Since last excel topic was very important, let me try to explain it in detail. This is how you may be expected to work on excel project:

1. Define the Project Scope and Objectives: Start by defining the scope and objectives of your Excel project. What specific problem or question are you trying to address with your data analysis? Clearly define the goals and deliverables of your project to guide your analysis.

2. Data Collection and Preparation:
- Identify Data Sources: Determine where your data will come from. This could include internal databases, external sources, spreadsheets, or manual data entry.
- Data Cleaning and Validation: Clean the data to ensure accuracy and consistency. This involves tasks such as removing duplicates, correcting errors, and validating data against predefined criteria.
- Data Transformation: Prepare the data for analysis by transforming it into a format suitable for Excel. This may involve restructuring the data, combining multiple datasets, or performing calculations to derive new variables.

3. Data Analysis:
- Exploratory Data Analysis (EDA): Use Excel's features such as sorting, filtering, and PivotTables to explore your data and gain insights into patterns, trends, and relationships.
- Statistical Analysis: Apply statistical techniques and formulas to analyze the data. This could include calculating summary statistics, conducting hypothesis tests, or performing regression analysis to model relationships between variables.
- Data Modeling: Use Excel's advanced functions and tools to build predictive models or forecast future trends based on historical data.

4. Data Visualization:
- Charts and Graphs: Create visualizations such as bar charts, line charts, and scatter plots to represent your data visually. Choose the most appropriate chart types to effectively communicate your findings.
- Dashboards: Design interactive dashboards that consolidate key insights and metrics into a single view. Use features like slicers, pivot charts, and dynamic ranges to make your dashboard user-friendly and interactive.

For free Excel resources, you can join this telegram channel: https://news.1rj.ru/str/excel_analyst

5. Automation and Efficiency:
- Macros and VBA: Automate repetitive tasks and streamline workflows using macros and VBA (Visual Basic for Applications). Write custom noscripts to perform complex calculations, data manipulation, or report generation automatically.
- Keyboard Shortcuts and Productivity Tips: Take advantage of Excel's keyboard shortcuts and productivity tips to work more efficiently. Learn commonly used shortcuts for navigation, selection, editing, and formatting to speed up your workflow.

6. Collaboration and Sharing:
- Shared Workbooks: Share your Excel workbook with team members or stakeholders to facilitate collaboration and decision-making. Use Excel's collaboration features to track changes, leave comments, and communicate effectively within the workbook.
- Interactive Reports: Create interactive reports and presentations that allow users to explore the data and drill down into specific details. Use features like hyperlinks, bookmarks, and data validation to enhance interactivity and usability.

7. Documentation and Reporting:
- Documentation: Document your analysis process, methodology, and assumptions to ensure transparency and reproducibility. Keep track of any changes made to the data or formulas for future reference.
- Reporting: Prepare a comprehensive report or presentation summarizing your findings, insights, and recommendations. Use clear and concise language, visualizations, and supporting evidence to communicate your analysis effectively to stakeholders.

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Many people pay too much to learn Excel, but my mission is to break down barriers. I have shared complete learning series to learn Excel from scratch.

Here are the links to the Excel series

Complete Excel Topics for Data Analyst: https://news.1rj.ru/str/sqlspecialist/547

Part-1: https://news.1rj.ru/str/sqlspecialist/617

Part-2: https://news.1rj.ru/str/sqlspecialist/620

Part-3: https://news.1rj.ru/str/sqlspecialist/623

Part-4: https://news.1rj.ru/str/sqlspecialist/624

Part-5: https://news.1rj.ru/str/sqlspecialist/628

Part-6: https://news.1rj.ru/str/sqlspecialist/633

Part-7: https://news.1rj.ru/str/sqlspecialist/634

Part-8: https://news.1rj.ru/str/sqlspecialist/635

Part-9: https://news.1rj.ru/str/sqlspecialist/640

Part-10: https://news.1rj.ru/str/sqlspecialist/641

Part-11: https://news.1rj.ru/str/sqlspecialist/644

Part-12:
https://news.1rj.ru/str/sqlspecialist/646

Part-13: https://news.1rj.ru/str/sqlspecialist/650

Part-14: https://news.1rj.ru/str/sqlspecialist/651

Part-15: https://news.1rj.ru/str/sqlspecialist/654

Part-16: https://news.1rj.ru/str/sqlspecialist/655

Part-17: https://news.1rj.ru/str/sqlspecialist/658

Part-18: https://news.1rj.ru/str/sqlspecialist/660

Part-19: https://news.1rj.ru/str/sqlspecialist/661

Part-20: https://news.1rj.ru/str/sqlspecialist/662

Bonus: https://news.1rj.ru/str/sqlspecialist/663

I saw a lot of big influencers copy pasting my content after removing the credits. It's absolutely fine for me as more people are getting free education because of my content.

But I will really appreciate if you share credits for the time and efforts I put in to create such valuable content. I hope you can understand.

You can join this telegram channel for more Excel Resources: https://news.1rj.ru/str/excel_analyst

Python Learning Series: https://news.1rj.ru/str/sqlspecialist/615

Complete SQL Topics for Data Analysts: https://news.1rj.ru/str/sqlspecialist/523

Complete Power BI Topics for Data Analysts: https://news.1rj.ru/str/sqlspecialist/588

I'll now start with learning series on SQL Interviews & Tableau.

Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.

Hope it helps :)

Complete Tableau Topics for Data Analysts 👇👇

1. Introduction to Tableau
- Overview of Tableau products (Desktop, Server, Online, Public, Reader)
- Installing and setting up Tableau

2. Connecting to Data
- Types of data connections (Excel, SQL, CSV, etc.)
- Connecting to live data vs. Extracts
- Data source page and data preparation (Joins, Blends, Unions)

3. Data Transformation and Preparation
- Data cleaning and shaping
- Pivoting and splitting data
- Data Interpreter
- Calculated fields
- Level of Detail (LOD) expressions
- Using Tableau Prep for data preparation

4. Building Basic Visualizations
- Bar charts, line charts, pie charts
- Scatter plots, histograms, bullet graphs
- Geographic maps and filled maps

5. Advanced Visualizations
- Dual-axis and blended axes
- Combined charts (bar-in-bar, line-in-bar)
- Treemaps, heat maps, and bubble charts
- Gantt charts, box plots, waterfall charts
- Motion charts and control charts

6. Dashboard Creation
- Designing effective dashboards
- Using containers and layout techniques
- Interactive dashboard elements (filters, parameters, actions)
- Device-specific dashboards

7. Table Calculations
- Basics of table calculations
- Quick table calculations (percent of total, running total)
- Custom table calculations (calculating differences, percent change)

8. Advanced Analytics
- Trend lines, reference lines, and reference bands
- Forecasting and clustering
- Integrating R and Python for advanced analytics

9. Interactivity and User Controls
- Filters (dimension filters, measure filters, context filters)
- Parameters (creating and using parameters)
- Dashboard actions (filter actions, highlight actions, URL actions)

10. Performance Optimization
- Extracts vs. live connections
- Data source optimization techniques
- Performance recording and analysis

11. Sharing and Collaboration
- Publishing workbooks to Tableau Server/Online
- Tableau Public for sharing visualizations
- Managing permissions and user access
- Embedding visualizations in web pages

12. Tableau Extensions and API
- Using Tableau extensions for additional functionality
- Introduction to Tableau JavaScript API for embedding and interacting with visualizations

13. Best Practices and Case Studies
- Best practices for data visualization and storytelling
- Real-world case studies and applications of Tableau

14. Certification Preparation
- Preparing for Tableau Desktop Specialist, Certified Associate, and Certified Professional exams

Best Resources to learn Tableau: https://whatsapp.com/channel/0029VasYW1V5kg6z4EHOHG1t

Like this post if you want me to continue this Tableau series 👍♥️

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Glad to see the amazing response for Tableau Learning Series 😄❤️

Let's start with the first topic today: Introduction to Tableau.

### 1. Introduction to Tableau

#### Overview of Tableau Products
Tableau offers several products tailored for different aspects of data visualization and analysis. Here's a quick rundown:

1. Tableau Desktop: This is the primary tool for creating visualizations, dashboards, and stories. It allows users to connect to various data sources, perform data analysis, and design interactive reports.
  
2. Tableau Server: A platform for sharing and collaboration, Tableau Server enables users to publish dashboards and share them within an organization. It also offers data governance and security features.
  
3. Tableau Online: A cloud-based version of Tableau Server, Tableau Online provides similar sharing and collaboration capabilities without the need for on-premise infrastructure.
  
4. Tableau Public: A free version of Tableau Desktop with limited capabilities. It requires all workbooks to be saved to the Tableau Public server, making them accessible to anyone.
  
5. Tableau Reader: A free tool that allows users to view and interact with Tableau visualizations created with Tableau Desktop but does not allow for editing or creation of new visualizations.
  
6. Tableau Prep: A tool designed for data preparation and cleaning. It helps users to combine, shape, and clean their data for analysis in Tableau Desktop.

#### Installing and Setting Up Tableau

System Requirements:
- Ensure your system meets the minimum hardware and software requirements for running Tableau Desktop.

Installation Steps:
1. Download Tableau Desktop: Visit the [Tableau website](https://www.tableau.com/) and download the latest version of Tableau Desktop.
  
2. Run the Installer: Open the downloaded file and follow the on-screen instructions to install Tableau Desktop. This typically involves accepting the license agreement and choosing an installation directory.
  
3. Activate the Product: Upon first launch, you will need to activate Tableau using a product key provided when you purchase Tableau. Alternatively, you can start a trial period if you're evaluating the software.

Pro Tip: You can go with Tableau Public to learn Tableau - it's free and don't need any license

Initial Setup:
1. Start Tableau: Open Tableau Desktop from your applications menu.
  
2. Explore the Workspace: Familiarize yourself with the Tableau interface, which includes:
   - The Data Pane: Where you connect to and manage your data sources.
   - The Sheets and Dashboards Pane: Where you create new sheets for visualizations and dashboards.
   - The Toolbar: Providing access to common functions and tools.
   - The Shelves (Rows, Columns, Marks, Filters, Pages): Where you drag and drop fields to build your visualizations.

3. Connect to a Sample Data Source: Tableau provides several sample data sources such as "Superstore" for practice. You can find these under the "Sample - Superstore" option in the Connect pane.

4. Explore Sample Workbooks: Tableau comes with sample workbooks that demonstrate various visualization techniques and best practices. These are a great resource for learning and inspiration.

By understanding the different Tableau products and getting your environment set up, you're ready to start working with data and creating impactful visualizations.

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Tableau Learning Series Part-2

Complete Tableau Topics for Data Analysis: https://news.1rj.ru/str/sqlspecialist/667

Today, let's learn about: Connecting to Data.

#### Types of Data Connections

Tableau can connect to a wide variety of data sources, including:

1. File-based Sources:
- Excel: Connects to .xlsx and .xls files.
- Text Files: Includes CSV, TSV, and other delimited text files.
- JSON Files: Connects to .json files for hierarchical data.
- PDF Files: Extracts tables from PDF documents.
- Spatial Files: Includes .shp, .kml, .geojson, etc.

2. Server-based Sources:
- Relational Databases: Such as SQL Server, MySQL, PostgreSQL, Oracle, and more.
- Cloud Databases: Such as Amazon Redshift, Google BigQuery, Snowflake, and others.
- Web Data Connectors: Allows connection to data available on the web via APIs (e.g., Google Sheets, Salesforce).

3. Extracts: A Tableau Data Extract (.hyper) is a snapshot of your data optimized for performance.

#### Connecting to Live Data vs. Extracts

1. Live Connection: Directly connects to the data source and queries it in real-time. This ensures that the data is always up-to-date but can be slower depending on the data source's performance and network latency.

2. Extracts: A static snapshot of the data that is stored locally. Extracts improve performance and allow for offline access. They need to be refreshed periodically to stay up-to-date with the source data.

#### Data Source Page and Data Preparation

Once you connect to a data source, Tableau takes you to the Data Source page where you can manage and prepare your data before starting your analysis.

1. Data Source Page Layout:
- Connections Pane: Lists all the data sources you are connected to.
- Canvas: Where you can drag and drop tables to create relationships and joins.
- Data Grid: Displays a preview of the data.

2. Data Preparation Tools:
- Joins: Combine tables based on common fields. Types of joins include inner, left, right, and full outer joins.
- Blends: Combine data from different data sources. This is useful when you cannot join tables directly due to different data sources.
- Unions: Stack tables with the same structure on top of each other.
- Pivoting: Reshape your data by pivoting columns into rows, useful for transforming wide data sets into long formats.
- Splitting: Split a single column into multiple columns based on a delimiter.
- Data Interpreter: Helps clean and prepare Excel or CSV data by interpreting the structure and cleaning up the data automatically.

Example: Steps for Connecting to an Excel File

1. Open Tableau Desktop.
2. Connect to Data: On the start page, click "Microsoft Excel" under the Connect pane.
3. Select the File: Browse and select an Excel file (e.g., "Sample - Superstore.xlsx").
4. Data Source Page:
- The sheets in the Excel file will be listed on the left side.
- Drag a sheet (e.g., "Orders") to the canvas.
- Tableau displays a preview of the data in the Data Grid.
- Perform any necessary data preparation steps (e.g., pivoting, splitting).

5. Go to Sheet: Click on the "Sheet 1" tab at the bottom to start building your visualization with the connected data.

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Tableau Learning Series Part-3

Complete Tableau Topics for Data Analysis: https://news.1rj.ru/str/sqlspecialist/667

Today, let's learn about Data Transformation and Preparation

Effective data transformation and preparation are crucial steps in ensuring that your data is clean, well-structured, and ready for analysis. Tableau provides several tools and features to help with this process.

#### Data Cleaning and Shaping

1. Renaming Fields: Double-click on a field name in the Data pane and give it a meaningful name.
2. Changing Data Types: Right-click on a field, select "Change Data Type," and choose the appropriate data type (e.g., string, number, date).
3. Splitting Fields: Split a field into multiple fields based on a delimiter. Right-click on a field and select "Split" or "Custom Split."
4. Pivoting Data: Convert columns into rows to reshape your data. This is useful for transforming wide data into a long format.
- Select the columns you want to pivot, right-click, and choose "Pivot."

#### Calculated Fields

Calculated fields allow you to create new data from existing data using formulas. Here’s how to create one:

1. Create a Calculated Field:
- Right-click in the Data pane and select "Create Calculated Field."
- Name your field and enter a formula. For example, to calculate a profit margin, you might use:

     [Profit] / [Sales] 
     

2. Common Functions:
- String Functions: E.g., LEFT(), RIGHT(), MID(), CONCAT().
- Date Functions: E.g., DATEPART(), DATETRUNC(), DATEDIFF().
- Logical Functions: E.g., IF, THEN, ELSEIF, ELSE, END.
- Aggregate Functions: E.g., SUM(), AVG(), MIN(), MAX().

#### Level of Detail (LOD) Expressions

LOD expressions allow you to control the granularity of your calculations. They are useful for performing complex aggregations and analyses.

1. Types of LOD Expressions:
- Fixed: Calculates the value using the specified dimensions, ignoring other dimensions in the view.

     { FIXED [Region] : SUM([Sales]) }
     

- Include: Adds dimensions to the view’s level of detail.

     { INCLUDE [Category] : SUM([Sales]) }
     

- Exclude: Removes dimensions from the view’s level of detail.

     { EXCLUDE [Segment] : SUM([Sales]) }
     

#### Using Tableau Prep for Data Preparation

Tableau Prep is a tool specifically designed for data preparation, offering an intuitive interface to clean and shape your data.

1. Connecting to Data: Similar to Tableau Desktop, connect to your data sources.
2. Flows: Tableau Prep uses flows, which are sequences of steps (clean, shape, combine, etc.) that you apply to your data.
3. Cleaning Steps:
- Cleaning and Shaping: Perform tasks like renaming fields, changing data types, splitting fields, and pivoting data.
- Union and Join: Combine multiple tables using unions and joins.
- Aggregate and Group: Aggregate data to create summary statistics and group similar values.
4. Output: Once the data is prepared, you can output it to a file or publish it to Tableau Server/Tableau Online for use in Tableau Desktop.

#### Example of Data Preparation in Tableau Prep

1. Start Tableau Prep and connect to your data source (e.g., an Excel file).
2. Add Steps:
- Drag a "Clean Step" to rename fields, split columns, and fix data types.
- Drag a "Join Step" to combine multiple tables.
- Add a "Pivot Step" to reshape data if needed.
3. Output Data:
- Add an "Output Step" and choose the output location and format.
- Run the flow to generate the cleaned data.

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Tableau Learning Series Part-4

Complete Tableau Topics for Data Analysis: https://news.1rj.ru/str/sqlspecialist/667

Today, let's learn about Building Basic Visualizations

Bar Charts

Bar charts are useful for comparing data across categories.

1. Creating a Simple Bar Chart:
- Drag a dimension (e.g., Category) to the Columns shelf.
- Drag a measure (e.g., Sales) to the Rows shelf.
- Tableau automatically creates a bar chart.

2. Customizing the Bar Chart:
- Use the Color shelf to color bars by another dimension (e.g., Sub-Category).
- Adjust the Size shelf to change bar thickness.
- Add labels by dragging a measure to the Label shelf.

Line Charts

Line charts are ideal for showing trends over time.

1. Creating a Simple Line Chart:
- Drag a date field (e.g., Order Date) to the Columns shelf.
- Drag a measure (e.g., Sales) to the Rows shelf.
- Tableau creates a line chart automatically if the date field is continuous.

2. Customizing the Line Chart:
- Use the Color shelf to distinguish lines by category (e.g., Region).
- Add markers by checking the "Show Markers" option in the Marks card.
- Adjust the date granularity (e.g., year, quarter, month) by clicking on the date field in the Columns shelf and selecting the desired granularity.

Pie Charts

Pie charts show proportions and percentages of a whole.

1. Creating a Simple Pie Chart:
- Drag a dimension (e.g., Category) to the Columns shelf.
- Drag a measure (e.g., Sales) to the Rows shelf.
- Click on the Show Me panel and select the pie chart icon.
- Move Category to the Color shelf and Sales to the Angle shelf.

2. Customizing the Pie Chart:
- Add labels by dragging the dimension or measure to the Label shelf.
- Adjust the Size shelf to change the size of the pie chart.
- Use the Color shelf to adjust colors for better distinction.

Scatter Plots

Scatter plots show relationships between two measures.

1. Creating a Simple Scatter Plot:
- Drag one measure (e.g., Sales) to the Columns shelf.
- Drag another measure (e.g., Profit) to the Rows shelf.
- Tableau creates a scatter plot automatically.

2. Customizing the Scatter Plot:
- Add a dimension (e.g., Region) to the Color shelf to color code the points.
- Add another dimension to the Detail shelf to distinguish between data points.
- Adjust the Size shelf to change the size of the points.

Histograms

Histograms display the distribution of a single measure.

1. Creating a Histogram:
- Drag a measure (e.g., Sales) to the Columns shelf.
- Right-click the measure in the Columns shelf, select "Create Bins," and set the bin size.
- Drag the newly created bin field to the Columns shelf.
- Drag another measure (e.g., Number of Records) to the Rows shelf.
- Tableau creates a histogram.

2. Customizing the Histogram:
- Adjust bin size by editing the bin field.
- Use the Color shelf to color bins by another dimension.
- Add labels by dragging a measure to the Label shelf.

Geographic Maps

Geographic maps are used to visualize data geographically.

1. Creating a Simple Map:
- Drag a geographic dimension (e.g., State) to the Columns shelf.
- Drag a measure (e.g., Sales) to the Rows shelf.
- Tableau creates a map with filled areas.

2. Customizing the Map:
- Use the Color shelf to color the regions by the measure.
- Add labels by dragging the dimension or measure to the Label shelf.
- Adjust the map style and layers through the Map menu.

## Building Dashboards

Once you have individual visualizations, you can combine them into a dashboard.

1. Creating a Dashboard:
- Click the New Dashboard icon at the bottom of the Tableau workspace.
- Drag sheets from the Sheets pane to the dashboard workspace.
- Arrange and resize the visualizations as needed.

2. Adding Interactivity:
- Use filters, actions, and parameters to make your dashboard interactive.
- Add text boxes, images, and web content for additional context.

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Requirements for data analyst role based on some jobs from @jobs_sql

👉 Must be proficient in writing complex SQL Queries.

👉 Understand business requirements in BI context and design data models to transform raw data into meaningful insights.

👉 Connecting data sources, importing data, and transforming data for Business intelligence.

👉 Strong working knowledge in Excel and visualization tools like PowerBI, Tableau or QlikView

👉 Developing visual reports, KPI scorecards, and dashboards using Power BI desktop.

Nowadays, recruiters primary focus on SQL & BI skills for data analyst roles. So try practicing SQL & create some BI projects using Tableau or Power BI.

You can refer our Power BI & SQL Series to understand the essential concepts.

Here are some essential telegram channels with important resources:

❯ SQL ➟ t.me/sqlanalyst
❯ Power BI ➟ t.me/PowerBI_analyst
❯ Resources ➟ @learndataanalysis

I am planning to come up with interview series as well to share some essential questions based on my experience in data analytics field.

Like this post if you want me to start the interview series 👍❤️

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SQL Interview Preparation Part-2

How to use window functions and CTEs to solve SQL interview questions?

1. Common Table Expressions (CTEs):

CTEs are temporary result sets that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. They help break down complex queries and improve readability.

Syntax:

WITH cte_name AS (
    SELECT column1, column2
    FROM table_name
    WHERE condition
)
SELECT column1, column2
FROM cte_name
WHERE another_condition;

Example Problem:
Find the top 3 highest-paid employees in each department.

Solution Using CTE:

WITH RankedSalaries AS (
    SELECT 
        employee_id, 
        department_id, 
        salary,
        ROW_NUMBER() OVER (PARTITION BY department_id ORDER BY salary DESC) AS rank
    FROM employees
)
SELECT 
    employee_id, 
    department_id, 
    salary
FROM RankedSalaries
WHERE rank <= 3;

2. Window Functions:

Window functions perform calculations across a set of table rows related to the current row. They do not reduce the number of rows returned.

Common Window Functions:
- ROW_NUMBER(): Assigns a unique number to each row within the partition.
- RANK(): Assigns a rank to each row within the partition, with gaps in ranking for ties.
- DENSE_RANK(): Similar to RANK(), but without gaps.
- SUM(), AVG(), COUNT(), etc., over a partition.

Syntax:

SELECT column1, 
       column2, 
       window_function() OVER (PARTITION BY column1 ORDER BY column2) AS window_column
FROM table_name;

Example Problem:
Calculate the running total of sales for each salesperson.

Solution Using Window Function:

SELECT 
    salesperson_id, 
    sale_date, 
    amount,
    SUM(amount) OVER (PARTITION BY salesperson_id ORDER BY sale_date) AS running_total
FROM sales;

Combining CTEs and Window Functions:

Example Problem:
Find the cumulative sales per department and the rank of each employee within their department based on their sales.

Solution:

WITH DepartmentSales AS (
    SELECT 
        department_id, 
        employee_id, 
        SUM(sales_amount) AS total_sales
    FROM sales
    GROUP BY department_id, employee_id
),
RankedSales AS (
    SELECT 
        department_id, 
        employee_id, 
        total_sales,
        RANK() OVER (PARTITION BY department_id ORDER BY total_sales DESC) AS sales_rank
    FROM DepartmentSales
)
SELECT 
    department_id, 
    employee_id, 
    total_sales,
    sales_rank,
    SUM(total_sales) OVER (PARTITION BY department_id ORDER BY sales_rank) AS cumulative_sales
FROM RankedSales;

For those of you who are new to this channel read SQL Basics before going through advanced concepts 😄

Part-1: https://news.1rj.ru/str/sqlspecialist/558

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SQL INTERVIEW PREPARATION PART-3

What are the different types of SQL commands?

SQL commands can be categorized into several types based on their functionality:

- DDL (Data Definition Language): These commands are used to define and modify database structures, such as tables and indexes.
- Examples: CREATE, ALTER, DROP
- Example:

         CREATE TABLE employees (
             id INT PRIMARY KEY,
             name VARCHAR(100),
             position VARCHAR(50)
         );
         

- DML (Data Manipulation Language): These commands are used to manipulate the data within the database.
- Examples: SELECT, INSERT, UPDATE, DELETE
- Example:

         INSERT INTO employees (id, name, position) VALUES (1, 'John Doe', 'Manager');
         

- DCL (Data Control Language): These commands are used to control access to data within the database.
- Examples: GRANT, REVOKE
- Example:

         GRANT SELECT ON employees TO user_name;
         

- TCL (Transaction Control Language): These commands are used to manage transactions in the database.
- Examples: COMMIT, ROLLBACK, SAVEPOINT
- Example:

         BEGIN;
         UPDATE employees SET position = 'Senior Manager' WHERE id = 1;
         COMMIT;
         

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SQL INTERVIEW PREPARATION PART-4

What is the difference between INNER JOIN and OUTER JOIN?

- INNER JOIN: Returns only the rows where there is a match in both tables.
- OUTER JOIN: Returns the matched rows as well as unmatched rows from one or both tables. There are three types of OUTER JOIN:
- LEFT OUTER JOIN (or LEFT JOIN): Returns all rows from the left table, and the matched rows from the right table. If no match is found, the result is NULL on the right side.
- RIGHT OUTER JOIN (or RIGHT JOIN): Returns all rows from the right table, and the matched rows from the left table. If no match is found, the result is NULL on the left side.
- FULL OUTER JOIN: Returns rows when there is a match in one of the tables. This means it returns all rows from the left table and the right table, filling in NULLs when there is no match.

Examples:
- INNER JOIN:

     SELECT employees.name, departments.department_name
     FROM employees
     INNER JOIN departments ON employees.department_id = departments.id;
     

- LEFT JOIN:

     SELECT employees.name, departments.department_name
     FROM employees
     LEFT JOIN departments ON employees.department_id = departments.id;
     

- RIGHT JOIN:

     SELECT employees.name, departments.department_name
     FROM employees
     RIGHT JOIN departments ON employees.department_id = departments.id;
     

- FULL OUTER JOIN:

     SELECT employees.name, departments.department_name
     FROM employees
     FULL OUTER JOIN departments ON employees.department_id = departments.id;
     

Go though SQL Learning Series to refresh your basics

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SQL INTERVIEW PREPARATION PART-5

Let's discuss about normalization today

   - Normalization is the process of organizing the data in a database to reduce redundancy and improve data integrity. The goal is to divide a database into two or more tables and define relationships between them to reduce redundancy and dependency. There are several normal forms, each with specific rules to help achieve this goal.

Normalization involves multiple steps, usually referred to as "normal forms" (NFs):

   - First Normal Form (1NF): Ensures that the table has a primary key and that each column contains atomic (indivisible) values.
     - Example:
      

       CREATE TABLE customers (
           customer_id INT PRIMARY KEY,
           customer_name VARCHAR(100),
           contact_number VARCHAR(15)
       );
       

   - Second Normal Form (2NF): Achieves 1NF and ensures that all non-key attributes are fully functionally dependent on the primary key. This means removing partial dependencies of any column on the primary key.
     - Example: If a table has a composite key (e.g., order_id, product_id) and some columns depend only on part of that key, those columns should be moved to another table.

   - Third Normal Form (3NF): Achieves 2NF and ensures that all the attributes are functionally dependent only on the primary key. This eliminates transitive dependencies.
     - Example:
      

       CREATE TABLE orders (
           order_id INT PRIMARY KEY,
           customer_id INT,
           order_date DATE,
           FOREIGN KEY (customer_id) REFERENCES customers(customer_id)
       );

       CREATE TABLE order_details (
           order_id INT,
           product_id INT,
           quantity INT,
           PRIMARY KEY (order_id, product_id),
           FOREIGN KEY (order_id) REFERENCES orders(order_id)
       );
       

   - Boyce-Codd Normal Form (BCNF): A stricter version of 3NF where every determinant is a candidate key. This addresses situations where 3NF is not sufficient to eliminate all redundancies.

Tricky Question:
   - How would you approach normalizing a table that contains repeating groups of data?
     - This question tests the understanding of the concept of atomicity and the process of transforming a table into 1NF.

Example Answer:
   - "If a table contains repeating groups, such as multiple phone numbers in one column separated by commas, I would first ensure that each piece of data is atomic. I would create a separate table for the repeating group and link it with a foreign key to the original table, thereby normalizing the data into 1NF."

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SQL INTERVIEW PREPARATION PART-6

Let's discuss about subquery today

- A subquery, also known as an inner query or nested query, is a query within another SQL query. It is used to provide data to the main query (outer query). Subqueries can be used in various clauses such as SELECT, FROM, WHERE, and HAVING.

Types of Subqueries:
- Single-row subquery: Returns a single row and is used with operators like =, <, >.
- Multi-row subquery: Returns multiple rows and is used with operators like IN, ANY, ALL.
- Correlated subquery: A subquery that references columns from the outer query. It is evaluated once for each row processed by the outer query.

Examples:
- Single-row subquery:

     SELECT name
     FROM employees
     WHERE department_id = (SELECT id FROM departments WHERE department_name = 'Sales');
     

- Multi-row subquery:

     SELECT name
     FROM employees
     WHERE department_id IN (SELECT id FROM departments WHERE region = 'North');
     

- Correlated subquery:

     SELECT e.name
     FROM employees e
     WHERE e.salary > (SELECT AVG(salary) FROM employees WHERE department_id = e.department_id);
     

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SQL INTERVIEW PREPARATION PART-7

Explain the difference between GROUP BY and ORDER BY in SQL.

- GROUP BY: Groups rows that have the same values into summary rows.
- ORDER BY: Sorts the result set in ascending or descending order based on one or more columns.

Tips:
- Mention that GROUP BY is typically used with aggregate functions like COUNT, SUM, AVG, etc., while ORDER BY is used for sorting the result set.
- Provide an example to illustrate the distinction between the two clauses.

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SQL INTERVIEW PREPARATION PART-8

How do you find the nth highest salary from a table in SQL?

Answer:
You can use the LIMIT clause in combination with the ORDER BY clause to find the nth highest salary.

Example:

SELECT DISTINCT salary
FROM employees
ORDER BY salary DESC
LIMIT n-1, 1;

Replace 'n' with the desired rank of the salary.

Tip: Emphasize the importance of using DISTINCT to handle cases where there are duplicate salaries, and ensure the ORDER BY clause is sorting the salaries in descending order to find the nth highest salary.

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SQL INTERVIEW PREPARATION PART-8

How can you find the second highest salary in a table without using the LIMIT clause?

You can use a subquery to find the maximum salary that is less than the overall maximum salary.

Example:

SELECT MAX(salary)
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);

Tip: Explain that this approach can be useful when the LIMIT clause is not supported or if you want to demonstrate proficiency in using subqueries.

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SQL INTERVIEW PREPARATION PART-9

What are window functions in SQL and can you provide an example?

Answer:
Window functions perform a calculation across a set of table rows that are somehow related to the current row. Unlike aggregate functions, window functions do not cause rows to become grouped into a single output row.

Example using ROW_NUMBER():

SELECT name, salary, department_id,
       ROW_NUMBER() OVER (PARTITION BY department_id ORDER BY salary DESC) as row_num
FROM employees;

In this example, ROW_NUMBER() assigns a unique rank to each row within each department, ordered by salary in descending order.

Tip: Highlight the usefulness of window functions for complex analytics and reporting tasks, where you need to perform calculations across rows while still returning individual rows. Explain other common window functions like RANK(), DENSE_RANK(), and NTILE().

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SQL INTERVIEW PREPARATION PART-10

Explain what a CTE (Common Table Expression) is and provide an example.

Answer:
A Common Table Expression (CTE) is a temporary result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. CTEs are defined using the WITH keyword and can improve the readability and organization of complex queries.

Example:

WITH EmployeeCTE AS (
    SELECT department_id, AVG(salary) as avg_salary
    FROM employees
    GROUP BY department_id
)
SELECT e.name, e.salary, e.department_id, c.avg_salary
FROM employees e
JOIN EmployeeCTE c ON e.department_id = c.department_id
WHERE e.salary > c.avg_salary;

In this example, the CTE EmployeeCTE calculates the average salary per department, which is then used in the main query to find employees earning above the average salary in their department.

Tip: Explain that CTEs can be particularly useful for breaking down complex queries into more manageable parts, improving both readability and maintainability. They also allow for recursive queries, which can be useful in hierarchical data structures.

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