Swiggy Senior Data Analyst Interview Questions and Answers

SQL test on hackerrank

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?
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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. Interviewer asked me basic SQL questions
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Q2. In the second technical round he asked me advanced SQL topics (Windows Functions, Joins & Subqueries)
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In the second technical round interview asked me about advanced sql topics, theory questions and two coding questions in joins and window functions.

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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The first round was a 45 min long aptitude and case study round which involved basic aptitude questions along with certain cases like "There has been a drop off in customers booking a hotel though the the total number of visits to the site has not dropped. How would you you design a system to analyze the problem and what kind of data will you collect." It was followed by a couple of HR questions

A mock data set of hotel bookings in different countries for last two years were given a week prior to the interview. We were to clean, prepare, derive insights and recommend actions. All of this needed to be documented as 15 page presentation. This presentation then needed to be presented in a 45 min meet.