What is Differential privacy?

Differential privacy seeks to protect individual data values by adding statistical “noise” to the analysis process. The math involved in adding the noise is complex, but the principle is fairly intuitive – the noise ensures that data aggregations stay statistically consistent with the actual data values allowing for some random variation, but make it impossible to work out the individual values from the aggregated data. In addition, the noise is different for each analysis, so the results are non-deterministic – in other words, two analyses that perform the same aggregation may produce slightly different results.

Understand Differential Privacy!

Think Data Analysis.com

Recently I decided to move from AWS to Azure for the hosting of my “Sandbox” sites. With the move, I plan to add serve up live interactive data content highlighting different “data” projects of personal and professional interest.

Much of what I’ve worked on is contained on “corporate” portals and intranets. The move to Azure from AWS for “server based” content will allow more flexibility and access to Power BI, SharePoint and Microsoft Teams.

15 Years since Hans Rosling first excited the world!

Classic Hans Rosling Bubble Chart – on main Tableau page!

I had a chance to take a look today at Tableau Public. I’ve used it in the past but to be honest Tableau is my third favorite visualization tool (behind Qlik and Power BI). I was impressed to see they used an example mimicking the famous TED talk by Hans Rosling. If you haven’t seen this and you’re interested in data and analytics – its a must watch! 🙂

Nathan Yau’s Reading List

I follow and really like Nathan Yau. His site FlowingData.com is a great resource for Data Visualization inspiration. Below is his reading list for during the crisis:

Making Charts

Books specifically about making and using charts…


Making sense of numbers…


Some code…

  • R Packages by Hadley Wickham — I know the basics, but I should know more.
  • The Book of R by Tilman M. Davies — A big, fat reference.
  • Some visualization with Python book. I’ve seen some books, but is there a well-regarded reference?


Outside visualization, but applicable…


To think about various visual forms…

Dashboard Comparison

A took a quick look at the COVID-19 data using Power BI and Qlik Sense. Both have their advantages – but are using the same dataset. A shared table in Snowflake (CT_US_COVID_TESTS).


This is a difficult time for many of us. My thoughts are first with all of the people suffering from the disease and it’s impact. Also with the heroic first responders, the men and women who are risking their own lives to save others.
There are a number of interesting site I’ve been following to get more info. The site below does a great job of explaining the growth of the virus and how to tell if we are flatting the curve.

The original Johns Hopkins site uses a map delivered by ESRI and ArcGIS to track the progression of the virus: