## April 19, 2021

## April 13, 2021

## How to Calculate a Z-Score

- If a Z-score is 0, it indicates that the data point's score is identical to the mean score.
- A Z-score of 1.0 would indicate a value that is one standard deviation from the mean.
- Z-scores may be positive or negative, with a positive value indicating the score is above the mean and a negative score indicating it is below the mean.

## April 12, 2021

## April 8, 2021

## How to Create a Ternary Graph / Triangular Chart

## April 5, 2021

## #MakeoverMonday 2021 Week 14 - Multiclass Classification of Dry Beans

## March 29, 2021

## #MakeoverMonday Week 13 - UK Trade With the EU Since the Brexit Referendum

I must admit I got a bit stuck with this week's dataset. It was very straightforward and I didn't like how anything turned out. With some ideas from the live audience on YouTube, I started comparing exports, imports and the trade balance. I then thought about a trick I had taught the Data School about using a timeline to filter.

Great, that's it! Wrong! The layout I had in my head was all wrong. I had to move some things around and then getting everything to line up took ages! Why or why isn't formatting easier???

Then, after creating the BANs, someone suggested that it would be good to show a zoomed in version of the line chart for the dates selected. I added those as sparklines with each BAN and it turned out so much better.

Check out and download the viz here.

## March 26, 2021

## #WOW2021 Week 5 - Predicting HBCU Future Enrollment

Workout Wednesday 2021 week 5 required you to become familiar with the statistical functions in Tableau as well as being able to create predictions based on those stats. I hadn't done either of these before, so I knew it would be a good learning opportunity.

View the dashboard here |

Building the chart itself was simple. I chose to NOT truncate the axis as Candra did because it's not best practice to truncate the axis of an area chart as it skews the magnitude of change across time.

To create the Gaussian process regression, I found information on Tableau's website about the calculation and how to configure it. For Gaussian regression, the help says to use this formula:

MODEL_PERCENTILE(

"model=gp",

AVG([Days to Ship Actual]),

ATTR(DATETRUNC('month',([Order Date])))

)

However, when I did so, the chart and values were not the same as Candra's. So I used the MODEL_QUANTILE function instead. As always, the help within the calculation window was immensely useful.

Great, I now had the line chart. But I couldn't figure out how to get the prediction to extends another five years. A Google search for "predicting the future Tableau" sent me to this link.

Some formatting, a few calcs to get the tooltips and title correct, and a sheet to trigger the change of the measure with a parameter action and done! Check out my solution here.

View the dashboard here |

## March 23, 2021

## How to Create a Parallel Coordinates Plot Over Time

## March 22, 2021

## #MakeoverMonday Week 12 - How much do Americans spend on cereals?

#### Viz 1 - Year over Year Change in Consumption of Food and Beverages in America

#### Viz 2 - Parallel Coordinates - How much do Americans spend on cereals relative to other products?

#### Viz 3 - Bump Chart - #MakeoverMonday 2021 Week 12 - How Does Cereal Rank in American Food Spending?

## #WorkoutWednesday 2021 Week 11 - Gapminder: Income vs. Life Expectancy

What you should see, though, is that the headers are in the first row. To fix that, click on the drop down triangle next to the unioned data sources and choose

*Field names are in first row*.

Now that the data is pivoted, in order to build the view, you need to create a calculated field for each measure: life expectancy, population, and income

## March 19, 2021

## #WorkoutWednesday 2021 - Week 2: Customer Lifetime Value (CLTV) Matrix

If you like a table calc challenge, this Workout Wednesday is for you. Get Ann's requirements here. On the surface it seems pretty simple:

- Get the first order date for each customer.
- Determine the number of quarters that elapsed since then.
- Calculate the cumulative value of each cohort.