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Carelon Global Solutions Senior Data Scientist Interview Questions and Answers
Carelon Global Solutions Senior Data Scientist Interview Experiences
1 interview found
Selected
I applied via Referral and was interviewed before May 2023. There were 2 interview rounds.
(1 Question)
- Q1. Difference between ridge and lasso in regression
- Ans.
Ridge and Lasso are regularization techniques in regression to prevent overfitting by adding penalty terms to the cost function.
Ridge regression adds a penalty term equivalent to the square of the magnitude of coefficients (L2 regularization).
Lasso regression adds a penalty term equivalent to the absolute value of the magnitude of coefficients (L1 regularization).
Ridge regression tends to shrink the coefficients toward...
(1 Question)
- Q1. How to delete a table in the database?
- Ans.
To delete a table in the database, you can use the DROP TABLE statement.
Use the DROP TABLE statement followed by the table name to delete the table.
Make sure to backup any important data in the table before deleting it.
Ensure that you have the necessary permissions to delete the table.
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Skills evaluated in this interview
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I applied via Company Website and was interviewed in Jul 2024. There was 1 interview round.
(6 Questions)
- Q1. Asked Algorithms used in the project (No follow-up on mentioned algorithms, cut-off mid-explanation of business problem, scale, and solution wanting to know just the names of the algorithm) - Answered by n...
- Q2. Count all pairs of numbers from a list where the ending digit of the ith number equals the starting digit of the jth number. Example [122, 21, 21, 23] should have 5 pairs (122, 21), (122, 21), (122, 23), (...
- Ans.
Count pairs of numbers where ending digit of ith number equals starting digit of jth number.
Iterate through each pair of numbers in the list
Check if the ending digit of the ith number equals the starting digit of the jth number
Increment the count if the condition is met
- Q3. Interpretation of graphs, the first graph had perpendicular lines from the error to the fitted line and the second graph had lines from the error to the fitted line, parallel to the y-axis. - Interpreted t...
- Ans.
Interpretation of graphs in linear regression analysis
Perpendicular lines from error to fitted line in first graph indicate OLS using projection matrices
Lines parallel to y-axis from error to fitted line in second graph suggest evaluation of linear regression to y-pred - y-actual method
PCA could also be a possible interpretation for the second graph
- Q4. What does np.einsum() do
- Ans.
np.einsum() performs Einstein summation on arrays.
Performs summation over specified indices
Can also perform other operations like multiplication, contraction, etc.
Syntax: np.einsum(subscripts, *operands)
- Q5. How to generate random numbers using numpy, what is the difference between numpy.random.rand and numpy.random.randn
- Ans.
numpy.random.rand generates random numbers from a uniform distribution, while numpy.random.randn generates random numbers from a standard normal distribution.
numpy.random.rand generates random numbers from a uniform distribution between 0 and 1.
numpy.random.randn generates random numbers from a standard normal distribution with mean 0 and standard deviation 1.
Example: np.random.rand(3, 2) will generate a 3x2 array of r...
- Q6. Difference between logit and probabilities in deep learning
- Ans.
Logit is the log-odds of the probability, while probabilities are the actual probabilities of an event occurring.
Logit is the natural logarithm of the odds ratio, used in logistic regression.
Probabilities are the actual likelihood of an event occurring, ranging from 0 to 1.
In deep learning, logit values are transformed into probabilities using a softmax function.
Logit values can be negative or positive, while probabili
Interview Preparation Tips
No mathematics/statistics/probability/algorithm is discussed in terms of implementations, or enhancements.
Skills evaluated in this interview
Senior Data Scientist Interview Questions & Answers
Microsoft Corporation
Anonymous
posted on 13 Apr 2024
I applied via Company Website and was interviewed in Mar 2024. There were 2 interview rounds.
1 hour, overall data science related, codility
(2 Questions)
- Q1. What is data science?
- Ans.
Data science is a field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
Data science involves collecting, analyzing, and interpreting large amounts of data to solve complex problems.
It combines statistics, machine learning, data visualization, and computer science to uncover patterns and trends in data.
Data scientists use programming language...
- Q2. Simple answer to this question
Skills evaluated in this interview
Senior Data Scientist Interview Questions & Answers
Bosch Global Software Technologies
Anonymous
posted on 6 May 2024
I applied via Approached by Company and was interviewed in Apr 2024. There was 1 interview round.
Python Interview questions on list and vectors
Selected
I applied via Company Website and was interviewed in Dec 2024. There were 3 interview rounds.
Basic self evaluation test.
(3 Questions)
- Q1. What project I have completed and follow-up questions on that?
- Q2. How to handle class imbalance.
- Ans.
Handling class imbalance involves techniques like resampling, using different algorithms, and adjusting class weights.
Use resampling techniques like oversampling or undersampling to balance the classes.
Utilize algorithms that are robust to class imbalance, such as Random Forest, XGBoost, or SVM.
Adjust class weights in the model to give more importance to minority class.
Use evaluation metrics like F1 score, precision, r...
- Q3. Basic Python coding questions.
(2 Questions)
- Q1. Data-related questions.
- Q2. ML Ops questions.
Interview Preparation Tips
- Python
- MLOPS
I applied via Recruitment Consulltant and was interviewed in Oct 2024. There was 1 interview round.
(1 Question)
- Q1. How to ensure scalability
I applied via LinkedIn and was interviewed in Jun 2024. There was 1 interview round.
(2 Questions)
- Q1. How do you preprocess large/small dataset
- Ans.
Preprocessing large/small datasets involves cleaning, transforming, and organizing data to prepare it for analysis.
Remove duplicates and missing values
Normalize or standardize numerical features
Encode categorical variables
Feature scaling
Handling outliers
Dimensionality reduction techniques like PCA
Splitting data into training and testing sets
- Q2. Data augmentation
Interview Preparation Tips
(2 Questions)
- Q1. Basic pandas questions on dataframes
- Q2. Some quiz questions
Selected
I applied via Campus Placement and was interviewed in Nov 2023. There was 1 interview round.
(1 Question)
- Q1. Questions related to the projects in the resume and some questions on machine learning concepts.
I applied via Referral and was interviewed in Oct 2023. There were 2 interview rounds.
SQL coding test on HackerRank. Also some questions on previous experience
Case study on a data project
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