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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 Rituraj Singh Gour for more.

Meesho Data Analyst interview experience (0-3) - Power BI Questions: 1. Explain the concept of context transition in DAX and provide an example. 3. How would you optimize a complex Power BI report for faster performance? 4. Describe the process of creating and using calculation groups in Power BI. 5. Explain how you would handle large datasets in Power BI without compromising performance. 6. What is a composite model in Power BI, and how can it be used effectively? 7. How does the USERELATIONSHIP function work, and when would you use it? 8. Describe how to use Power Query M language for advanced data transformations. 9. 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.

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.

Meesho data analyst interview experience (0-3 year)(sql,powerbi) . . POWER BI: 4 questions 1.⁠ ⁠What does Filter context in DAX mean? 2.⁠ ⁠Explain how to implement Row-Level Security (RLS) in Power BI. 3.⁠ ⁠Describe different types of filters in Power BI. 4.⁠ ⁠Explain the difference between 'ALL' and 'ALLSELECTED' in DAX. SQL: 4 questions 1.⁠ ⁠What are SQL transactions, and how do you use BEGIN, COMMIT, and ROLLBACK? 2.⁠ ⁠How do you ensure data integrity using transactions? 3. Write a query to get the customers who made transactions in two consecutive months in 2023. 4. Write a query to find the cumulative sales department-wise. Tips to solve these questions: 🚩 Get hands-on practice: Work on a real dataset to practice DAX formulas and SQL queries. 🚩 Be prepared for follow-up questions: Interviewers will ask about the logic behind your answers, so practice explaining your approach clearly through mock interviews. For this kind of hands-on practice, try connecting with experienced Data Analysts

Attention all businesses in need of data-driven insights! As a proficient Data Analyst, I specialize in extracting valuable insights from complex datasets. With expertise in data analysis and visualization, I offer tailored solutions to help you make informed decisions. Let's collaborate to unlock the potential of your data! #DataAnalysis #DataVisualization #Freelance

📊 After extensive research and hands-on experience with various tools, I’ve crafted a roadmap that has worked wonders for me as a data analyst. Here it is, step-by-step! 🚀 Data Analyst Roadmap 🗺️ Step 1: Excel 📝 - Master basic and advanced Excel functions - Learn pivot tables, charts, and formulas Step 2: BI Tools 📈 - Get proficient with Business Intelligence (BI) tools like Power BI or Tableau - Focus on creating dashboards and reports Step 3: Data Visualization 🎨 - Understand the principles of data visualization - Use tools like Tableau, Power BI, or even advanced Excel Step 4: SQL 🗃️ - Learn SQL for data querying and manipulation - Practice writing complex queries to extract meaningful insights Additional Skills 📚 A: Python 🐍 - Learn Python programming for data analysis - Focus on libraries like Pandas, NumPy, and Matplotlib B: Statistics 📊 - Understand basic and advanced statistical concepts - Apply these concepts to analyze and interpret data ### Final Goal 🎯 Data Analyst 🧑💼 - Integrate all the above skills - Perform data analysis, create reports, and derive insights 💡 **Important:** Data Visualization is crucial. Make sure to present data in a clear and compelling manner! Happy analyzing! 😊🔍

Hello Connections 😊 🚀 Thrilled to share a snapshot of the typical steps a Data Analyst follows in transforming raw data into actionable insights! 📊 Whether you're just starting out in data analytics or are a seasoned expert, revisiting the fundamentals can spark new ideas and innovations. 1. Understand the Business Question Every data analysis begins with understanding the core business questions. What is the problem or decision the business is facing? This step sets the direction for all that follows. 2. Data Collection Data collection involves gathering the necessary data from various sources, whether it's internal databases, external APIs, or even manual data entry. Ensuring the data's relevance and accuracy is key here. 3. Data Cleaning Perhaps the most crucial step—data cleaning. This involves handling missing values, removing duplicates, correcting errors, and standardizing data formats. Clean data is a prerequisite for reliable analysis. 4. Data Exploration Data exploration, or exploratory data analysis (EDA), uses statistical summaries and visualizations to understand patterns, detect outliers, and form hypotheses about the data. 5. Data Analysis Using statistical or machine learning techniques, data analysts extract insights and answers to the business questions. This can involve anything from simple descriptive statistics to complex predictive models. 6. Data Visualization Visual representations of data help in communicating findings effectively. Tools like Tableau, PowerBI, or even Python's Matplotlib make this task easier, helping stakeholders quickly grasp the conclusions. 7. Interpretation and Decision Making The final step is about interpreting the results and translating them into actionable business decisions or recommendations. The success of this step relies heavily on the analyst's ability to connect data insights with business strategy. 8. Reporting Communicating findings, often through detailed reports or presentations, is critical. It ensures that stakeholders understand the insights and can act upon them. By iterating through these steps, data analysts empower businesses to make data-driven decisions that can drastically improve performance and competitive edge. #DataAnalytics #BusinessIntelligence #DataScience #fresherjobs

Most Important Questions I Was Asked in My Data Analyst Interviews These are the skills I was asked questions about in my four interviews. 1. Excel - Pivot Tables - Vlookup and Index Match - Conditional Formatting - Text Functions 2. SQL - Group BY and Have BY - JOINS 3. Tableau - Data Cleaning in Tableau - Tableau Dashboarding Questions asked about my projects and portfolio - How did you get the dataset for the project? - How did you clean the data set, which tool did you use and what algorithms did you use if any? - How would you have created the same project on SQL instead of Excel? - Other specific questions Case Studies Given to me in my Interviews - Zomato Case Study which had Restaurants and Customer Data (SQL) - Sales Data for an E-commerce Company (Excel) Generic Questions asked - How would you go about measuring the performance of our company? - Tell me some most important metrics for an Ed-Tech company - Standard Deviation vs Variance - Give some examples of Quantitative vs Qualitative Data - What are the various steps involved in any analytics project?

I've been getting a lot of messages from freshers asking how to start a career in data analytics. I remember being in your shoes, so I've put together a guide to help you take your first steps into Data Analytics field. 1. Learn Excel 2. Learn SQL 3. Learn a BI tool (Tableau or Power BI) 4. Create 1 Excel project 5. Create 1 SQL project 6. Create 3 dashboards 7. Create a data portfolio 8. Add projects to your resume 9. Link portfolio on LinkedIn and resume 10. Practice data related STAR questions 11. Set time to connect with recruiters 12. Set time to connect with others within a company 13. Set time to connect with other Data Analysts Get through this as fast as possible so you can spend all of your time applying Applying is the hardest and most time consuming part So you need to put all your energy into that, NOT perfecting tools I have created 100-Day Roadmap & Resources for Data Analyst 👇👇 https://lnkd.in/d2rPVkUr Follow Data Analytics for more resources Save this post for future reference ✅ Hope it helps :)

✅Welcome to the Exciting World of Data Analytics✅ As a fresher stepping into the field of data analytics, you are about to embark on a journey filled with immense growth, problem-solving, and creativity. In today's data-driven world, your role as a data analyst will allow you to uncover valuable insights, make informed decisions, and drive real-world impact. While it might seem overwhelming at first, remember that every expert was once a beginner. With curiosity, determination, and the right skills, you can excel and become a key player in this thriving field. To succeed as a data analyst, here are the key skills you should focus on developing: ➡️Strong Analytical Mindset: Cultivate the ability to think critically, identify patterns, and draw meaningful conclusions from data. ➡️Proficiency in Excel: Excel is a fundamental tool for data manipulation, and mastering it will help you handle large datasets efficiently. ➡️Data Visualization: Learn tools like Power BI or Tableau to present data in clear, compelling visual formats that drive decision-making. ➡️Statistical Knowledge: Understanding statistics helps in analyzing data trends and making accurate predictions. ➡️SQL: SQL is essential for querying databases, so mastering this language will help you extract and manipulate data effectively. ➡️Programming Skills (Python/R): Familiarize yourself with programming languages like Python or R for data analysis, automation, and handling large datasets. ➡️Problem-Solving Ability: Sharpen your ability to approach business problems methodically and provide data-driven solutions. ➡️Communication Skills: Being able to translate complex data into understandable insights is critical to influencing decisions and strategies. #dataanalytics #dataanalysis #learndata #DataDrivenDecisions #SalesAnalytics #CustomerInsights #PowerBI #BusinessIntelligence #DataVisualization #SalesOptimization #AnalyticsDashboard #data #datascience #dataengineering #databricks #datacamp #dataenthusiasts #sql #analytics #learning #bigdata #datascientist #excel