Vistaprint Senior Data Scientist Interview Questions and Answers

Q1. SQL Window function- hard
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Q2. Case study questions answers
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DSA question, probability, statistical test

SQL and Python coding test

Q1. Bagging vs Boosting
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Q2. Overfitting and Underfitting in GBDT
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Q1. Questions on Recent project
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Q2. Deep Learning vs classical ML
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Q1. What is a logistic regression model?
Ans. 
Logistic regression is a statistical model used to predict the probability of a binary outcome based on one or more predictor variables.
• Logistic regression is used when the dependent variable is binary (0/1, True/False, Yes/No, etc.)

• It estimates the probability that a given input belongs to a particular category.

• The model calculates the odds of the event happening.

• It uses a logistic function to map the input values to ...read more
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Q2. Explain the random forest model.
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Random forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.
• Random forest is a type of ensemble learning method.

• It builds multiple decision trees during training.

• Each tree is built using a subset of the training data and a random subset of features.

• The final prediction is made by averaging the predictions of all the individual trees.

• Random...read more
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Q3. Explain decision trees
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Decision trees are a popular machine learning algorithm used for classification and regression tasks.
• Decision trees are a flowchart-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome.

• They are easy to interpret and visualize, making them popular for exploratory data analysis.

• Decision trees can handle both numerical ...read more
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Q1. How do you see yourself in next 5 years?
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In the next 5 years, I see myself growing into a senior data scientist role, leading projects and mentoring junior team members.
• Continuing to enhance my skills in data analysis, machine learning, and programming languages such as Python and R

• Taking on more responsibilities in project management and client interactions

• Working towards becoming a subject matter expert in a specific industry or domain

• Mentoring and guiding ...read more
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Q2. What are you 5 years back.what is the difference?
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I was a student pursuing my undergraduate degree in Computer Science.
• 5 years back, I was studying Computer Science in college.

• Now, I have completed my degree and gained experience in data science through internships and projects.

• I have developed strong analytical and programming skills over the past 5 years.

• I have also learned new technologies and tools in the field of data science.

• I have a better understanding of real
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Q1. Tell me about your projects?
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Q2. How do you approach the project if you are using logistic regression model?
Ans. 
Approach involves data preprocessing, model training, evaluation, and interpretation.
• Perform data preprocessing such as handling missing values, encoding categorical variables, and scaling features.

• Split the data into training and testing sets.

• Train the logistic regression model on the training data.

• Evaluate the model using metrics like accuracy, precision, recall, and F1 score.

• Interpret the model coefficients to under...read more
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Q1. What are you future goals?
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Q2. What would you do if your interested field doesnt have any work in the company?
Ans. 
I would seek opportunities to apply my skills in related fields within the company.
• Explore other departments or teams within the company that may have projects related to my field of interest

• Offer to collaborate with colleagues in different departments to bring a new perspective to their projects

• Seek out professional development opportunities to expand my skills and knowledge in related areas
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Q1. Explain all of decision tree and random forest?
Ans. 
Decision tree is a tree-like model of decisions and their possible consequences, while random forest is an ensemble learning method that builds multiple decision trees and merges them together.
• Decision tree is a flowchart-like structure where each internal node represents a decision based on an attribute, each branch represents the outcome of the decision, and each leaf node represents a class label.

• Random forest is a ...read more
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Q2. Some basic probability questions
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Q1. Explain past projects
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Q1. Explain resume points
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Resume points are concise descriptions of your work experience, skills, and achievements listed on your resume.
• Resume points should be clear, specific, and quantifiable.

• Use action verbs to start each point, such as 'developed', 'implemented', 'analyzed'.

• Include relevant metrics or results to demonstrate impact, such as 'increased sales by 20%' or 'reduced processing time by 30%'.
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