Bonny Benny

Mastering Tableau: Essential Interview Questions and Answers Dive deep into Tableau with our comprehensive guide to mastering the platform. This article features crucial interview questions and expert answers that will help you prepare effectively for Tableau-related job interviews. Whether you're a beginner or an experienced user looking to enhance your skills, this resource provides valuable insights and practical knowledge to excel in the world of data visualization and analytics. Pdf credit goes to respective owner Follow for more resources #Tableau #interview #qna #analytics

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Instantly add 159 Tableau color palettes... 1. Download my color palettes - https://lnkd.in/earBQuhC 🎨 2. Go to My Tableau Repository 📂 3. Paste the file 🖇 Done! NOTE: If you already have custom colors, you'll need to: 1. Download my palettes - https://lnkd.in/earBQuhC 2. Copy everything between the <preferences> and </preferences> tags 3. Open your Preferences.tps file with a text editor 4. Paste what you copied in step 2 in between the preferences tags Enjoy!

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Enhance your categorical data visualizations with statistical insights! The ggpiestats() function from the ggstatsplot package creates pie charts for categorical or nominal variables, adding results from contingency table analysis directly into the plot’s subtitle. ✔️ Effective Categorical Visualization: Displays proportions of categories clearly, helping you visualize distribution and compare groups. ✔️ Built-In Statistical Testing: Includes results from Pearson’s chi-squared test for between-subjects designs or McNemar’s chi-squared test for within-subjects designs, with key insights shown in the subtitle. For single categorical variables, it also displays results from a one-sample proportion test (goodness of fit). ✔️ Customizable Design: Adjust colors, labels, and layout to fit your data presentation style, ensuring a polished and informative chart. ✔️ Seamless Integration with ggplot2: Works effortlessly with ggplot2, letting you customize and refine your pie charts using familiar syntax. The visualization shown here is from the package website, illustrating how ggpiestats() effectively combines visual presentation with statistical testing: https://lnkd.in/e2upCduU Want to master ggplot2 and its powerful extensions for impactful visualizations? Join my online course, “Data Visualization in R Using ggplot2 & Friends,” starting on November 25, 2024! Learn more: https://lnkd.in/ehRrzdJ7 #tidyverse #dataviz #datavisualization #bigdata #analysis

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Highly recommended for anyone who would like to sharpen their IT skills which is utmost important in period 9 FENG SHUI. Tai ChooTack - The ExcelSifu is author to two bestseller books. Do connect with him to find out more. #technology #fengshui #amandaleong #metaphysics

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SQL for Data Analysis Cheat Sheet Key elements of our SQL for Data Analysis Cheat Sheet include: SELECT Statement: Retrieve data from tables using the SELECT statement and learn about aliases, DISTINCT, and WHERE clauses for filtering data. JOINs: Master the art of combining data from multiple tables using INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN operations. Aggregation Functions: Calculate summary statistics and aggregate data using functions like COUNT, SUM, AVG, MIN, and MAX. GROUP BY and HAVING: Group data based on common attributes and filter groups using the GROUP BY and HAVING clauses. Subqueries: Nest queries within queries to perform complex operations and retrieve specific subsets of data. Window Functions: Analyze data within partitions using window functions like ROW_NUMBER, RANK, DENSE_RANK, and NTILE. Common Table Expressions (CTEs): Simplify complex queries and improve readability by using CTEs to break down queries into logical components. Conditional Logic: Use CASE statements to perform conditional operations and customize query results based on specific criteria. Data Manipulation: Insert, update, and delete data from tables using INSERT, UPDATE, and DELETE statements. Data Definition: Learn about creating and modifying database objects such as tables, indexes, and views using DDL statements. With our SQL for Data Analysis Cheat Sheet by your side, you'll be equipped to tackle any data analysis challenge with confidence and precision. Download your copy today and take your analytical skills to the next level! ✅ Get any Data science training videos, https://lnkd.in/gQVwVNSG ✅ Subscribe to our Youtube channel: https://lnkd.in/gD54ZjUh ✅ P.S. Want to Upskill your Data Science workforce? Check out our course catalog for corporate training, https://lnkd.in/dYipv_Qm ❤️ #datasciencecommunity --------------------------------- 💥 I am Data Science Learner Community. 🔺Supporting you to Upskill in DataScience skills. 🔺Providing Corporate IT training, consulting, mentoring with a team of Sr. Data Science Certified Experts. ☎️ CONTACT: +1 (515) 309-7846 (or) Email - [email protected] ✅ WhatsApp us for more info: https://wa.me/15153097846 More information: 🌍 Website: https://www.zarantech.com/ 🌍 Instagram: https://lnkd.in/gYJct6D 🌍 Linkedin: https://lnkd.in/gWjHpzm 🌍 Facebook: https://lnkd.in/gprZE9S 🌍 Quora: https://lnkd.in/g2Fy2v39 Like this post? Want to see more? 🔔Ring it on my Profile 🔹Follow #DataScienceLearnerCommunity 🔹Follow our Linkedin Learner community, https://lnkd.in/gMZw7-MN #datascientists #datascience #machinelearning #algorithms #artificialintelligence #python #community #training #tableau  #sql #jobopportunities #success #job #chatgpt4

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𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗔𝗻𝘀𝘄𝗲𝗿𝘀 This pdf will help you to clear the doubts about interview questions and will also help you to crack the interview. 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀: 1. What is Power BI? 2. What are the key components of Power BI? 3. What is Power BI Desktop? 4. What is the Power BI Service? 5. What are Power BI dashboards? 6. What is a Power BI report? 7. What is DAX? 8. What are some common data sources for Power BI? 9. How do you refresh data in Power BI? 10. What are Power BI visualizations? 11. What is Power Query? 12. What is the use of the Power BI gateway? 13. What are calculated columns in Power BI? 14. What are measures in Power BI? 15. What is a data model in Power BI? 16. How do you create a relationship between tables in Power BI? 17. What is a slicer in Power BI? 18. What is a Power BI template? 19. What is the use of the 'Get Data' feature in Power BI? 20. What is drill through in Power BI? 21. How can you share Power BI reports? 22. What is a custom visual in Power BI? 23. What are Power BI apps? 24. What is a KPI in Power BI? 25. How do you handle large datasets in Power BI? 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀: 26. What is row-level security (RLS) in Power BI? 27. What is a calculated table in Power BI? 28. How do you optimize Power BI reports for performance? 29. What are Power BI workspaces? 30. What is Power BI Embedded? 31. What is the purpose of the Power BI REST API? 32. How do you create a custom date table in Power BI? 33. Explain the use of the CALCULATE function in DAX. 34. What are hierarchies in Power BI? 35. What is a dataflow in Power BI? 36. Explain the use of the FILTER function in DAX. 37. What are the benefits of using Power BI Premium? 38. What is incremental refresh in Power BI? 39. How do you handle many-to-many relationships in Power BI? 40. What are parameters in Power BI? 41. How do you use the SWITCH function in DAX? 42. Explain the concept of context transition in DAX. 43. What is the purpose of the ALLEXCEPT function in DAX? 44. How do you create a measure to calculate year-over-year growth in Power BI? 45. What is Power BI Report Server? 46. Explain the use of the RELATED function in DAX. 47. How do you implement dynamic titles in Power BI visuals? 48. What is the purpose of the VALUES function in DAX? 49. How do you handle circular dependencies in DAX? 50. Explain the difference between DirectQuery and Import mode. 𝗜𝗳 𝘆𝗼𝘂 𝗳𝗼𝘂𝗻𝗱 𝘁𝗵𝗶𝘀 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲, 𝗱𝗼𝗻'𝘁 𝗳𝗼𝗿𝗴𝗲𝘁 𝘁𝗼 𝗵𝗶𝘁 "𝗟𝗶𝗸𝗲" 𝗯𝘂𝘁𝘁𝗼𝗻 𝗮𝗻𝗱 𝘀𝗵𝗮𝗿𝗲 𝗶𝘁 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗻𝗲𝘁𝘄𝗼𝗿𝗸.

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Do you know there's a new mode in Microsoft PowerBI apart from Import mode & Direct Query Mode? Direct Lake—a new game changer feature in Power BI (thanks to Fabric) that enhances how data is accessed and queried. Here's the explanation in simple terms: ➡️ How it Works? - Normally, in Direct Query or Import Mode, Power BI sends queries to a SQL Endpoint for data. - In Direct Lake Mode, Power BI can skip the SQL Endpoint and directly query delta-parquet files stored in the Lakehouse. This means: 1. Faster query performance (similar to Import Mode). 2. No need to refresh the data since it updates automatically, like in Direct Query Mode. ➡️ What’s the magic trick behind Direct Lake Mode? - The parquet file format (used in the Lakehouse) is already optimized for analytics, making it easy for Power BI to use. - Instead of loading data into "traditional" Power BI storage (like Analysis Services), Power BI works directly with these parquet files. - Power BI uses V-Order, a special way of organizing parquet files for even better performance. - All Fabric engines (like Synapse or Data Factory) automatically write parquet files in V-Order. ➡️ Any prerequisites to use Direct Lake? - You will need a Power BI Premium or Fabric F Capacity. - A Lakehouse and an associated SQL Endpoint. - Delta tables saved as V-Ordered parquet files. ➡️ Benefits: - Combines the performance of Import Mode with the real-time updates of Direct Query. - No extra steps or data refresh required—data flows naturally! ➡️ Any Caution? In some cases, Power BI might still need to fall back to Direct Query Mode when certain queries or conditions arise. P.S: I will be starting my live classroom bootcamp of Microsoft Fabric really soon and will be teaching everything from Basic To Advance👩‍💻👩‍💻 Fill the below form to reserve your spot before the launch⬇️ https://bit.ly/4i4zSjF Shashank Mishra 🇮🇳 Rahul Shukla Grow Data Skills #microsoft #fabric

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Is it necessary to learn SQL before you start learning Tableau or Power BI ? Personally I would highly recommend it because many of the concepts like filters , joins , aggregation , grouping , date and string functions etc. are the same in SQL and BI tools. So it becomes very easy to conquer any BI tool if you are already proficient with SQL. And for that reason I am pleased to announce the addition of 4 hours of SQL refresher self paced videos to my upcoming Tableau Bootcamp (zero to hero) starting on June 1st. This will help learners to become job ready with the amazing combination of SQL and Tableau😻😻 It also makes my life easy as I can easily relate to SQL while explaining the concepts in Tableau. So you have close to 2 weeks time to cover the 4 hours of SQL content. Along with that you will have assignments to practice and their solutions. A special discount of 10 percent is valid for a limited time . Use code EARLY10. Link to register for the Tableau for data analytics LIVE bootcamp in the comments section. #sql #tableau #analytics

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3 ways to implement row-level security (RLS) on Tableau: 1. Manual or dynamic user filter For very small environments, the easiest route is to just create user filters in Tableau. The problem is that maintenance gets heavy quickly as data and users scale. 2. RLS in the database Teams can implement RLS in databases themselves. For older versions of Tableau, they'll need to use live queries in this case. Newer versions can support database RLS on extracts, but they require teams to create and maintain an entitlements table. 3. RLS with Satori Satori allows Tableau users to implement RLS even while working in data extract. Using an entitlement file with predefined security policies and permissions, Satori allows data teams to enforce policies automatically, on Tableau and across your data stores. Adam Gaulding's recent blog post goes in depth into how Satori helps Tableau users automate RLS when using data extract. Link below 👇

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The 6 most important skills for data analysts explained in simple terms: 1 - Business Acumen How well do you understand the goals and objectives of the business you're working in? If you're not working, do you have a good idea what's important to businesses? How can data analysis contribute to overall business strategy? 2 - Technical Know-How Do you have enough technical skill to handle anything that comes your way? Are there new options out there that you could recommend to your organisation? 3 - Communication Skills How do you articulate complex data findings to non-technical stakeholders? How do you handle misunderstandings or conflicts arising from data interpretations? Can you structure your communication to be both informative and inspiring? 4 - Stakeholder Management How do you identify your most important stakeholders and find out what's most important to them? How do you effectively prioritise your work making sure you don't burn out and deliver the best possible output? 5 - Critical Thinking How do you approach ambiguous or poorly defined problems? How do you evaluate the reliability of data sources and the validity of your analysis? 6 - Presentation Skills What are the key elements of a compelling data analysis presentation? How do you tailor your presentations to different audiences, such as technical and non-technical stakeholders? Which are you going to work on next? Let me know in the comments below👇 If you got something from this, consider following me: Tom Mitchell for more data related content every day.

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How to create a great Dashboard and actionable management report Credits to Bojan Radojicic  follow him for more finance insights! ~~~~~~~~~ 𝙏𝙝𝙚 𝙤𝙧𝙞𝙜𝙞𝙣𝙖𝙡 𝙥𝙤𝙨𝙩 𝙞𝙨 𝙝𝙚𝙧𝙚: Watch how to create great Dashboard and actionable management report 1️⃣ 𝗦𝘁𝗲𝗽 𝟭: 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 → Prepare functional categories for reporting → Like cost centers, types of revenues, asserts of liabilities → Allocate the cost / profit centers to the general ledger account in your CoA 2️⃣ 𝗦𝘁𝗲𝗽 𝟮: 𝗗𝗮𝘁𝗮 𝗶𝗻𝗽𝘂𝘁 → Create or adjust your budget data to be aligned with cost/profit centers. → Split annual budget to the periods, months or quarters → Export your trial balance with these columns: → GL Account, Description, Amount, and just input last day of reporting period. → Now use VLOOKUP or XLOOKUP function to input cost/profit center for each line 3️⃣ 𝗦𝘁𝗲𝗽 𝟯: 𝗥𝘂𝗻 𝗿𝗲𝗽𝗼𝗿𝘁𝘀 → Create beautiful report that automatically takes a data → Create pivot table for selecting a period → Check actual figures variance to last period or budget → Create beautiful dashboard for making right decisions ~~~~~~~~~ 📌 If you want to upgrade your career in finance, start with our Corporate Finance Modeling Masterclass: 💠 40 Lessons 💠 7 Hours of video course 💠 50+ Excel modeling sheets 💠 330 pages of PDF modeling tutorial Start here: https://bojanfin.com/ ~~~~~~~~~

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Power BI Interview Question: what is Role-Playing Dimensions? A Complete Guide- https://lnkd.in/gHkRTXNb #PowerBI #PowerBIInterviewQuestions #DataWarehousing #RolePlaying #InterviewQuestions #DataAnalytics #RolePlayingDimension #DataIntegrity #DataManagement #TechInterviews #MicrosoftFabric #DataPlatform

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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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15 Resources Every Data Analyst Needs in 2025 📊 Looking to level up your skills and stay ahead of the curve? 🚀 (Data Analysts – this one’s for you!) Resources: ↳ Statistics Tutorial for Beginners: Start strong with the foundations of statistics, the backbone of data analysis. ↳ Microsoft Excel Video Training: Excel is still a key tool for many data analysts. Learn how to crunch numbers like a pro! ↳ Power BI Training by Microsoft: Gain insights from your data with Power BI, a leading tool for data visualization and business intelligence. ↳ Tableau Public: Learn how to build interactive dashboards and stunning visuals with this free Tableau version. ↳ Tableau Community Projects: Collaborate and learn with other data enthusiasts through hands-on projects and challenges. ↳ Google Cloud Datasets: Access real-world datasets to hone your skills in data analysis, machine learning, and cloud computing. ↳ Awesome Public Datasets: Explore a treasure trove of public datasets perfect for practicing data analysis. ↳ Stanford SQL Course: Master SQL, one of the most in-demand skills for data analysts, through this comprehensive course. ↳ Python for Data Science: Unlock the power of Python with this course designed for data scientists. ↳ Hands-on Machine Learning: Dive into machine learning with real-world examples and code. ↳ Chat-GPT or Any LLM: Leverage language models for advanced data processing and analysis. ↳ Tableau for Kids: Start young! A fun way to introduce kids to data analysis and visualization. ↳ Microsoft Learning: ↳ Tableau Free Training: ↳ lunartech.ai DM me I will Send my "Data Integration" cheat-book. Follow me for more. #datascience #visualization #data #powerbi #dataanalysis #businessintelligence #tableau #datavisualization

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6 statistical methods that can be used for A/B Testing (and when to use them). A/B Testing is a staple of data science and data analyst interviews. And it's the Number 1 technique that companies benefit from in improving customer revenue. So here's a 6 of the most common stat methods used in A/B testing. Let's dive in. 1. Z-Test (Standard Score Test): Ideal for large sample sizes (typically over 30) and when the population variance is known. Compares the mean of two groups to see if they are different from each other. Often used in conversion rate optimization, click-through rates. 2. T-Test (Student's T-Test): Similar to Z-test but preferred when dealing with smaller sample sizes (less than 30) and when the population variance is unknown. 3. Welch's T-test: This is an adaptation of the Student's t-test. Used when the two groups have unequal variances and/or unequal sample sizes, which is a common scenario in real-world data. 4. Mann-Whitney U Test: Non-parametric alternative to the T-test; used when data does not follow a normal distribution. Compares differences between two independent groups when the dependent variable is either ordinal or continuous, but not normally distributed. 5. Fisher’s Exact Test: Examines the significance of the association between two kinds of classifications. Preferred for small sample sizes, particularly in 2x2 tables. 6. Pearson's Chi-Squared (χ²) Test: This test is primarily used for categorical data in a contingency table format (like a 2x2 table). It is most effective when comparing two or more groups in terms of a categorical variable (like pass/fail, click/no-click). There you have it- my top 6 statistical tests for A/B Testing. The next problem you'll face is how to apply data science to business. I'd like to help. I’ve spent 100 hours consolidating my learnings into a free 5-day course, How to Solve Business Problems with Data Science. It comes with: 300+ lines of R and Python code  5 bonus trainings 2 systematic frameworks 1 complete roadmap to avoid mistakes and start solving business problems with data science, TODAY. 👉 Here it is for free: https://lnkd.in/e_EkiuFD

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