Tableau Desktop - Advanced Course

This course is taught by a live instructor and is available in two class formats:

1. Live Online for Individuals
2. Onsite/Online for Private Groups

Data preparation for Tableau is the process of organizing and transforming data into a structure that can be used in Tableau. This includes understanding Tableau's data-handling engine, utilizing data-mining and knowledge-discovery process models such as CRISP-DM, and focusing on preparing the data to make it easier to use with Tableau. It is important to understand the process of data preparation for Tableau because it helps ensure that the best insights and results are produced from your data.

By understanding Tableau's data-handling engine, understanding the process models such as CRISP-DM, and focusing on data preparation, you can better use the features of Tableau to gain further insights from your data. With Tableau, you can create dimensional views of your data and easily identify patterns and trends. Additionally, Tableau has powerful tools for identifying outliers that may be overlooked in other solutions. By focusing on data preparation before using Tableau, you ensure that the results are accurate and free of errors.

Tableau also has many features that can help with data preparation for Tableau. These include data transformation tools, the ability to combine multiple sources of data, and the ability to clean and filter your data. Additionally, Tableau has a range of functions to help you perform calculations and manipulate your data so that it is ready for use in Tableau.

By understanding the importance of data preparation for Tableau and utilizing its features, you can leverage your data to gain deeper insights into your business and make informed decisions. Data preparation is an essential part of using Tableau, so it's important to understand how to properly prepare your data before using the solution. Doing so will help you get the most out of your data and ensure that your results are accurate.

Tableau joins, blends and data structures are essential components for creating a successful analytics workflow. Joins provide the ability to combine two or more tables based on common attributes, enabling users to make faster and more informed decisions. There are several types of joins available in Tableau such as inner join, left join, right join, full outer join, and self-join. Complex joins are also possible, enabling users to join tables that have more than one common attribute.

Data blending is a powerful feature of Tableau that allows users to combine multiple data sources on the same visualization without having to write a single query for them. This feature enables users to quickly analyze large datasets and gain insights from them.

Data structures provide the backbone for all analytics operations in Tableau. They help organize information and enable users to access and analyze it quickly. Data structures like tables, hierarchies, and hierarchal orderings are important for providing a good foundation for visualizations. By understanding these key components of Tableau, users can ensure their analyses are efficient and accurate.

Data densification is a process of increasing the density (or resolution) of data by filling in missing values to create more detailed reports. This can be useful for understanding trends over time or other changes in data. Working with cubes is another way to work with data. A cube is a multidimensional representation of an underlying data set, allowing users to slice and dice the data in many different ways. Tableau is a powerful tool for working with big data, allowing users to interactively explore large datasets quickly and uncover hidden insights. Tableau's drag-and-drop interface makes it easy to create sophisticated visualizations and combine data from multiple sources. With its advanced analytics options, Tableau can help users uncover meaningful insights from their big data. In addition, Tableau has robust security protocols in place to ensure the accuracy and integrity of data. This makes Tableau an ideal solution for working with large datasets.

Tableau's comprehensive suite of tools enables organizations to leverage their big data, providing increased efficiency and insights into their operations. By leveraging the power of Tableau and data densification, organizations can unlock the potential of their big data to uncover previously unknown relationships, trends, and correlations between their data points. This can provide valuable insights that can be used to inform business decisions and strategies.

Tableau Table Calculations are powerful interactive functions that enable users to perform calculations and analyses on the fly. Table Calculation functions can range from basic arithmetic and statistical operations to complex predictive analytics. The function is applied by dragging a variable into the view and then selecting the calculation type from the drop-down menu in Tableau’s visualization pane. It is then possible to customize the calculation by setting specific parameters that define how the data should be analyzed. This makes Table Calculations a powerful tool for quickly and accurately analyzing large datasets in real time. Users can create custom calculations with their logic or use pre-defined ones to further investigate patterns in the data. With Tableau Table Calculations, users can unlock the true potential of their data and gain valuable insights from it.

Tableau Level of Detail (LOD) calculations enable the user to create custom views of their data by allowing them to granularly control which dimensions and measures are included in their visualization. This powerful tool offers users unprecedented flexibility in how they present their data, all while providing an easy-to-use interface.

Tableau's LOD calculations have many practical applications. For example, they can be used to break down data into small sections and see how different metrics vary among them. They can also be useful in constructing scenarios such as budgeting or forecasting, where the user needs to consider multiple variables at once.

Tableau's LOD calculations are also very useful for building playgrounds. With their help, users can quickly create multiple visualizations of the same data and play around with different configurations to better understand trends or relationships in their data. This is especially important for businesses that need to make quick decisions based on complex datasets.

Tableau's Level of Detail calculations is an extremely powerful tool that can be used to better understand complex data. With their help, users can create customized visuals that offer a deep level of insight into the underlying datasets. Furthermore, they enable quick exploration of playgrounds and scenarios, allowing businesses to make informed decisions promptly.

Tableau advanced chart types are designed to help users create more visually appealing and meaningful data visualizations. Through the use of subtle design elements, such as custom background images, these chart types can be used to enhance popular visualizations and make them easier to understand. For example, using a combination of color palettes, shapes, and icons, users can create a heatmap that allows them to quickly analyze data trends at a glance. Similarly, combination charts and scatter plots can be used to easily compare two or more related measures. No matter the type of data visualization you're trying to create, Tableau's advanced chart types provide users with powerful tools to enhance their visualizations. With the right tools, users can create custom visualizations that will help them gain valuable insights from their data.

Tableau mapping is an interactive data visualization tool that allows users to quickly and easily identify patterns, trends, and relationships in their datasets. Tableau maps provide a dynamic way to explore geographical data through intuitive visualizations. With a wide range of map types, overlays, boundaries, and layers, users can gain insights from their data without having to leave the Tableau environment.

In addition to the mapping features available in Tableau, users can extend their mapping capabilities with other technologies such as Google Maps and GeoJSON. With these tools, users can add geographical context to datasets and gain a deeper understanding of their data. By combining the powerful visualizations of Tableau with the contextual information provided through these technologies, users can unlock powerful insights.

By leveraging the power of Tableau mapping, users can quickly and easily visualize their data in a way that provides greater insight than traditional methods of analysis. By adding other technologies to extend Tableau's mapping capabilities, users can gain even more valuable insights from their datasets. With Tableau mapping and the right technology, users can unlock powerful insights that were not previously possible.

Tableau is a powerful data visualization tool that can help you present your data in an engaging and informative way. However, simply using Tableau to generate images isn’t always enough to create effective presentations. To drive home the point of your message, it’s important to use presentation techniques designed specifically for Tableau.

One key technique is to export a Tableau image directly into PowerPoint. This allows you to use the advanced formatting options in PowerPoint to craft an attractive, easy-to-read presentation slide. You can also embed Tableau views directly into PowerPoint, which allows your audience to interact with the data without leaving the presentation. With this method, you can keep the most up-to-date version of your data in the presentation.

To bring the presentation to life, consider using advanced features such as animation and story points dashboards. Animations allow you to highlight certain elements or transitions on your slides over time, while story points dashboards can provide a more interactive experience for your audience. Both techniques help to keep the viewer’s attention and make the presentation more memorable.

Using these Tableau presentation techniques will help you create an effective presentation that gets your message across in a powerful way. With proper preparation and practice, you can confidently deliver a professional and engaging content-rich presentation using Tableau.

Tableau provides many tools and options to help create appealing visuals and effective dashboards. Here are some best practices to keep in mind when creating visualizations and designing dashboards with Tableau:

• Use visualization design theory to select the right type of chart for your specific data set. The most common types of charts include bar, line, heat map, pie, and scatter plots.
• Follow dashboard design principles to ensure your dashboards are effective and communicate the message you want to convey. This includes using appropriate font sizes, colors, and shapes that represent data accurately as well as ensuring there is enough white space between elements for clarity.
• Select the best sheets for your dashboard by considering your data and the message you want to communicate. Tableau provides different sheet types such as maps, text boxes, filter controls, and more.
• Leverage advanced features like tooltips and conditional formatting to add clarity to your visualization. This will enhance user experience when exploring dashboards.
• Utilize parameters and calculated fields to create interactive dashboards. This will allow users to customize their views quickly and easily.
• Make use of story points to guide viewers through an analysis journey, highlighting key insights along the way.

By following these best practices when creating visualizations and designing dashboards with Tableau, you can ensure your visuals are effective in communicating data insights.

Performance Recording is a feature of Tableau that allows for an in-depth analysis of workbook performance. It helps to identify areas of improvement and provides data points that can be used to optimize your Tableau implementation.

Hardware updates are one way to improve performance. Such techniques, including using faster CPUs or increasing RAM, can have a significant impact. On-the-fly techniques, such as reducing the size of viz images and limiting video cache can help to improve responsiveness.

Data sources play an important role in Tableau's performance. When working with multiple data sources, it is best practice to first create a single data source by joining or blending the individual datasets. This results in faster performance time since data sources no longer have to be joined during query execution.

Further optimization can be achieved by using extracts instead of live connections. Extracts are a snapshot of the data which is saved as a Tableau Data Extract (TDE) file. Queries on this type of file will run significantly faster than queries on a live connection.

Filters are an important feature of Tableau, however, their use should be carefully considered. Filtering is done at a data source level, so overusing filters can lead to slower performance times as unnecessary rows and columns are queried. Complex calculations can affect query execution times. To achieve maximum performance, the calculations should be simple and executed at the data source level where possible.

By taking a closer look at the hardware, data sources, extracts, and filters, it is possible to improve Tableau's performance significantly. This will help to ensure the smooth functioning of your Tableau implementation.

Tableau Server is a powerful business intelligence tool that enables organizations to securely share data, rapidly create interactive visualizations, and generate insights from their data. Tableau Server helps teams collaborate, analyze data more quickly, and make better decisions. It can be deployed both on-premise or in the cloud.

Tableau file types (like .twb, .twbx, and .tds) are used to develop dashboards within Tableau Server. The File Repository Service (FRS) stores all files created in Tableau desktop such as workbooks, data sources, extracts, flows, etc., thus providing secure storage for the organization's data.

The Tableau Server architecture consists of several components including the web server, data server, search server, authentication service, and the Tableau file repository. The web server provides access to data visualizations via a web browser and also allows users to interact with their content. The data server is responsible for connecting to databases, query processing, generating results, and populating dashboards. The search server provides indexing of content on the Tableau Server and enables users to quickly locate their assets.

Tableau Server is an invaluable tool that helps businesses make informed decisions by unlocking insights from their data. It provides secure storage of files, efficient query processing, and advanced analytics capabilities which makes it a top choice among organizations.

R Integration with Tableau is a powerful tool for data analysis, allowing users to use R functions and packages directly within Tableau. The integration of these two platforms offers a streamlined approach to visualizing and understanding data through the power of analytics.

To set up R Integration with Tableau, users must first configure their environment correctly. This includes installing the necessary versions of R and Tableau, setting up a connection between the two programs, and ensuring that any additional packages needed are configured correctly. Once all these steps have been completed, users can then start to use the R functions within their Tableau workbooks.

By using R with Tableau, users can gain access to a range of powerful and sophisticated analytics capabilities. This includes predictive modeling, statistical analysis, and advanced visualizations that allow for a more detailed exploration of data. In addition to this, users can also create customized R visuals that can be used within Tableau workbooks.

When using R Integration with Tableau, it is important to ensure that all settings and packages are configured correctly. This will help to prevent any errors or incorrect results when running R functions. Additionally, it is important to stay up-to-date with the latest versions of both Tableau and R to ensure that all features work properly.

If users encounter any issues when using R Integration with Tableau, there are a variety of troubleshooting tools available to help. These can range from basic tips and tricks to more complex solutions that involve updating or reconfiguring either Tableau or R. By taking advantage of these resources, users can quickly address any issues they may have with their integration setup.

R Integration with Tableau is an invaluable tool for data analysis, allowing users to unlock the power of analytics and gain insights that can be used to drive decisions. By ensuring that the integration between Tableau and R is set up correctly, troubleshooting any issues, and staying up-to-date on both programs, users can take full advantage of this powerful combination.