VizWiz

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August 6, 2025

Tableau Tutorial for Beginners (2025)

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Want to learn Tableau fast?

Start with this beginner-friendly tutorial where I'll show you how to import data, clean it, build your first interactive dashboard, and share your dashboard—all using Tableau Desktop.

📚 RESOURCES
• 📂 Sample Excel file to follow along (thanks Kevin Stratvert)
• 📊 Workbook

📺 RELATED VIDEOS
• Mastering Containers
• 370 FREE Tableau Tutorials

📩 NEWSLETTER
Each week, I'll send you Tableau tips, tutorials, and strategies to design better dashboards, have more impactful, and stand out in your career: https://nlt.kit.com/

🔽 CONNECT WITH ME
• Official website
• LinkedIn


February 20, 2025

How to Create a Multi Column Dot Plot

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Want to make comparing multiple measures across multiple dimensions in Tableau easy to understand? Traditional charts can quickly get messy and hard to interpret, but a Multi-Column Dot Plot offers a simple, effective solution! In this tutorial, you’ll learn how to:
✅ Build a Multi-Column Dot Plot step-by-step in Tableau ✅ Compare multiple measures across multiple dimensions ✅ Plot each dot on a 0% to 100% scale for standardized comparisons ✅ Deliver impactful insights with minimal visual clutter Download the workbook & data source: 🔗 Workbook 🔗 Data (requires free account)
🚀 Take your Tableau skills to the next level with Next-Level Tableau, a community-based membership that offers: ✅ Exclusive live classes with a Tableau Visionary Hall of Fame member ✅ Mastering advanced Tableau techniques to solve business problems ✅ Access to a supportive community to exchange ideas and never feel stuck 📊 Become a Tableau expert today! 👉 https://nextleveltableau.com/

October 24, 2023

Master Containers: Build an Interactive Tableau Dashboard From Scratch

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In this video you will learn step-by-step how to create an interactive Tableau dashboard. 

We'll start with a 2 datasets:

1. Beverage sales
2. Custom shape for US states

I will show you how relationships work in Tableau to combine these two datasets.

Once we create the data set, we'll build all of the charts we need, create a parameter, and apply the filters.

We’ll create:

1. Summary key figures (KPIs)
2. A custom map of the US
3. A monthly breakdown as a line chart
4. Two bar charts for products and customers

Lastly, we'll make the dashboard dynamic and interactive using different filter types and a parameter and make sure everything updates automatically.

As a bonus, you’ll learn a lot about working with containers in Tableau.

Resources:
1. Data
3. Workbook (or click the image below)


April 24, 2017

Makeover Monday: Which data skills are HOT where you live?

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This week Tableau joins us for a special edition of Makeover Monday. One of their very own, Marissa Michelotti, published this viz on Tableau Public and volunteered it for the Makeover Monday Community to improve upon.


What works well?

  • Clear title that tells me what the viz is about
  • Can easily find the top 10 job skills in any country (but there are some problems with this too).
  • Keyword search in the box & whisker plot
  • Overall layout is simple

What could be improved?

  • What do the colors mean in the bump chart? There's no legend.
  • The bump chart cuts off jobs when they aren't in the top 10, making it look like they didn't exist in the other years.
  • The country filter only applies to the top chart. I didn't realize that. I think it could be more clear.
  • Box & whisker plots are hard to understand for most audiences. And using one for data that is ranks doesn't make a lot of sense to me since you really can't tell how much better the top is from the bottom with ranks.
  • How are the jobs organized in the bottom chart? Is there a logic to the sort?
  • What's the scale of the box & whisker plot? It looks half cut off to me.
  • For most people, they probably won't care how their country compares to other countries. I would think it's more important to understand which jobs are hot where you live.
  • There are a LOT of missing roles in 2016 that existed in 2015 & 2014. I would remove 2016 since it's not comparable to the other years.

Much like Eva, I decide to make this a tool people could use. If someone lives in the U.S., they probably don't care which jobs are hot in Singapore. Or maybe they do care about Singapore because they want to move there. My viz is intended to help people find the hot jobs that are geographically important to them.

Click on the image for the interactive version.

May 4, 2016

Data+Women: Women are Underrepresented on Tech Boards

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I was listening to the latest Tableau Wannabe Podcast about Women in Data Month and Emily mentioned how Tableau has no females on its Board of Directors. I’m also preparing to speak at the first Data+Women London meetup tomorrow, so I wanted to educate myself a bit and also verify Emily's comment.

I looked on Google Finance at Tableau to get a list of comparable companies. I then included some more big tech companies from Silicon Valley for comparison purposes. The data is shocking!

Of the 17 companies I selected:

  1. Only seven (7) have boards with at least 25% female composition
  2. 0% of the companies have 50% representation of females
  3. Tableau and MicroStrategy have exactly zero (0) female members on their boards


This is sad, truly sad. My message to the leaders of these companies: “Lean in!"

April 10, 2016

Dear Data Two | Week 49: Data

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At the start of this week, I attempted to follow the lead set by Stefanie and Giorgia during their week 49 and track every time I heard, said or wrote the word “data”. Tracking swear words was difficult enough and I quickly realized there was no way I would be able to keep up with “data”, there’s simply too much of it.

It was Tuesday and I couldn’t really start a new topic because I’d be missing a day. Instead, I decided to look at the data I had created through the first 48 week of Dear Data Two. There are four main types of data I create every week: raw data (usually in Excel), Tableau extracts, Tableau workbooks, and scanned images. As you click through the story below, you’ll see I’ve created nearly 1GB of data through 48 weeks, 95% from pictures.

This means that the data sets I had been working with have mostly been small. And this revealed a problem I hadn’t know: my Tableau extract were actually BIGGER than the raw data. I always had it in my head that Tableau extracts would be smaller than the source data, but in 42/45 weeks, that wasn’t the case.

Some summary stats:

  • 357 pictures = 926.4 MB
  • Raw data = 3.5 MB
  • TDEs created = 3.9 MB
  • TDEs were on average 10% bigger than the raw data
  • 45 Tableau workbooks = 36.2 MB


Overall, it was fun to analyse the data of Dear Data Two. Flip through the story below to see my take.

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