Simple notes to consider when creating a visualization

Recently I finished a specialization named "Data Visualization with Tableau Specialization" in Coursera, and I believe it was a very comprehensive review of what a person needs to know to start to work with Tableau and also design principles a person needs to consider when creating a clear and insightful dashboard with "any" VIZ tool. In what follows, I have summarized the important notes of the first course of this specialization whose name is "Fundamental of Visualization with Tableau". Here are the few steps that anyone who wants to find insights out of a dataset should follow:

1. Briefly look at the data and dataset

   Take a brief look at your data and try to understand the columns. If you have a document describing the rows and columns, it is in your best interest to read it at least shortly to gain an overview of your dataset.

2. Identify the potential questions
   Try to list several questions regarding the dataset and the context you are working with. Of course, while you are trying to answer these initial questions, you probably face other questions and as you go on the whole context becomes more clear and you will find yourself closer to the most important insights of the dataset. So do not let your possibly short list of questions stop you in this step, push yourself to the next steps and dive into the "data exploration" part.

3. Create some simple sheets and find simple insights

   Try not to be so preoccupied with the style and picky with the correct color of your sheets at this stage. There is a very high chance that you change the color palette as you get more insights from the dataset and reach the end of your viz project. Instead, try to create some rudimentary charts and tables based on your initial questions, understand the relations among the columns and find out the probable need to create other calculated variables to provide better insights.

Make the data pop out

Clean your chart to the point that your audience can get the message; The more we omit the cluttered elements in the chart the more our data pops out. While omitting the unnecessary parts strongly depends on the context that you are working with, applying the following list of actions usually makes sense in more of them:

• Assign clear names to your sheets when you are done with them (Even if you are most likely to delete them later in the process). This simple step makes the whole Exploration process easier.

• Remove the unnecessary axes

• Remove unnecessary axis titles

• Shorten Month names (January --> J, February --> F, etc.)

• Remove unnecessary headers

• If needed benefit from filter action(s)

• Ask others to comment on the readability and understandability of your viz (If your audience gets what you are talking about, you've done good work)

• Keep in mind that people from West world read from left to write. Therefore, if it's possible put the most important charts (interaction pattern) in the upper left and the least important ones towards the lower right. We also put

Finally, the course introduces "The top five Data VIZ best practices" which are:

• Know your audience

• Know your data

• Use color purposefully

• Less is more; Simpler is usually better

• Get feedback early and often

Although this course very shortly focuses on the technical part of Tableau, it gives the audience a very great view of what is data visualization as a whole and how to work with the very basic features of Tableau. If a person wants to learn more about the Basic Features of Tableau in a short amount of time, I recommend them to follow the first course of this specialization and then skip to the third course and follow that one as well.

I will continue summarizing the course contents of this specialization.

Best of luck.

Cover credit: BING search engine