More and more organizations are using Tableau to shine better light on their survey data. In these intensive hands-on sessions participants will learn both best practices in survey visualization and how to use Tableau to render these visualizations.

Topics we will explore include:

• Reshaping and optimizing data for expedited visual analysis (getting the data "just so")
• Tools for reshaping data
• Tableau Prep
• EasyMorph
• Alteryx Designer
• Survey tool web data connectors
• Visualizing demographic data
• Mapping all questions to all possible responses
• Visualizing Yes / No / Maybe data (single punch)
• Visualizing Check-all-that-apply data (multi punch)
• Visualizing sentiment (divergent stacked bars and Top N Boxes)
• Visualizing Likert-scale data
• Net Promoter Score analysis and visualization
• Benchmarking and jitter plots
• Showing rank and magnitude
• On-demand drill down
• Dealing with weighted responses
• Statistical significance and confidence
• Comparing Importance with Satisfaction
• Building interactive dashboards

DAY ONE (November 13)

9:45 AM to 2:15 PM Eastern Daylight Time (UTC -5)

DAY TWO (November 15)

10 AM to 2:15 PM Eastern Daylight Time (UTC -5)

All attendees will receive a 100-page student guide and sample files.

Requirements:

A computer with the following software installed:

• Tableau 10.4 desktop or later
• Microsoft Excel 2010, 2013, or 2016
• A fast internet connection

Highly recommended:

Tableau Prep (you will receive instructions for download).

Prerequisites

Participants should have at least intermediate level understanding of Tableau. For example, you should be able to do the following:

• Connect to non-survey data
• Create visualizations with filters (bar charts, line charts for time series data, etc.)
• Create simple dashboards
• Understand the differences between a dimension and a measure, discrete vs. continuous, etc.
• Be able to create a calculated field.

About the Instructor

Steve Wexler is the founder of Data Revelations and co-author of The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios (Wiley, 2017).

Steve has worked with ADP, Gallup, Deloitte, Convergys, Consumer Reports, The Economist, ConEd, D&B, Marist, Cornell University, Stanford University, Tradeweb, Tiffany, McKinsey & Company, and many other organizations to help them understand and visualize their data. Steve is a five-time Tableau Zen Master, Iron Viz Champion, and Tableau Training Partner.

His presentations and training classes combine an extraordinary level of product mastery with the real-world experience gained through developing thousands of visualizations for dozens of clients. Steve has taught thousands of people in both large and small organizations and is known for conducting his seminars with clarity, patience, and humor.

What people are saying about the class

"A huge thank you to Steve for offering this class. I have struggled with trying to learn Tableau and this class was a godsend. Steve explains things in accessible terms and step-by-step instructions so that I feel like I can really use this software after all. "

"Given Steve’s in-depth practical experience and discussion of many highly relevant real world examples attendees will come away with a full understanding of all steps required to analyze survey analysis in the most effective and flexible way. This class is a must for anyone who is analyzing survey data."

"I’ve been to every Tableau conference since the first, and discovered Steve and his amazing talents early on. Since then, I’ve never missed a presentation. I always learn something useful and innovative about survey analysis. I finally decided to take one of his on-line classes this year, and was really glad I did. It was an opportunity to learn at the foot of a master. Beyond the class itself, Steve was willing to spend time with me personally to walk me through some problems I’d been wrestling with. He has a real desire to teach others what he knows, and is incredibly generous with his time and knowledge."

"Your class was an eye opener on how to better structure our data, leverage calculations/visualizations that are more applicable to our data (% selected, % top rating, etc.) and generally make sense of our data. "