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Tableau Cheat Sheet ✅ This Tableau cheatsheet is designed to be your quick reference guide for data visualization and analysis using Tableau. Whether you’re a beginner learning the basics or an experienced user looking for a handy resource, this cheatsheet covers essential topics. 1. Connecting to Data - Use *Connect* pane to connect to various data sources (Excel, SQL Server, Text files, etc.). 2. Data Preparation - Data Interpreter: Clean data automatically using the Data Interpreter. - Join Data: Combine data from multiple tables using joins (Inner, Left, Right, Outer). - Union Data: Stack data from multiple tables with the same structure. 3. Creating Views - Drag & Drop: Drag fields from the Data pane onto Rows, Columns, or Marks to create visualizations. - Show Me: Use the *Show Me* panel to select different visualization types. 4. Types of Visualizations - Bar Chart: Compare values across categories. - Line Chart: Display trends over time. - Pie Chart: Show proportions of a whole (use sparingly). - Map: Visualize geographic data. - Scatter Plot: Show relationships between two variables. 5. Filters - Dimension Filters: Filter data based on categorical values. - Measure Filters: Filter data based on numerical values. - Context Filters: Set a context for other filters to improve performance. 6. Calculated Fields - Create calculated fields to derive new data: - Example: Sales Growth = SUM([Sales]) - SUM([Previous Sales]) 7. Parameters - Use parameters to allow user input and control measures dynamically. 8. Formatting - Format fonts, colors, borders, and lines using the Format pane for better visual appeal. 9. Dashboards - Combine multiple sheets into a dashboard using the *Dashboard* tab. - Use dashboard actions (filter, highlight, URL) to create interactivity. 10. Story Points - Create a story to guide users through insights with narrative and visualizations. 11. Publishing & Sharing - Publish dashboards to Tableau Server or Tableau Online for sharing and collaboration. 12. Export Options - Export to PDF or image for offline use. 13. Keyboard Shortcuts - Show/Hide Sidebar: Ctrl+Alt+T - Duplicate Sheet: Ctrl + D - Undo: Ctrl + Z - Redo: Ctrl + Y 14. Performance Optimization - Use extracts instead of live connections for faster performance. - Optimize calculations and filters to improve dashboard loading times. Best Resources to learn Tableau: https://topmate.io/analyst/890464 Hope you'll like it Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Q1: How would you analyze data to understand user connection patterns on a professional network? Ans: I'd use graph databases like Neo4j for social network analysis. By analyzing connection patterns, I can identify influencers or isolated communities. Q2: Describe a challenging data visualization you created to represent user engagement metrics. Ans: I visualized multi-dimensional data showing user engagement across features, regions, and time using tools like D3.js, creating an interactive dashboard with drill-down capabilities. Q3: How would you identify and target passive job seekers on LinkedIn? Ans: I'd analyze user behavior patterns, like increased profile updates, frequent visits to job postings, or engagement with career-related content, to identify potential passive job seekers. Q4: How do you measure the effectiveness of a new feature launched on LinkedIn? Ans: I'd set up A/B tests, comparing user engagement metrics between those who have access to the new feature and a control group. I'd then analyze metrics like time spent, feature usage frequency, and overall platform engagement to measure effectiveness.

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Here are 30 most asked SQL questions to clear your next interview - ➤ 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 1. Calculate the moving average of sales for the past 3 months. 2. Assign a dense rank to employees based on their salary. 3. Retrieve the first and last order date for each customer. 4. Find the Nth highest salary for each department using window functions. 5. Determine the percentage of total sales contributed by each employee. ➤ 𝗖𝗼𝗺𝗺𝗼𝗻 𝗧𝗮𝗯𝗹𝗲 𝗘𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻𝘀 (𝗖𝗧𝗘) 1. Use a CTE to split a full name into first and last names. 2. Write a CTE to find the longest consecutive streak of sales for an employee. 3. Generate Fibonacci numbers up to a given limit using a recursive CTE. 4. Use a CTE to identify duplicate records in a table. 5. Find the total sales for each category and filter categories with sales greater than a threshold using a CTE. ➤ 𝗝𝗼𝗶𝗻𝘀 (𝗜𝗻𝗻𝗲𝗿, 𝗢𝘂𝘁𝗲𝗿, 𝗖𝗿𝗼𝘀𝘀, 𝗦𝗲𝗹𝗳) 1. Retrieve a list of customers who have placed orders and those who have not placed orders (Full Outer Join). 2. Find employees working on multiple projects using a self join. 3. Match orders with customers and also display unmatched orders (Left Join). 4. Generate a product pair list but exclude pairs with identical products (Cross Join with condition). 5. Retrieve employees and their managers using a self join. ➤ 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀 1. Find customers whose total order amount is greater than the average order amount. 2. Retrieve employees who earn the lowest salary in their department. 3. Identify products that have been ordered more than 10 times using a subquery. 4. Find regions where the maximum sales are below a given threshold. ➤ 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 1. Calculate the median salary for each department. 2. Find the total sales for each month and rank them in descending order. 3. Count the number of distinct customers for each product. 4. Retrieve the top 5 regions by total sales. 5. Calculate the average order value for each customer. ➤ 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 1. Write a query to find duplicate values in an indexed column. 2. Analyze the impact of adding a composite index on query performance. 3. Identify columns with high cardinality that could benefit from indexing 4. Compare query execution times before and after adding a clustered index. 5. Write a query that avoids the use of an index to test performance differences.

Top 10 Advanced SQL Interview Questions and Answers 1. What is a Common Table Expression (CTE), and when would you use it? A Common Table Expression (CTE) is a temporary result set that can be referred to within a SELECT, INSERT, UPDATE, or DELETE statement. Example:

    
           WITH SalesCTE AS (
       SELECT SalespersonID, SUM(SalesAmount) AS TotalSales
       FROM Sales
       GROUP BY SalespersonID
   )
   SELECT * FROM SalesCTE WHERE TotalSales > 5000;
   {}
    

2. How do you optimize a query with a large dataset? - Use proper indexes. - Avoid SELECT *; only retrieve required columns. - Break down complex queries using temporary tables or CTEs. - Analyze query execution plans. 3. What is the difference between RANK(), DENSE_RANK(), and ROW_NUMBER()? - RANK(): Skips ranking if there’s a tie (e.g., 1, 2, 2, 4). - DENSE_RANK(): Does not skip ranks after a tie (e.g., 1, 2, 2, 3). - ROW_NUMBER(): Assigns unique numbers sequentially, regardless of ties. 4. How do you find duplicate records in a table?

    
           SELECT ColumnName, COUNT(*)
   FROM TableName
   GROUP BY ColumnName
   HAVING COUNT(*) > 1;
   {}
    

5. What is the difference between INNER JOIN and LEFT JOIN? - INNER JOIN: Returns records that match in both tables. - LEFT JOIN: Returns all records from the left table, and matching records from the right table (NULL if no match). 6. Explain window functions and provide an example. Window functions operate on a set of rows related to the current row, without collapsing them into a single output. Example:

    
           SELECT EmployeeID, Salary, 
          RANK() OVER (PARTITION BY DepartmentID ORDER BY Salary DESC) AS Rank
   FROM Employees;
   {}
    

7. What are the different types of indexes in SQL? - Clustered Index: Reorders the data physically in the table. - Non-Clustered Index: Creates a separate structure for data retrieval. - Unique Index: Ensures no duplicate values in the column. 8. How do you handle NULL values in SQL? - Use COALESCE() or ISNULL() to replace NULL values. - Filter with IS NULL or IS NOT NULL in WHERE clauses. Example:

    
           SELECT COALESCE(PhoneNumber, 'N/A') AS ContactNumber FROM Customers;
   {}
    

9. What is the difference between DELETE and TRUNCATE? - DELETE: Removes specific rows, can use WHERE clause, and logs individual row deletions. - TRUNCATE: Removes all rows, faster, and resets table identity. 10. How do you use a CASE statement in SQL?

    
            SELECT ProductName,
           CASE 
               WHEN Quantity > 100 THEN 'High Stock'
               WHEN Quantity BETWEEN 50 AND 100 THEN 'Medium Stock'
               ELSE 'Low Stock'
           END AS StockStatus
    FROM Products;
    {}
    

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Essential questions related to Data Analytics 👇👇 Question 1: What is the first skill a fresher should learn for a Data Analytics job? Answer: SQL. It’s the foundation for retrieving, manipulating, and analyzing data stored in databases. Question 2: Which SQL database query should we learn - MySQL, PostgreSQL, PL-SQL, etc.? Answer: Core SQL concepts are consistent across platforms. Focus on joins, aggregations, subqueries, and window functions. Question 3: How much Python is required? Answer: Learn basic syntax, loops, conditional statements, functions, and error handling. Then focus on Pandas and Numpy very well for data handling and analysis. Working Knowledge of Python + Good knowledge of Data Analysis Libraries is needed only. Question 4: What other skills are required? Answer: MS Excel for data cleaning and analysis, and a BI tool like Power BI or Tableau for creating dashboards. Question 5: Is knowledge of Macros/VBA required? Answer: No. Most Data Analyst roles don’t require it. Question 6: When should I start applying for jobs? Answer: Apply after acquiring 50% of the required skills and gaining practical experience through projects or internships. Question 7: Are certifications required? Answer: No. Projects and hands-on experience are more valuable. Question 8: How important is data visualization in a Data Analyst role? Answer: Very important. Use tools like Tableau or Power BI to present insights effectively. Question 9: Is understanding statistics important for data analysis? Answer: Yes. Learn descriptive statistics, hypothesis testing, and regression analysis for better insights. Question 10: How much emphasis should be placed on machine learning? Answer: A basic understanding is helpful but not essential for Data Analyst roles. Question 11: What role does communication play in a Data Analyst's job? Answer: It’s crucial. You need to present insights in a clear and actionable way for stakeholders. Question 12: Is data cleaning a necessary skill? Answer: Yes. Cleaning and preparing raw data is a major part of a Data Analyst’s job. Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 ENJOY LEARNING 👍👍

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