Interview questions for Tiger Analytics Data Analyst
Hi everyone, this topic is for sharing Preparation guidelines and interview experience for Tiger Analytics Data Analyst
The Data Analyst at Tiger Analytics involves a multi-stage assessment and interview process, designed to evaluate both technical skills and business proficiency. Below is a summary of the process and key points from the interviews you provided:
Technical/ML/Python/Probability
Explain the difference between supervised and unsupervised learning with real-world examples relevant to analytics.
What is overfitting and how can it be prevented? (e.g., regularization, cross-validation, pruning, early stopping)
Which evaluation metrics would you use for a classification problem vs. a regression problem, and why?
Describe the bias-variance trade-off and its implications for model performance.
Walk me through your feature engineering approach for a typical business dataset.
What is cross-validation? When and how would you apply it?
Explain Bayes’ theorem and solve a conditional probability scenario.
Compute and interpret probabilities for independent vs. dependent events.
Write Python code to clean and analyze a dataset (e.g., filter rows, group/aggregate, compute new features) using pandas.
Implement a Python function to find top-N items by a metric or to remove duplicates efficiently.
Data Interpretation/SQL/Python/R
Given a sales/orders dataset, write an SQL query to compute monthly revenue and identify the top 5 customers by spend.
Perform a left join between Customers and Orders to list customers with no orders.
Calculate user retention or cohort metrics based on transaction dates.
Identify outliers or anomalies in a numeric column and explain methods you would use (e.g., z-score, IQR).
You are given a raw dataset. What initial checks and EDA steps would you perform, and what key insights would you aim to surface?
Present your findings from a dataset analysis and recommend next steps or additional data needed.
Application Process: I applied via a referral in May 2022.
Interview Rounds:
Round 1 - Resume Shortlist:
Questions Asked: No specific questions were asked; the focus was on reviewing my resume.
Your Approach: I ensured my resume was concise and highlighted relevant skills and experiences for the Data Analyst role.
Outcome: My resume was shortlisted for the next round.
Round 2 - Aptitude Test:
Questions Asked: The round consisted of 10 MCQ questions and 2 Python coding questions. The difficulty level was medium to difficult.
Your Approach: I focused on solving the coding questions efficiently and double-checked my answers for the MCQs.
Outcome: I completed the test, but the final outcome wasn’t shared immediately.
Preparation Tips:
For the coding questions, practice medium to difficult-level Python problems.
Brush up on aptitude topics as the MCQs can be tricky.
Conclusion:
Overall, the interview process was smooth, and the questions were challenging but fair. I would recommend focusing on Python coding and aptitude skills to prepare for this role.
Application Process: Approached by the company and interviewed before December 2021.
Interview Rounds:
Round 1 - Resume Shortlist:
Outcome: Resume was shortlisted for further rounds.
Round 2 - Coding Test:
Questions Asked: Online coding test with 2 Python questions (DP and String) and 8 MCQs.
Outcome: Cleared the coding test.
Round 3 - Technical Round 1:
Questions Asked:
Easy-level array coding question.
Detailed explanation of one of my projects (line by line).
Permutation and combination question.
Outcome: Advanced to the next round.
Round 4 - Technical Round 2:
Questions Asked: Similar to the second round.
Outcome: Final round cleared.
Preparation Tips:
Prepare basic coding questions (arrays and strings) from LeetCode (easy level).
Conclusion:
Overall, the interview process was smooth, and the questions were aligned with the role. Practicing coding problems and being thorough with my projects helped me perform well. For future candidates, I’d recommend focusing on coding fundamentals and being ready to explain your projects in detail.
Detailed questions about projects mentioned in the resume.
Your Approach: Prepared thoroughly by revisiting my projects and brushing up on core ML and data science topics.
Outcome: Cleared the round.
Preparation Tips:
Focus on your resume and projects, as most questions will revolve around them.
Practice coding problems and revise fundamental ML/data science concepts.
Be ready to explain your projects in detail, including challenges faced and solutions implemented.
Conclusion:
Overall, the interview process was smooth and focused on practical knowledge. Preparing my resume well and being thorough with my projects helped a lot. For future candidates, I’d recommend practicing coding and being confident about your project work.
Application Process: Applied through campus placement.
Interview Rounds:
Round 1 - Technical Round:
Questions Asked:
Probability-related questions.
1 easy LeetCode coding question (details not specified).
Your Approach:
For the probability questions, I relied on fundamental probability concepts and practiced problems beforehand.
For the coding question, I ensured I understood the problem statement clearly and wrote clean, efficient code.
Outcome: Successfully cleared the round.
Preparation Tips:
Brush up on probability concepts and practice problems.
Solve easy to medium-level coding problems on platforms like LeetCode.
Focus on writing clean and efficient code during the interview.
Conclusion:
The interview process was straightforward, and the questions were aligned with the role’s requirements. Practicing probability and coding problems beforehand helped me perform well. For future candidates, I’d recommend focusing on these areas and staying calm during the interview.
Application Process: Applied via campus placement in October 2021.
Interview Rounds:
Round 1 - Technical Interview:
Questions Asked:
Basic coding questions.
Questions on probability.
Puzzles.
Your Approach: Prepared by revising fundamental coding concepts, probability theory, and practicing puzzles.
Outcome: The interview was friendly and supportive. [Result not specified.]
Preparation Tips:
Focus on basic coding skills.
Brush up on probability concepts.
Practice puzzles to improve problem-solving skills.
The interview environment is supportive, so stay calm and confident.
Conclusion:
Overall, the interview was a positive experience. The interviewer was approachable, and the questions were aligned with the role. For future candidates, I recommend thorough preparation in coding, probability, and puzzles to ace the technical round.
Application Process: Applied through the company’s recruitment process, which included resume shortlisting, aptitude test, and coding test rounds.
Interview Rounds:
Round 1 - Resume Shortlist:
Questions Asked: Resume-based screening to assess qualifications and skills.
Your Approach: Ensured my resume was professional and highlighted relevant skills and experiences.
Outcome: Successfully shortlisted for the next round.
Round 2 - Aptitude Test:
Questions Asked: Basic aptitude questions and coding-related problems.
Your Approach: Revised fundamental aptitude topics and practiced basic coding problems.
Outcome: Cleared the aptitude test and moved to the coding round.
Round 3 - Coding Test:
Questions Asked: Basic data-related questions and coding problems.
Your Approach: Focused on data structures and algorithms, and practiced coding under time constraints.
Outcome: Performed well and advanced further in the process.
Preparation Tips:
Thoroughly review your resume and be prepared to discuss every detail.
Practice aptitude questions and basic coding problems to build confidence.
Be honest about your skills and avoid exaggerating them.
Conclusion:
The interview process was structured and tested both technical and analytical skills. Being well-prepared and confident helped me navigate the rounds effectively. For future candidates, I recommend focusing on fundamentals and maintaining clarity in communication.
Location: On-campus at International Institute of Information Technology (IIIT), Bhubaneswar
Application Process: Applied via campus placement drive before September 2023.
Interview Rounds:
Round 1 - Aptitude Test:
Questions Asked: Basic statistics and algebra questions.
Your Approach: Focused on revising fundamental concepts and practicing sample problems beforehand.
Outcome: Cleared the round successfully.
Round 2 - Coding Test:
Questions Asked: Explained one of my projects and wrote code for the longest common substring problem.
Your Approach: Prepared by reviewing my project details and practicing dynamic programming problems.
Outcome: Advanced to the next round.
Round 3 - Coding Test:
Questions Asked: Solved a simple coding problem, answered probability questions, and explained the Simple Linear Model in detail.
Your Approach: Brushed up on probability concepts and linear regression fundamentals.
Outcome: Performed well and completed the interview process.
Preparation Tips:
Revise basic statistics, algebra, and probability concepts.
Practice coding problems, especially dynamic programming and string manipulation.
Be thorough with your project details and understand the underlying models (e.g., Simple Linear Model).
Conclusion:
The interview process was structured and tested both technical and analytical skills. Preparing well for aptitude and coding rounds helped me perform confidently. Future candidates should focus on strengthening their fundamentals and practicing problem-solving under time constraints.
Application Process: I applied through campus placement before December 2021.
Interview Rounds:
Round 1 - Coding Test:
Questions Asked: Questions based on SQL and Python (hard level).
Your Approach: I focused on solving complex SQL queries and Python problems, ensuring I understood the logic behind each solution.
Outcome: Passed this round and moved to the next stage.
Round 2 - Technical Round:
Questions Asked:
Live coding round on Python.
Live coding questions on SQL.
Your Approach: I practiced live coding beforehand to get comfortable with real-time problem-solving. I also revised core Python and SQL concepts.
Outcome: The round was challenging, but I managed to solve the problems.
Preparation Tips:
Don’t apply to this company if you’re a fresher, as they don’t have enough projects for freshers.
Focus on mastering SQL and Python, especially complex queries and live coding scenarios.
Conclusion:
The interview process was rigorous, with a strong emphasis on technical skills. While I cleared the rounds, I felt the company might not be the best fit for freshers due to limited project opportunities. My advice would be to thoroughly prepare for coding challenges and assess the company’s suitability for your career stage.
Application Process: Applied via LinkedIn and was interviewed in January 2024.
Interview Rounds:
Round 1 - Aptitude Test:
Questions Asked: Easy questions.
Your Approach: Found the questions straightforward and easy to clear.
Outcome: Cleared the round successfully.
Round 2 - Technical Round:
Questions Asked: One-to-one technical questions based on SQL and Python.
Your Approach: Answered the questions confidently, focusing on my knowledge of SQL and Python.
Outcome: [Not specified]
Preparation Tips:
Prepare well in Python and SQL questions.
Conclusion:
The interview process was smooth, with a focus on technical skills. Make sure to brush up on SQL and Python concepts to perform well in the technical round.
Application Process: I was interviewed in June 2022. The process included three rounds: Resume Shortlist, Coding Test, and a One-on-One Round.
Interview Rounds:
Round 1 - Resume Shortlist:
Questions Asked: The recruiter reviewed my resume for relevance and clarity.
Your Approach: I ensured my resume was concise and highlighted my key skills and experiences.
Outcome: I was shortlisted for the next round.
Round 2 - Coding Test:
Questions Asked: There were 2 easy-level coding questions and some aptitude MCQs.
Your Approach: I focused on solving the coding problems efficiently and double-checked my answers for the MCQs.
Outcome: I cleared this round and moved to the final interview.
Round 3 - One-on-One Round:
Questions Asked:
3 coding questions (easy to medium level).
Discussion about my projects and past internship experience.
Basic maths questions on probability, statistics, and permutations & combinations.
Your Approach: I prepared by revising basic DSA and 12th-grade maths concepts. I also practiced explaining my projects clearly.
Outcome: The round went well, and I felt confident about my performance.
Preparation Tips:
Focus on basic Data Structures and Algorithms (DSA).
Revise 12th-grade maths topics like probability, statistics, and permutations & combinations.
Be ready to discuss your projects and internships in detail.
Conclusion:
Overall, the interview process was smooth and well-structured. I would advise future candidates to ensure their resumes are crisp and to practice basic coding and maths problems thoroughly. Confidence and clarity while discussing your experiences can make a big difference!
Application Process: Applied through campus placement before February 2022.
Interview Rounds:
Round 1 - Resume Shortlist:
Questions Asked: Resume screening to check qualifications and experience.
Your Approach: Ensured my resume was error-free and highlighted relevant skills.
Outcome: Passed this round.
Round 2 - Coding Test:
Questions Asked: MCQs on Java, OOPs concepts, and basic coding questions. Surprisingly, no questions related to ML, Python, SQL, or BI tools.
Your Approach: Answered the questions based on my knowledge of OOPs and Java.
Outcome: Not sure why the test was unrelated to the role, but no one from our college was selected.
Round 3 - Assignment:
Questions Asked: This round did not happen as no one was called further from our college.
Your Approach: N/A
Outcome: N/A
Preparation Tips:
Focus on basic skills, especially if you’ve studied OOPs and related subjects. Brush up on them, as unexpected topics might appear in the test.
Conclusion:
The interview process was unexpected, with the coding test focusing on Java and OOPs instead of data analytics skills. It was disappointing that no one from our college was selected, but it was a learning experience. Future candidates should be prepared for surprises and ensure they cover a broad range of topics, even if they seem unrelated to the role.
Application Process: Applied through an online job portal.
Interview Rounds:
Round 1 - Technical Interview:
Questions Asked:
Explain a hypothetical use case in your stream that uses data science.
Probability questions.
Two coding questions.
Some machine learning questions.
Your Approach:
For the use case, I outlined a real-world scenario from my academic projects and explained how data science could be applied to solve it.
For probability and coding questions, I took my time to think through the problem and explained my thought process step-by-step.
For machine learning questions, I focused on the basics and how they relate to practical applications.
Outcome: The interviewer was polite and patient, helping me evaluate my logical thinking. The feedback was constructive, and I felt it was a good learning experience.
Preparation Tips:
Brush up on probability and coding basics.
Be ready to explain real-world use cases of data science.
Practice explaining your thought process clearly during problem-solving.
Conclusion:
Overall, the interview was a great experience. The interviewer was supportive, and the questions were well-balanced to test both technical and analytical skills. I could have prepared more on machine learning concepts, but the feedback helped me identify areas for improvement. For future candidates, focus on clarity in explanations and practical applications of data science.
Application Process: I applied through campus placement in October 2023.
Interview Rounds:
Round 1 - Aptitude Test:
Questions Asked: Basic aptitude and coding questions.
Your Approach: I focused on solving the aptitude questions quickly and efficiently, ensuring accuracy in the coding section.
Outcome: Passed this round successfully.
Round 2 - Technical Round:
Questions Asked:
It goes for two rounds.
Moderate SQL question regarding joins with a WHERE clause.
Your Approach: For the SQL question, I carefully analyzed the query requirements and ensured I used the correct joins and WHERE conditions.
Outcome: Cleared this round as well.
Round 3 - HR Round:
Questions Asked: General HR questions (the round lasted only 10 minutes).
Your Approach: I kept my answers concise and professional, focusing on my interest in the role and company.
Outcome: Successfully cleared the HR round.
Preparation Tips:
Be confident in at least one programming language, especially SQL for technical rounds.
Practice aptitude questions to improve speed and accuracy.
Conclusion:
Overall, the interview process was smooth and well-structured. I felt prepared for the technical and aptitude rounds, but I could have practiced more SQL scenarios to feel even more confident. My advice to future candidates is to focus on mastering SQL and staying calm during the HR round.
Application Process: Applied via twocom.com and was interviewed in October 2021.
Interview Rounds:
Round 1 - Coding Test:
Questions Asked: 3 basic data structure questions (LeetCode medium-easy level).
Your Approach: Focused on solving the problems efficiently within the 1-hour time limit.
Outcome: Successfully completed the round.
Round 2 - Coding Test:
Questions Asked: 2 data structure questions (LeetCode medium-hard level) and 10 MCQs on basic computer science knowledge.
Your Approach: Prioritized solving the coding questions first and then tackled the MCQs.
Outcome: Cleared this round as well.
Round 3 - One-on-One Round:
Questions Asked:
How can you prove to the client that students with higher classes are taller than those in lower classes?
Compare two arrays in Python and print if both are the same or not.
What is permutation and combination, and how is it used in data science?
Your Approach: Explained the logic clearly for each question and provided practical examples where applicable.
Outcome: Performed well and advanced further in the process.
Preparation Tips:
Don’t get stuck on any question; think through it and take your time.
Be vocal about your thought process. If you’re stuck, communicate it to the interviewer rather than wasting time.
Conclusion:
The interview process was challenging but fair. The key was to stay calm and articulate my thought process clearly. Practicing LeetCode problems and brushing up on basic CS concepts helped a lot. For future candidates, focus on problem-solving and communication skills.
Application Process: I applied for the Data Analyst position at Tiger Analytics through Naukri.com in April 2023.
Interview Rounds:
Round 1 - Resume Shortlist:
Questions Asked: The recruiter reviewed my resume to assess my qualifications and experience.
Your Approach: I ensured my resume was concise and highlighted relevant skills and projects.
Outcome: My resume was shortlisted for the next round.
Round 2 - Aptitude Test:
Questions Asked: The test included questions on basic data structures, puzzles, and statistics problems.
Your Approach: I brushed up on core concepts and practiced problem-solving beforehand.
Outcome: I cleared the aptitude test and moved to the next round.
Round 3 - One-on-one Round:
Questions Asked:
What was your previous company project?
What are different types of indexing?
How does random forest work?
Explain overfitting.
Explain macros in Excel.
Your Approach: I answered based on my understanding and practical experience, providing clear explanations.
Outcome: The interview went well, but I did not receive further updates.
Preparation Tips:
Focus on strengthening your knowledge of SQL and Python, as these are key skills for the role.
Practice solving puzzles and statistics problems to prepare for the aptitude test.
Be ready to explain your previous projects and technical concepts like indexing, random forests, and overfitting.
Conclusion:
Overall, the interview process was smooth and well-structured. I felt prepared for the technical rounds, but I could have practiced more on explaining macros in Excel. For future candidates, I recommend thoroughly reviewing your resume and brushing up on both technical and analytical skills.
Application Process: The application was part of the campus placement process.
Interview Rounds:
Round 1 - Technical Interview:
Questions Asked:
Tell me about yourself.
Questions about my internship, such as what I learned and competitor analysis.
Questions about my ML project.
Write a Python program for the KNN algorithm (I suggested using the sklearn library, but the interviewer wanted raw code).
Write pseudo-code for finding the shortest distance between two given words in an array.
Explain the Random Forest algorithm (I mistakenly explained Decision Tree).
Your Approach: I focused on explaining my projects and internships clearly. For coding questions, I tried to provide logical solutions even if I couldn’t write the exact code.
Outcome: The interviewer seemed satisfied with my problem-solving approach despite some gaps in coding knowledge. I was sent to the next round.
Round 2 - Analytical and Communication Skills Interview:
Questions Asked:
Views on how data has changed the world.
Details about my startup mentioned in the resume.
Key performance metrics for the eCommerce market.
Calculate the market size for the Indian grocery market.
Questions on statistics and data cleaning.
Asked if I had any questions for them (I inquired about the VP’s daily challenges and skills to learn).
Your Approach: I structured my answers logically and tried to communicate my thoughts clearly. For market-sizing, I broke down the problem into smaller parts.
Outcome: The round lasted about 45 minutes, and the interviewer appreciated my analytical thinking and communication skills.
Preparation Tips:
Brush up on coding basics, especially for algorithms like KNN.
Practice explaining ML concepts clearly, even if you can’t write the code.
Prepare for market-sizing and analytical questions by breaking them down systematically.
Always have thoughtful questions ready for the interviewer.
Conclusion:
Overall, the interview was a great learning experience. I realized the importance of knowing some coding implementations even if libraries are available. My analytical and communication skills helped me progress, but I could have prepared better for coding questions. For future candidates, focus on both theoretical and practical aspects of data analysis, and always engage with the interviewer thoughtfully.
Application Process: The application was submitted online, and the process included an online test followed by technical and HR rounds.
Interview Rounds:
Round 1 - Online Test:
Questions Asked: The test covered Python concepts relevant to the Data Analyst role.
Your Approach: I reviewed Python basics, data structures, and common libraries like Pandas and NumPy to prepare.
Outcome: Cleared the round successfully.
Round 2 - Technical Interview:
Questions Asked: Focused on Python programming, data manipulation, and problem-solving scenarios.
Your Approach: I explained my thought process clearly and used examples to demonstrate my understanding.
Outcome: Advanced to the next round.
Round 3 - Technical Interview:
Questions Asked: More in-depth Python questions, including real-world data analysis problems.
Your Approach: I applied my knowledge of Python libraries and logical reasoning to solve the problems.
Outcome: Cleared this round as well.
Round 4 - HR Interview:
Questions Asked: General HR questions about my background, motivation, and fit for the role.
Your Approach: I answered honestly and aligned my responses with the company’s values.
Outcome: Successfully completed the HR round.
Preparation Tips:
Brush up on Python fundamentals, especially data structures and libraries like Pandas and NumPy.
Practice solving real-world data analysis problems to build confidence.
Be clear and concise in explaining your thought process during technical rounds.
Conclusion:
The interview process was smooth and well-structured. I felt prepared for the technical rounds, but practicing more real-world problems could have made me even more confident. My advice to future candidates is to focus on practical applications of Python and stay calm during the interviews.
Questions Asked: Questions related to machine learning concepts, Python programming, and probability. The interviewer also provided datasets to interpret using tools like SQL, Python, or R.
Your Approach: Demonstrated problem-solving skills by explaining ML concepts clearly and writing efficient Python code. Interpreted the datasets logically.
Outcome: Cleared the round with positive feedback.
Questions Asked: Further in-depth questions on ML, Python, and probability, along with another dataset interpretation task.
Your Approach: Built on the previous round’s performance, ensuring clarity in explanations and accuracy in coding.
Outcome: Successfully cleared the round.
Preparation Tips:
Focus on strengthening your understanding of machine learning concepts, Python programming, and probability.
Practice interpreting datasets using tools like SQL, Python, or R.
Be prepared to explain your thought process clearly during technical rounds.
Conclusion:
The interview process was thorough and focused on assessing quantitative and analytical skills. The key to success was a strong foundation in ML, Python, and probability, along with the ability to interpret data effectively. Practicing problem-solving and clear communication helped a lot. Future candidates should focus on these areas to perform well.