This course will take you through the various parts of analytical dashboarding: from best practices for designing a dashboard, creating a unified analytical environment, to deploying and publishing visualizations. We will briefly discuss the advanced visualization techniques and you will develop an information layout of the biggest gainers and losers in the financial markets and compare those movements to the economic data as your capstone project.
This course is part of the Use Tableau for Your Data Science Workflow Specialization
Offered By


About this Course
Offered by

University of California, Irvine
Since 1965, the University of California, Irvine has combined the strengths of a major research university with the bounty of an incomparable Southern California location. UCI’s unyielding commitment to rigorous academics, cutting-edge research, and leadership and character development makes the campus a driving force for innovation and discovery that serves our local, national and global communities in many ways.
Syllabus - What you will learn from this course
From Data to Visual Understanding
Now that you know the primary visualization methods and how to address the action and interaction patterns necessary for visual analytics, it’s time to put it all together. What are the principles of good dashboard design and how do you determine the most effective dashboard to communicate your story? In this module, we’ll take a look at the considerations and processes for creating an analytical dashboard.
Analytical Dashboarding
After the target audience, objectives, and principles have been clearly defined, creating the dashboard itself is less of a technical exercise and more of a design challenge. This module brings all these elements together into a unified analytical environment. You’ll begin to put everything together in Tableau and design your dashboard.
Deploying and Publishing
In the past, deploying and publishing visual analytics was challenging for many organizations. The underlying data management and engineering pipelines were much less capable than today’s backend systems. In this module, we’ll investigate what to do as new analytical platforms enter the market and mature over time, how to stand out as a data visualization expert, and discuss considerations for creating custom visualizations.
Going Beyond
Consider what you’ve learned so far in this specialization. How might you use geospatial analysis or network analysis in your data science workflow? In addition, how might you design visuals to explain how data has changed over time? In this module, we’ll briefly discuss advanced features that can be used to answer these questions in Tableau. As you reflect on the course content, you will also finalize your capstone project, making use of storytelling principles and best practices for focusing and decluttering, and develop an information layout.
About the Use Tableau for Your Data Science Workflow Specialization
This specialization covers the foundations of visualization in the context of the data science workflow. Through the application of interactive visual analytics, students will learn how to extract structure from historical data and present key points through graphical storytelling. Additional topics include data manipulation, visualization foundations, audience identification, ethical considerations, dashboard creation, and report generation. Demonstrations of the basic visualization techniques used in Tableau will be included with a hands-on project.

Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I subscribe to this Specialization?
Is financial aid available?
More questions? Visit the Learner Help Center.