US COVID-19 Cases

During these uncertain times, how can you make sense of the data tsunami being presented on the state of pandemic in US? For the last couple of months, many Americans found themselves checking the spread of COVID-19 cases on a daily basis. As most of US states went into shelter-in-place mode, resources like Johns Hopkins and 91-DIVOC became a daily refuge for those seeking to stay informed. In today’s post, we will work on creating our own version of a web-based, interactive and visually appealing COVID-19 dashboard using Google DataStudio. Doing so we will gain a better understanding of the data used, decide on the type of data we deem most relevant, and maintain control over the best ways to visualize such data to help our audience make most sense of it. In the process of building this data viz, we will utilize various objects and features of the mighty GDS application: Google Sheets connector, Calculated fields, Scorecard, Table, Geo Map, Line and Combo charts, Date range, Filter controls and recently released optional metrics – are some but not all features we will cover.

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First Impressions of using Qlik Sense cloud, using Survey of Business Owners data


First Impressions of using Qlik Sense cloud, using Survey of Business Owners data.

          There is certainly no shortage of various data visualization and BI tools on the market. On this blog we’ve already covered Tableau and Power BI , it’s time for us to review their competition, another leader on Gartner’s Magic Quadrant for Analytics and BI platforms: Qlik , which offers a suite of different BI tools. For the purposes of this post let’s focus on their web-based free product: Qlik Sense – Cloud. Most of the modern data viz programs are supposed to be rather intuitive and very easy to use; so I decided to play with this program without going through the trouble of learning to use it first. American data finder had just the right data set for this experiment: Survey of Business Owners data, which among other things can help us quantify number of companies by size and owner’s gender, see if male vs. female-owned organizations earn higher revenue, employ more workers, and/or pay higher salaries to their employees. Let the data discovery journey begin.

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