Full Stack Academy Data Scientist Interview Questions and Answers

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
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Q1. Why should we hire you
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Q1. Final round discussion on package and benefits
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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.
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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
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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
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Q1. This was related to Projects, what projects did you work on and domain-related questions
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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
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Q3. Questions about output shape when an convolution operation is performed using a filter.
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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
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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.
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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.
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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
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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
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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
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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
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Q2. SQL query on self join
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Q3. Bias and Variance Tradeoff
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Q4. Model interpretability
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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.
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Q2. Questions related to Hypothesis testing
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Q1. Identify geometric alorithm pattern
Ans. 
Geometric algorithm patterns involve solving problems related to geometric shapes and structures.
• Identifying and solving problems related to points, lines, angles, and shapes

• Utilizing geometric formulas and theorems to find solutions

• Examples include calculating area, perimeter, angles, and distances in geometric figures
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Q2. If minimal data, which would you train for categorical prediction model?
Ans. 
I would train a decision tree model as it can handle categorical data well with minimal data.
• Decision tree models are suitable for categorical prediction with minimal data

• They can handle both numerical and categorical data

• Decision trees are easy to interpret and visualize

• Examples: predicting customer churn, classifying spam emails
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Q1. What's the difference between k means and knn
Ans. 
K-means is a clustering algorithm while KNN is a classification algorithm.
• K-means is unsupervised learning, KNN is supervised learning

• K-means partitions data into K clusters based on distance, KNN classifies data points based on similarity to K neighbors

• K-means requires specifying the number of clusters (K), KNN requires specifying the number of neighbors (K)

• Example: K-means can be used to group customers based on purc...read more
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Q1. NLP techniques, word stop,
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Q2. K-nearset neighout algorithm, gaussian mixtture model
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