Great Learning Data Scientist Interview Questions, Process, and Tips

Q1. Greatset number from an array
Ans. 
Find the greatest number from an array of strings.
• Convert the array of strings to an array of integers.

• Use a sorting algorithm to sort the array in descending order.

• Return the first element of the sorted array as the greatest number.
Answered by AI
Add your answer
Q2. Questions related to Hypothesis testing
Add your answer

Python Coding Test to test general knowledge on progamming

Q1. Explain Bias-Variance Tradeoff
Ans. 
Bias-variance tradeoff is the balance between model complexity and generalization error.
• Bias refers to error from erroneous assumptions in the learning algorithm, leading to underfitting.

• Variance refers to error from sensitivity to fluctuations in the training data, leading to overfitting.

• Increasing model complexity reduces bias but increases variance, while decreasing complexity increases bias but reduces variance.

• The...read more
Answered by AI
Add your answer

Q1. Why should we hire you
Add your answer

Q1. Final round discussion on package and benefits
Add your answer

Q1. Interpretation of classification metrics like accuracy, precision, recall
Ans. 
Classification metrics like accuracy, precision, and recall are used to evaluate the performance of a classification model.
• Accuracy measures the overall correctness of the model's predictions.

• Precision measures the proportion of true positive predictions out of all positive predictions.

• Recall measures the proportion of true positive predictions out of all actual positive instances.
Answered by AI
Add your answer
Q2. 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
Answered by AI
Add your answer
Q3. Difference between R-squared and Adjusted R-squared
Ans. 
R-squared measures the proportion of variance explained by the model, while Adjusted R-squared adjusts for the number of predictors in the model.
• R-squared is the proportion of variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1, with 1 indicating a perfect fit.

• Adjusted R-squared penalizes the addition of unnecessary predictors to the model, providing a more accur...read more
Answered by AI
Add your answer
Q4. Feature Selection Techniques
Ans. 
Feature selection techniques help in selecting the most relevant features for building predictive models.
• Filter methods: Select features based on statistical measures like correlation, chi-squared test, etc.

• Wrapper methods: Use a specific model to evaluate the importance of features by adding or removing them iteratively.

• Embedded methods: Feature selection is integrated into the model training process, like LASSO regre...read more
Answered by AI
Add your answer

Q1. Various types of joins in SQL
Ans. 
Various types of joins in SQL include inner join, outer join, left join, right join, and full join.
• Inner join: Returns rows when there is a match in both tables.

• Outer join: Returns all rows when there is a match in one of the tables.

• Left join: Returns all rows from the left table and the matched rows from the right table.

• Right join: Returns all rows from the right table and the matched rows from the left table.

• Full joi...read more
Answered by AI
Add your answer
Q2. SQL query on self join
Add your answer
Q3. Bias and Variance Tradeoff
Add your answer
Q4. Model interpretability
Add your answer

The coding test was a Hackerank test with 3 python and 2 SQL questions.

Q1. This is a technical test with questions from Machine Learning and statistics.
Add your answer
Q2. What is a Central Limit Theorem?
Ans. 
Central Limit Theorem states that the sampling distribution of the mean of any independent, random variable will be normal or nearly normal.
• The theorem applies to large sample sizes.

• It is a fundamental concept in statistics.

• It is used to estimate population parameters from sample statistics.

• It is important in hypothesis testing and confidence intervals.

• Example: If we take a large number of samples of the same size from...read more
Answered by AI
Add your answer
Q3. Can you explain gradient descent?
Ans. 
Gradient descent is an iterative optimization algorithm used to minimize a cost function by adjusting model parameters.
• Gradient descent is used in machine learning to optimize models.

• It works by iteratively adjusting model parameters to minimize a cost function.

• The algorithm calculates the gradient of the cost function and moves in the direction of steepest descent.

• There are different variants of gradient descent, such...read more
Answered by AI
Add your answer

Q1. This was related to Projects, what projects did you work on and domain-related questions
Add your answer
Q2. What is Image segmentation?
Ans. 
Image segmentation is the process of dividing an image into multiple segments or regions.
• It is used in computer vision to identify and separate objects or regions of interest in an image.

• It can be done using various techniques such as thresholding, clustering, edge detection, and region growing.

• Applications include object recognition, medical imaging, and autonomous vehicles.

• Examples include separating the foreground a...read more
Answered by AI
Add your answer
Q3. Questions about output shape when an convolution operation is performed using a filter.
Add your answer
Q4. How is object detection done using CNN?
Ans. 
Object detection using CNN involves training a neural network to identify and locate objects within an image.
• CNNs use convolutional layers to extract features from images

• These features are then passed through fully connected layers to classify and locate objects

• Common architectures for object detection include YOLO, SSD, and Faster R-CNN
Answered by AI
Add your answer

Analyze a scenario for the reduce in sales of a product in the end of the month.

Q1. One to One round with Manager, less technical but some case study and work culture related.
Add your answer