Naga Aravind Yasam

#collected Netflix Analytics Engineer Interview Experience: CTC - 35 LPA SQL Questions: 1️⃣ SQL Question 1: Identify VIP Users for Netflix Question: To better cater to its most dedicated users, Netflix would like to identify its “VIP users” - those who are most active in terms of the number of hours of content they watch. Write a SQL query that will retrieve the top 10 users with the most watched hours in the last month. Tables: • users table: user_id (integer), sign_up_date (date), subscription_type (text) • watching_activity table: activity_id (integer), user_id (integer), date_time (timestamp), show_id (integer), hours_watched (float) 2️⃣ SQL Question 2: Analyzing Ratings For Netflix Shows Question: Given a table of user ratings for Netflix shows, calculate the average rating for each show within a given month. Assume that there is a column for user_id, show_id, rating (out of 5 stars), and date of review. Order the results by month and then by average rating (descending order). Tables: • show_reviews table: review_id (integer), user_id (integer), review_date (timestamp), show_id (integer), stars (integer) 3️⃣ SQL Question 3: What does EXCEPT / MINUS SQL commands do? Question: Explain the purpose and usage of the EXCEPT (or MINUS in some SQL dialects) SQL commands. 4️⃣ SQL Question 4: Filter Netflix Users Based on Viewing History and Subscription Status Question: You are given a database of Netflix’s user viewing history and their current subscription status. Write a SQL query to find all active customers who watched more than 10 episodes of a show called “Stranger Things” in the last 30 days. Tables: • users table: user_id (integer), active (boolean) • viewing_history table: user_id (integer), show_id (integer), episode_id (integer), watch_date (date) • shows table: show_id (integer), show_name (text) 5️⃣ SQL Question 5: What does it mean to denormalize a database? Question: Explain the concept and implications of denormalizing a database. 6️⃣ SQL Question 6: Filter and Match Customer’s Viewing Records Question: As a data analyst at Netflix, you are asked to analyze the customer’s viewing records. You confirmed that Netflix is especially interested in customers who have been continuously watching a particular genre - ‘Documentary’ over the last month. The task is to find the name and email of those customers who have viewed more than five ‘Documentary’ movies within the last month. ‘Documentary’ could be a part of a broader genre category in the genre field (for example, ‘Documentary, History’). Therefore, the matching pattern could occur anywhere within the string. Tables: • movies table: movie_id (integer), title (text), genre (text), release_year (integer) • customer table: user_id (integer), name (text), email (text), last_movie_watched (integer), date_watched (date)

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5 WhatsApp Groups for Data Science and ML Updates tinyurl.com/yx2wy54z #DSMLEnthusiasts #DataScience #MachineLearning #BigData #AI #AINews #AnalyticsInsight #AnalyticsInsightMagazine

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Meesho Data Analyst interview experience (0-3) - Power BI Questions: 1. Explain the concept of context transition in DAX and provide an example. 2. How would you optimize a complex Power BI report for faster performance? 3. Describe the process of creating and using calculation groups in Power BI. 4. Explain how you would handle large datasets in Power BI without compromising performance. 5. What is a composite model in Power BI, and how can it be used effectively? 6. How does the USERELATIONSHIP function work, and when would you use it? 7. Describe how to use Power Query M language for advanced data transformations. 8. Explain the difference between CROSSFILTER and TREATAS in DAX. SQL Questions: 1. How would you optimize a slow-running query with multiple joins? 2. What is a recursive CTE, and can you provide an example of when to use it? 3. Explain the difference between clustered and non-clustered indexes and when to use each. 4. Write a query to find the second highest salary in each department. 5. How would you detect and resolve deadlocks in SQL? 6. Explain window functions and provide examples of ROW_NUMBER, RANK, and DENSE_RANK. 7. Describe the ACID properties in database transactions and their significance. 8. Write a query to calculate a running total with partitions based on specific conditions. Follow Digvijay Rajput for more.

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Meesho Data Analyst interview experience (0-3) - Power BI Questions: 1. Explain the concept of context transition in DAX and provide an example. 2. How would you optimize a complex Power BI report for faster performance? 3. Describe the process of creating and using calculation groups in Power BI. 4. Explain how you would handle large datasets in Power BI without compromising performance. 5. What is a composite model in Power BI, and how can it be used effectively? 6. How does the USERELATIONSHIP function work, and when would you use it? 7. Describe how to use Power Query M language for advanced data transformations. 8. Explain the difference between CROSSFILTER and TREATAS in DAX. SQL Questions: 1. How would you optimize a slow-running query with multiple joins? 2. What is a recursive CTE, and can you provide an example of when to use it? 3. Explain the difference between clustered and non-clustered indexes and when to use each. 4. Write a query to find the second highest salary in each department. 5. How would you detect and resolve deadlocks in SQL? 6. Explain window functions and provide examples of ROW_NUMBER, RANK, and DENSE_RANK. 7. Describe the ACID properties in database transactions and their significance. 8. Write a query to calculate a running total with partitions based on specific conditions. Follow Digvijay Rajput for more.

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Meesho Data Analyst interview experience (0-3) - Power BI Questions: 1. Explain the concept of context transition in DAX and provide an example. 2. How would you optimize a complex Power BI report for faster performance? 3. Describe the process of creating and using calculation groups in Power BI. 4. Explain how you would handle large datasets in Power BI without compromising performance. 5. What is a composite model in Power BI, and how can it be used effectively? 6. How does the USERELATIONSHIP function work, and when would you use it? 7. Describe how to use Power Query M language for advanced data transformations. 8. Explain the difference between CROSSFILTER and TREATAS in DAX. SQL Questions: 1. How would you optimize a slow-running query with multiple joins? 2. What is a recursive CTE, and can you provide an example of when to use it? 3. Explain the difference between clustered and non-clustered indexes and when to use each. 4. Write a query to find the second highest salary in each department. 5. How would you detect and resolve deadlocks in SQL? 6. Explain window functions and provide examples of ROW_NUMBER, RANK, and DENSE_RANK. 7. Describe the ACID properties in database transactions and their significance. 8. Write a query to calculate a running total with partitions based on specific conditions. Follow Digvijay Rajput for more.

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📌 #confidential interview questions for data analysts... ✅ SQL: 1. How would you optimize a complex query that takes a long time to execute? 2. What is the difference between an INNER JOIN and a LEFT JOIN? When would you use each? 3. Explain the concept of database normalization and its different forms (1NF, 2NF, 3NF). 4. How do you prevent SQL injection attacks in queries? 5. Can you explain how transactions work in SQL? What are ACID properties? ✅ Excel: 1. How do you create a dynamic dashboard in Excel using Pivot Tables and Slicers? 2. Can you explain how to perform a Monte Carlo simulation in Excel? 3. How do you automate repetitive tasks in Excel using VBA (Visual Basic for Applications)? 4. What are array formulas, and when would you use them in Excel? 5. How do you use conditional formatting to highlight trends and outliers in a dataset? ✅ Power BI: 1. How do you handle row-level security (RLS) in Power BI reports? 2. Can you explain the difference between calculated columns and measures in Power BI? 3. How do you track and improve Power BI report performance using the Performance Analyzer? 4. How do you implement drillthrough functionality in a Power BI report? 5. What are the different types of Power BI filters, and how do you manage them? ✅ Python: 1. How do you use pandas to perform groupby operations and aggregate data? 2. Can you explain the differences between NumPy and pandas libraries in Python? 3. How would you handle missing or null values in a large dataset using Python? 4. How do you apply machine learning models using scikit-learn in Python for predictive analysis? 5. Explain how to use the itertools library in Python to work with permutations and combinations. Follow for more updates Shreya Gupta 💫 #googledataanalytics #google #lifeatgoogle #careerswitch #careerbreak #sql #dedication #hardwork #persistance #hackkerrank #jobs #fear #motivation #dataanalytics #dataanalysis #interview #SQL #DataSkills #DataAnalytics #DataScience #PracticeMakesPerfect #SQLQueries #DataManagement #LearnSQL #CodingPractice #DataExperts #HackerRank #LeetCode #GeeksforGeeks #Consistency #HappyLearning

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Here is a very interesting question asked in Probo for a senior data analyst position. We have a table which has 5 columns. a. User_id, poll_id, poll_option_id, amount, created_dt b. Users invest money on these different poll options - these entries are there in the table c. One of the options is the outcome of the event. Example : Event: How many matches will the Indian cricket team win in 2022? A) Less than 50 B) 50-60 C) 61-65 D) Greater than 65 India wins 63 matches in the end. Option C is the winner. Money invested in option A, B and D should be proportionately distributed amongst users who invested money in option C. For example - If the total money invested in option A, B and D is Rs. 1500 and there are 3 users who invested 500 in option C {250, 200 and 50} These users would receive (750, 600 and 150 respectively) - sum is 1500. Write down a query for the above settlement process. Check out my detailed explanation in the comments section. #sql #analytics

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Chi-Square Goodness of Fit Test Categorical Data: When you have categorical data and want to see if the distribution of observed frequencies matches an expected distribution. Expected Frequencies: You should have a sufficiently large sample size, and the expected frequency in each category should be at least 5. Steps to Perform the Chi-Square Goodness of Fit Test Define Hypotheses: Null Hypothesis: The observed data follows the expected distribution. Alternative Hypothesis: The observed data does not follow the expected distribution. Calculate Expected Frequencies: Determine the expected frequencies for each category based on the theoretical distribution. Compute Chi-Square Statistic Determine Degrees of Freedom: Degrees of Freedom=Number of Categories−1 Find the Critical Value: Use a Chi-Square distribution table to find the critical value based on the significance level (α usually 0.05) and the degrees of freedom. Make a Decision: If the Chi-Square statistic is greater than the critical value, reject the null hypothesis. If the Chi-Square statistic is less than or equal to the critical value, do not reject the null hypothesis. Example Suppose you roll a die 60 times, and the results are as follows: 1: 8 times 2: 10 times 3: 12 times 4: 9 times 5: 11 times 6: 10 times You want to test if the die is fair. Expected Frequency: If the die is fair, each face should appear 60/6 = 10 Times Chi - Square Statistics = 1 Degree of Freedom = 6-1 = 5 Critical Value: For α=0.05\alpha = 0.05α=0.05 and 5 degrees of freedom, the critical value is approximately 11.07. Decision: Since 1 < 11.07, we do not reject the null hypothesis. There is no evidence to suggest that the die is unfair. #ChiSquare #MachineLearning #Statistics

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Hey everyone! Today we are going to look at TIP in Tableau. Dynamic number format when switching measures (Calc) Create a new calc. field to switch from one measure to another. Assign a different sign for each measure type. Format the calc. field accordingly. It only works if the measures are always positive. What are your thoughts? Share them in the comments below! 👇 Abishek Gupta #TableauTips #DataAnalysis #TableauPerformance #DataVisualization #DataManagement #TableauTricks #DataCleaning #TableauExtract #DataOptimization #DataInsights #TableauExperts #DataAnalytics #TableauCommunity #DataExtraction #TableauWorkbook #DataPreparation #AnalyticsInTableau #TableauSkills #DataEfficiency #TableauHacks #DataTips #TableauUsers #PerformanceBoost #DataConnections #TableauTutorial #AnalyticsTips #TableauLearning #DataCleansing #TableauBestPractices #DataExtractionTips #TableauHelp #DataOptimizationTips #TableauWisdom #DataVisualizationTips

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🚀 Starting Your Tableau Journey: Key Skills to Master for Job Success 🚀 If you're learning Tableau and looking to break into the data visualization or analytics field, you're on the right track! Here are some essential Tableau skills that can give you a competitive edge in the job market: 💡 1. Master the Basics First: Understand the fundamentals – dashboards, worksheets, and charts. Be confident in using core chart types like bar charts, line graphs, and pie charts. 📊 2. Data Connections: Learn how to connect to different data sources (Excel, SQL, Google Sheets, etc.) and manage joins, unions, and relationships. Understanding data blending and extraction will give you a strong foundation. 🎯 3. Calculated Fields & Table Calculations: Employ calculated fields to customize your data analysis. Master table calculations (running totals, percent differences) to add more depth to your reports. 🌍 4. Filters & Parameters: Gain proficiency in applying filters, context filters, and creating dynamic views with parameters. This makes your dashboards interactive and user-friendly. 📈 5. Data Visualization Best Practices: Focus on storytelling with data! Simplicity and clarity are key – avoid clutter, choose the right visualizations, and ensure that your insights are easily understood. 📉 6. Advanced Analytics: Understand trend lines, forecasting, reference lines, and statistical tools within Tableau to provide data-driven insights that go beyond the surface. 🔧 7. Performance Optimization: Optimize dashboards for performance. Know how to reduce load times, use extracts efficiently, and manage large datasets effectively. 📚 8. Practice Real-Life Scenarios: Work on real datasets or participate in Tableau Public challenges to build a strong portfolio. Hands-on experience is invaluable! 👨‍💼 9. Certification: Consider earning a Tableau certification. It’s a great way to validate your skills and stand out in the job market. 🔑 10. Stay Curious & Keep Learning: The data world is ever-evolving! Stay updated with Tableau’s latest features, new visualization trends, and connect with the community. Remember, landing a job with Tableau isn’t just about mastering the tool – it's about solving real-world problems with data. Best of luck in your learning journey! 💼✨ #DataAnalytics #TableauTips #CareerInData #LearningPath #JobSkills #DataScience #DataAnalytics #DataVisualization #Tableau #BusinessIntelligence #Analytics #CareerGrowth #TechCareers #TableauTips #LearnData #DataDriven #Dashboards #DataCommunity #TechSkills #DataInsights #JobSeekers #LearningAndDevelopment #ProfessionalDevelopment #CareerAdvice #DataStorytelling

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Usage of Container Objects in Tableau I’ve been thinking about when to use floating containers in Tableau. When I started learning tableau, I found them helpful for arranging visualizations easily. But over time, I realized they could disrupt the layout of other dashboard components. After experimenting with a mix of floating and tiled containers, I noticed slight improvements, but the results were still not ideal. Now, I use only tiled containers. Although it was a bit tricky at first, I find them better for organizing and viewing dashboards effectively. That said, I still wonder—if floating containers aren't commonly used, why do we have them as an option in Tableau? Do they have a purpose that I’m missing? I’d love to hear your thoughts, suggestions, or any resources you can share on this topic! #Tableau #DataVisualization #DashboardDesign #TableauTips #DataAnalytics #FloatingContainers #TiledContainers #DataScience #Analytics #DataStorytelling #BusinessAnalytics #TableauGeeks #DataNerd #DataGeeks

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I am starting a new videos series on my YouTube channel : Think DAX 🚀🚀 This is one of the most requested topic by students over last few months . I think I can bring a new perspective on how to write DAX. Similar to SQL we will solve scenarios based DAX questions with multiple approaches. Along with that we will solve the question in SQL as well. We will try to relate SQL and DAX solutions wherever possible. You can send DAX related interview questions to me and I will feature them on my playlist. Email id in the comments section. Hope you guys are excited for this one.☝️ #powerbi #dax #sql

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This's a helpful guide for your job search.

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What's the right mix of reactive & proactive work for a Data Analyst? - It's not 100% reactive / 0% proactive - It's not 0% reactive / 100% proactive The right mix depends on lots of things! Some scenarios where it's good to lean into reactive analytics: - You're new to a domain or industry - You get an urgent, non-typical request for help - You have a new stakeholder and want to build trust In these cases you don't have prior expertise to lean on to be proactive, or someone just really needs your help unexpectedly and quickly. Conversely, you need to be proactive when: - You have deep domain knowledge - You understand the business strategy really well - You have a strong relationship with your stakeholder - Your stakeholders don't know what's possible with analytics The common theme here: you (the analyst) know the context of your work and what's possible. You know how analytics could help the company achieve its goals. What mix of reactive/proactive work are you currently doing? #data #analytics

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After taking more than 500+ interviews for data science and analytics roles over the past 4-5 years, we've noticed that 8 out of 10 candidates fail in the first or second round. But what is the reason? So there are 2 main reasons because of which people are not able to clear their data science analytics interviews. In this video, We’ll discuss the two main reasons why candidates struggle and how you can overcome these obstacles to get the job. 1️⃣ Fundamentals Are Not Clear- Many candidates lack a strong grasp of the fundamental concepts in data science and analytics. This includes areas like statistics and programming. 2️⃣ Lack of Problem-Solving Skill- Data science requires strong analytical thinking to break down complex problems into manageable parts and derive solutions. So because of these 2 problems we have started Analytics Shiksha! Our mission is to: Make your fundamentals strong and make you understand the problem solving skill and build mindset through frameworks which we will learn in our super30 data analytics course. #dataanalytics #datascience #datascienceanalytics #datascienceinterview #dataanalyticsinterview #techcareer #dataananalyst #datascientist #datascienceskills #dataanalyticsskills #dataanalyticscourse #datasciencejobs #dataanalystjobs #artofproblemsolving

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Tiger Analytics Data Analyst Interview Experience (1-3 Years Experience) 👇 1. Tell me about your background and current role. 2. Rate your SQL proficiency from 1 to 10 and justify your rating. 3. Write an SQL query to find the highest-paid employee in each department who joined in the last 3 years. 4. Explain window functions in SQL and provide an example of ranking products by quarterly revenue. 5. How would you merge two DataFrames in Python and calculate the average order value for each customer? 6. Write a Python code snippet to calculate cumulative revenue using Pandas. 7. Describe a project where you used Power BI for data visualization. 8. What challenges have you faced while working with large datasets, and how did you solve them? 9. How would you resample a time series dataset to get monthly totals in Python? 10. Discuss a time when you faced difficulties during a project and how you overcame them. 11. How do you handle missing data in a dataset, and what methods do you use for imputation? 12. Explain how you would optimize a slow SQL query. 13. Describe your experience with data cleaning and the steps you follow. 14. How would you validate the accuracy of a data model or report? 15. What key metrics would you track when analyzing the performance of a business using data? Now, if you’re looking for a platform to help you with your end-to-end interview preparation, you can check out the programs by Bosscoder Academy. Check the complete program here: ✨Here’s the exciting part get 30% off with a festive discount on all of their programs ✨ They can help you with → https://lnkd.in/ghchysUz ✅ Structured curriculum to learn Data tools, Machine Learning, Deep Learning and more. ✅ Personal mentorship from experienced Data professionals. ✅ Live classes, project-based learning, and access to a talented community of people with similar goals of upskilling. Also, get placement support post-completion of the program #DataAnalytics #InterviewQuestions #collab

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