. 
The question seems to be incomplete or misspelled.
• It is possible that the interviewer made a mistake while asking the question.

• Ask for clarification or context to provide a relevant answer.
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Q1. Gave one easy question and asked what will be the output
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Q2. Leetcode 2 sum question
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Q1. Overfitting and Underfitting
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Q2. Find Nth-largest element
Ans. 
Find Nth-largest element in an array
• Sort the array in descending order

• Return the element at index N-1
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Q3. NLP Data preprocessing
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Q1. Salary Discussion
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Q2. Fitment discussion
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