Excel Learning Series Part-12

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

Today, let's learn about another important topic Data Visualization with Power BI:

1. Connecting Excel to Power BI: Power BI is a powerful business analytics tool provided by Microsoft. You can connect Excel to Power BI to leverage its advanced data visualization capabilities. This connection allows you to create interactive dashboards and reports based on your Excel data.

2. Creating Interactive Dashboards: With Power BI, you can create interactive dashboards that provide dynamic visualizations of your data. You can add various types of charts, graphs, maps, and other visual elements to your dashboard and customize them to meet your specific requirements. Power BI also offers features such as slicers, filters, and drill-down capabilities, allowing users to explore and analyze data in different ways.

For example:
- You can connect Excel to Power BI and import your Excel data into a Power BI dataset. Once the data is imported, you can create interactive visualizations such as bar charts, line charts, and pie charts based on the imported data.
- You can then combine these visualizations into a dashboard layout and add filters and slicers to allow users to interactively explore the data.

Data Visualization with Power BI enhances the presentation and analysis of data, providing insights that are easily understandable and actionable.

Refer our Power BI Learning Series to know more.

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Required Skills:
Strong mathematics skills (Masters level applied statistics preferred)
Proficiency in Python, SQL, and spreadsheets
High degree of comfort with data management, ETL techniques, and data ingestion
Experience with QA/QC testing and data troubleshooting
AWS Glue, Step, S3, Admin, or similar data tooling experience a plus
Classification, NLP, statistical machine learning modeling experience a plus
Experience with Python Regex library a plus
Experience with Pyspark a plus
Experience with Mac OS and Google suite

Nowadays, companies are expecting a lot of skills from freshers to mid-level experienced people. It's better to learn something new every week and upskill yourself whenever possible.

Also, SQL is one of the very underrated skill which most of the jobs ask for. So those of you who are new to data field, I would recommend to start with learning SQL and proceed further as per your comfort.

I already covered essential topics for SQL: https://news.1rj.ru/str/sqlspecialist/567

Start with these topics and gradually improve everyday with consistent practice.

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Excel Learning Series Part-13

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

Today, let's learn about Macros and Automation:

1. Recording and Running Macros: Macros are sequences of actions that you can record to automate repetitive tasks in Excel. You can record a macro by performing the desired actions manually, and Excel will generate VBA (Visual Basic for Applications) code to replicate those actions. Once recorded, you can run the macro to repeat the same series of actions automatically.

2. Automation with VBA (Visual Basic for Applications): VBA is a programming language that allows you to create custom macros and automate tasks in Excel. With VBA, you can write noscripts to perform complex calculations, manipulate data, create custom functions, interact with external databases, and much more. VBA opens up a wide range of possibilities for automating tasks and extending Excel's functionality beyond its built-in features.

For example:
- You can record a macro to automate the process of formatting and organizing data in a specific way. This could include tasks such as applying cell styles, sorting data, and generating summary reports.
- With VBA, you can create custom macros to automate repetitive tasks such as data cleaning, report generation, and data analysis. For instance, you could write a VBA noscript to automatically import data from external sources, perform calculations, and generate visualizations based on specific criteria.

Macros and automation help streamline workflows, increase productivity, and reduce errors by eliminating manual repetitive tasks.

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Excel Learning Series Part-14

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

Today, let's learn about Advanced Data Analysis:

1. Regression Analysis: Regression analysis is a statistical method used to explore the relationship between one dependent variable and one or more independent variables. Excel provides built-in tools for performing linear regression analysis, which can help you understand the strength and nature of the relationship between variables, make predictions, and identify outliers.

2. Data Forecasting with Excel: Excel offers several methods for data forecasting, including exponential smoothing, moving averages, and trend analysis. These methods allow you to analyze historical data trends and make predictions about future values based on those trends. Excel's forecasting tools provide visualizations and statistical measures to help you assess the accuracy and reliability of your forecasts.

For example:
- You can use regression analysis in Excel to analyze the relationship between advertising expenditure and sales revenue. By performing a regression analysis, you can determine the extent to which advertising spending influences sales and make predictions about future sales based on different advertising budgets.
- Excel's forecasting tools can be used to predict future sales volumes based on historical sales data. You can apply different forecasting methods, such as exponential smoothing or moving averages, to identify patterns and trends in the data and make predictions about future sales performance.

Advanced data analysis techniques such as regression analysis and data forecasting enable you to gain deeper insights into your data and make more informed decisions.

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Excel Learning Series Part-15

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

Now, let's learn about Collaboration and Sharing:

1. Excel Sharing Options: Excel provides several ways to share workbooks with others for collaboration. You can share a workbook by sending it as an email attachment, saving it to a shared network location, or using cloud storage services such as OneDrive or SharePoint. Excel also offers built-in collaboration features, such as co-authoring, which allow multiple users to edit the same workbook simultaneously.

2. Collaborative Editing and Comments: Excel allows multiple users to collaborate on a workbook in real-time, making changes and updates that are automatically synced across all users' devices. Users can see each other's changes in real-time and communicate through comments and chat within the workbook. Excel also provides a version history feature, allowing users to track changes and revert to previous versions if needed.

For example:
- You can share an Excel workbook with your team via OneDrive, allowing everyone to access and edit the same document simultaneously. Each user's changes are automatically synced, ensuring that everyone has access to the most up-to-date version of the workbook.
- Users can leave comments within the workbook to provide feedback or ask questions about specific data or calculations. Comments can be replied to, resolved, and tracked, making it easy to collaborate and communicate effectively within the workbook.

Collaboration and sharing features in Excel facilitate teamwork and communication, enabling multiple users to work together efficiently on the same document.

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Excel Learning Series Part-16

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

Today, let's learn about Excel Shortcuts and Productivity Tips:

1. Time-saving Keyboard Shortcuts: Excel offers a wide range of keyboard shortcuts to help you perform common tasks more efficiently. These shortcuts allow you to execute commands and actions quickly without having to navigate through menus and ribbons. Learning and using keyboard shortcuts can significantly speed up your workflow and boost productivity in Excel.

2. Productivity Tips for Efficient Work: Excel provides various features and functionalities designed to improve productivity and streamline your work processes. These include:
- Using named ranges to easily reference specific ranges of cells in your formulas.
- Utilizing Excel's AutoFill feature to quickly populate a series of cells with sequential or patterned data.
- Taking advantage of Excel's built-in templates and functions to perform common calculations and tasks.
- Organizing and structuring your workbooks with clear headings, cell formatting, and worksheet tabs for easy navigation.

For example:
- Instead of manually copying and pasting data, you can use the keyboard shortcut "Ctrl + C" to copy and "Ctrl + V" to paste.
- You can quickly insert the current date into a cell by pressing "Ctrl + ;" (semicolon).
- To select an entire column or row, you can use the keyboard shortcut "Ctrl + Spacebar" for columns and "Shift + Spacebar" for rows.

Here are some additional keyboard shortcuts to enhance your productivity in Excel:

1. Navigation Shortcuts:
- Ctrl + Arrow Keys: Navigate to the edge of data regions.
- Ctrl + Home: Move to the beginning of the worksheet.
- Ctrl + End: Move to the last cell of the data region.
- Ctrl + Page Up / Page Down: Switch between worksheets.

2. Selection Shortcuts:
- Shift + Arrow Keys: Select cells or ranges of cells.
- Ctrl + Shift + Arrow Keys: Extend the selection to the edge of data regions.
- Ctrl + A: Select the entire worksheet.

3. Editing Shortcuts:
- Ctrl + Z: Undo the last action.
- Ctrl + Y: Redo the last undone action.
- Ctrl + X: Cut selected cells.
- Ctrl + C: Copy selected cells.
- Ctrl + V: Paste copied or cut cells.
- Ctrl + D: Fill down (copies the content from the cell above).
- Ctrl + R: Fill right (copies the content from the cell to the left).

4. Formatting Shortcuts:
- Ctrl + B: Apply bold formatting.
- Ctrl + I: Apply italic formatting.
- Ctrl + U: Apply underline formatting.
- Ctrl + 1: Open the Format Cells dialog box.
- Ctrl + Shift + L: Toggle filters on or off (for Excel tables).

5. Formula Shortcuts:
- Ctrl + Shift + Enter: Enter an array formula.
- F2: Edit the active cell.
- Ctrl + `: Toggle formula view mode (show/hide formulas).
- Alt + =: Insert autosum formula.

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Excel Learning Series Part-17

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

Today, let's learn about Data Import and Export:

1. Importing Data: Excel allows you to import data from various external sources, including databases, text files, CSV files, XML files, and web pages. You can use Excel's built-in data import features to connect to external data sources and bring data directly into your Excel workbook. This enables you to work with data from different sources without having to manually input or copy-paste the data.

2. Exporting Data: Excel also provides options for exporting data from your workbook to external files or formats. You can export data to CSV (Comma-Separated Values) files, text files, PDF files, HTML files, and more. This allows you to share your data with others or use it in other applications and systems that support these formats.

For example:
- To import data from a CSV file into Excel, you can use the "Data" tab and choose the "From Text/CSV" option to open the CSV file and import its contents into Excel.
- To export data from Excel to a CSV file, you can select the data you want to export, go to the "File" tab, choose "Save As," and select "CSV (Comma delimited)" as the file format.

These import and export features in Excel facilitate data exchange and integration with other systems, enabling you to work with data from diverse sources and share your analysis with others effectively.

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Excel Learning Series Part-18

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

Today, let's learn about Data Security and Protection:

1. Password Protection: Excel allows you to protect your workbooks with passwords to prevent unauthorized access. You can set a password to open the workbook, as well as a password to modify the workbook. Password protection helps ensure that only authorized users can view and edit the contents of your Excel files.

2. Worksheet and Workbook Security: Excel provides options for securing individual worksheets or entire workbooks. You can restrict users from making changes to specific cells, rows, or columns by locking them and then protecting the worksheet. Additionally, you can apply workbook-level security settings to control access to sensitive data or features, such as macros or external data connections.

For example:
- To set a password to open an Excel workbook, you can go to the "File" tab, choose "Save As," click on "Tools" in the Save As dialog box, and select "General Options." Here, you can enter a password under "Password to open."
- To protect a worksheet, you can select the cells you want to lock, right-click, choose "Format Cells," go to the "Protection" tab, and check the "Locked" checkbox. Then, you can go to the "Review" tab and click on "Protect Sheet" to set a password and protect the worksheet.

These security features help safeguard your data and prevent unauthorized access or changes to your Excel workbooks.

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Excel Learning Series Part-19

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

Now, let's learn about Excel Add-Ins:

1. Using Excel Add-Ins for Extended Functionality: Excel Add-Ins are additional features or programs that you can install to extend Excel's functionality and capabilities. Add-Ins can provide specialized tools, functions, or integration with external systems that enhance your ability to work with data and perform advanced tasks in Excel.

2. Installing Excel Add-Ins: Excel Add-Ins can be installed from various sources, including the Microsoft Office Store, third-party vendors, or custom-developed solutions. Once installed, Add-Ins typically appear as additional tabs or commands within the Excel interface, giving you access to their features and functionalities.

For example:
- The "Solver" Add-In in Excel is used for optimization and what-if analysis, allowing you to find optimal solutions to complex problems by adjusting variables and constraints.
- The "Analysis ToolPak" Add-In provides advanced statistical functions and data analysis tools, such as regression analysis, correlation analysis, and histograms.

Excel Add-Ins offer a convenient way to expand Excel's capabilities and tailor it to your specific needs.

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Excel Learning Series Part-20

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

Today, let's learn about Mastering Excel for Data Analysis:

1. Comprehensive Project or Case Study Integrating Various Excel Skills: Mastering Excel for Data Analysis involves applying all the skills and techniques learned in the previous topics to real-world scenarios. This comprehensive project or case study allows you to practice and demonstrate your proficiency in using Excel for data analysis, visualization, and decision-making.

2. Key Components of the Mastering Excel Project:
- Data Preparation: Importing and cleaning data from multiple sources, removing duplicates, and validating data.
- Data Analysis: Performing advanced calculations, statistical analysis, and data modeling using formulas, functions, and PivotTables.
- Data Visualization: Creating informative and visually appealing charts, graphs, and dashboards to present insights and findings.
- Automation and Efficiency: Implementing macros, keyboard shortcuts, and productivity tips to streamline workflows and increase efficiency.
- Collaboration and Sharing: Enabling collaboration with team members, sharing insights, and facilitating decision-making through shared workbooks and interactive dashboards.

For example:
- You could create a project where you analyze sales data for a fictional company, including importing data from multiple sources, cleaning and preparing the data, performing sales trend analysis using PivotTables and charts, and creating an interactive dashboard to visualize key performance metrics.
- Alternatively, you could design a case study where you simulate real-world scenarios, such as forecasting sales for a new product launch, analyzing customer demographics and preferences, and optimizing marketing strategies based on data insights.

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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.

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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.

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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.

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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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Stay tuned for the next topics ☺️

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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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