Advanced Tableau Desktop

Categories: Data Analytics
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About Course

Advanced Tableau Desktop concepts often involve delving deeper into data analysis, visualization customization, and optimizing performance. Here are some advanced topics you might explore:

  1. Advanced Calculations: Utilize Tableau’s calculation features to perform complex calculations, including level of detail (LOD) expressions, table calculations, and nested calculations.
  2. Parameters: Master the use of parameters to allow users to interactively control aspects of the visualization, such as filtering data, changing measures, or altering visual properties.
  3. Advanced Mapping Techniques: Explore advanced mapping techniques such as custom geocoding, background images, polygon maps, and integration with spatial files or APIs.
  4. Dashboard Design Best Practices: Learn about best practices for designing effective dashboards, including layout optimization, interactivity, user experience considerations, and storytelling.
  5. Data Blending and Joins: Understand how to blend data from multiple sources and perform advanced join operations to integrate disparate datasets for analysis.
  6. Performance Optimization: Optimize the performance of your Tableau workbooks by managing data extracts, minimizing calculations, utilizing data source filters, and leveraging performance recording tools.
  7. Advanced Visualizations: Experiment with advanced visualization types such as tree maps, heat maps, box plots, bullet graphs, and custom visualizations using extensions or JavaScript integration.
  8. Integration with R or Python: Incorporate advanced analytics and machine learning models into Tableau using R or Python integration to perform predictive analytics or advanced statistical analysis.
  9. Custom SQL Queries: Learn how to write and optimize custom SQL queries within Tableau to directly access data and perform advanced data manipulation.
  10. Advanced Interactivity: Explore advanced interactivity features such as parameter actions, set actions, dashboard actions, and URL actions to create dynamic and interactive visualizations.
  11. Tableau Extensions: Experiment with Tableau extensions to extend the capabilities of Tableau, integrating with external services, embedding web content, or creating custom functionality.
  12. Security and Permissions: Understand advanced security features in Tableau, including row-level security, column-level security, and data encryption to ensure data protection and compliance.

Each of these topics with our Advanced Tableau Desktop course offers opportunities for advanced exploration and mastery within Tableau Desktop, allowing users to create more sophisticated and impactful data visualizations and analyses.

Basics course : https://www.infiniteskillshub.com.au/courses/tableau-desktop-basics/?v=3a1ed7090bfa

Intermediate course: https://www.infiniteskillshub.com.au/courses/intermediate-tableau-desktop-skills/?v=3a1ed7090bfa

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What Will You Learn?

  • Advanced Calculations: Utilize Tableau's calculation features to perform complex calculations, including level of detail (LOD) expressions, table calculations, and nested calculations.
  • Parameters: Master the use of parameters to allow users to interactively control aspects of the visualization, such as filtering data, changing measures, or altering visual properties.
  • Advanced Mapping Techniques: Explore advanced mapping techniques such as custom geocoding, background images, polygon maps, and integration with spatial files or APIs.
  • Dashboard Design Best Practices: Learn about best practices for designing effective dashboards, including layout optimization, interactivity, user experience considerations, and storytelling.
  • Data Blending and Joins: Understand how to blend data from multiple sources and perform advanced join operations to integrate disparate datasets for analysis.
  • Performance Optimization: Optimize the performance of your Tableau workbooks by managing data extracts, minimizing calculations, utilizing data source filters, and leveraging performance recording tools.
  • Advanced Visualizations: Experiment with advanced visualization types such as tree maps, heat maps, box plots, bullet graphs, and custom visualizations using extensions or JavaScript integration.
  • Integration with R or Python: Incorporate advanced analytics and machine learning models into Tableau using R or Python integration to perform predictive analytics or advanced statistical analysis.
  • Custom SQL Queries: Learn how to write and optimize custom SQL queries within Tableau to directly access data and perform advanced data manipulation.
  • Advanced Interactivity: Explore advanced interactivity features such as parameter actions, set actions, dashboard actions, and URL actions to create dynamic and interactive visualizations.
  • Tableau Extensions: Experiment with Tableau extensions to extend the capabilities of Tableau, integrating with external services, embedding web content, or creating custom functionality.
  • Security and Permissions: Understand advanced security features in Tableau, including row-level security, column-level security, and data encryption to ensure data protection and compliance.

Course Content

Data Fetch techniques

  • Intro to custom SQL queries
    00:00
  • Custom SQL queries examples
    00:00

Advanced Calculations

Parameters

Time Related Data Analysis
Temporal data is prevalent in various fields and applications, including: Financial Analysis: Stock market data, trading volumes, and economic indicators are often analyzed over time to identify trends, patterns, and correlations. Sensor Data: IoT (Internet of Things) devices generate temporal data such as temperature readings, humidity levels, and motion detection, which can be analyzed to monitor trends or detect anomalies. Healthcare: Patient records, vital signs, medication schedules, and medical events are temporal data points used for tracking patient health over time and assessing treatment effectiveness. Climate and Environmental Monitoring: Weather data, climate patterns, pollution levels, and natural disaster occurrences are examples of temporal data used for forecasting and studying long-term trends. Project Management: Gantt charts, project timelines, task durations, and milestones represent temporal data used for scheduling, resource allocation, and progress tracking in project management. Social Media and Web Analytics: Timestamps on posts, website visits, user interactions, and engagement metrics provide temporal insights into user behavior, content popularity, and marketing campaign effectiveness.

Extensions

Analytics extensions

Geo spatial Analysis

Dashboard objects and Interactivity

Dashboards best practices and Stories

Security and privacy

Quiz

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