Imarticus Learning Data Scientist Interview Questions and Answers

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. 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. Define a function for count of owels in a string.
Ans. 
A function to count vowels in a string, identifying 'a', 'e', 'i', 'o', 'u' regardless of case.
• Define a function named 'count_vowels'.

• Use a loop or a comprehension to iterate through each character in the string.

• Check if each character is a vowel (a, e, i, o, u) using a set for efficiency.

• Maintain a counter to keep track of the number of vowels found.

• Return the final count after iterating through the string.

• Example: co...read more
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Q2. Interviewer expecting to explain big ML algorithms than simple regression and classification tasks..
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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
Ans. 
A self join in SQL allows a table to be joined with itself to compare rows within the same table.
• Self join is useful for hierarchical data, like employee-manager relationships.

• Example: SELECT a.EmployeeID, a.Name, b.Name AS ManagerName FROM Employees a JOIN Employees b ON a.ManagerID = b.EmployeeID;

• It can also be used to find duplicates or related records within the same table.

• Example: SELECT a.ProductID, a.ProductName...read more
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Q3. Bias and Variance Tradeoff
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Q4. Model interpretability
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Q1. Ask some questions about my self and previously what i did and some questions related to company...
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Q2. Tell me about your self
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Q3. What are the skills you have
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Q4. Why are you choose this company
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They share some apptitude questions and communication related questions to answer them...

Take a one topic from my self to discuss with other to communicate....how easily

Verbal, logical, Quantative test

Q1. Self introduction
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Q1. What makes you a suitable candidate for this position?
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Q2. Where do you envision yourself in five years?
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Any general knowledge topic

Q1. Tell me about yourself
Ans. 
I am a data analyst with a passion for transforming data into actionable insights, skilled in SQL, Python, and data visualization.
• Educational Background: I hold a degree in Statistics, which provided me with a strong foundation in data analysis techniques.

• Professional Experience: I have over 3 years of experience working with large datasets, where I utilized SQL to extract and manipulate data.

• Technical Skills: Proficie...read more
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Q2. Strength and weakness
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Q3. Short term and long term goal
Ans. 
Short term goal is to enhance data analysis skills, long term goal is to become a data science expert.
• Short term goal: Improve proficiency in SQL, Python, and data visualization tools

• Long term goal: Obtain advanced certifications in machine learning and AI

• Short term goal: Complete online courses on statistical analysis and data cleaning

• Long term goal: Lead data science projects and mentor junior analysts
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Q4. Why did you chosen this company?
Ans. My Strength is that I am not only a hard working person but also a smart working person. I am a quick learner and self motivational person. I am a good team player, good listener and good in time management
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Q5. Why we want to hire you?
Ans. 
I bring a unique blend of analytical skills, industry knowledge, and a passion for data-driven decision-making.
• Strong analytical skills: I have experience using tools like SQL and Python to extract insights from complex datasets.

• Industry knowledge: My background in [specific industry] allows me to understand key metrics and trends that drive business success.

• Problem-solving mindset: I successfully identified a 15% cost...read more
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Q6. Salary discussion
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