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For the last few weeks, I’ve been teaching myself how to use Qlik Sense, using a combination of tutorials, documentation, and experimentation. When it comes to building your first app, the first hurdle is finding some data to visualise. Many of the Qlik Sense tutorials available online come complete with sample data for you to follow along with, which is great, but I found that following tutorial instructions only got me so far in terms of learning and developing confidence.

It’s one thing to have an instructor hold your hand and guide you through the app creation process, but to really learn how to use something you need to employ some trial and error. This means finding some data and getting stuck in. But where to find the data? My first thought was to visit NZ Stat, but most of what’s available there are aggregated in a table, when what you really want is unit-level data, like this craft beer dataset that I found on r/datasets. This delightful subreddit is arguably the best place on the internet to go hunting for interesting data to sharpen your teeth on: in under five minutes’ browsing I found datasets of Romanian earthquakes, English drinking habits, Donald Trump’s tweets and, a perfect 10 on my obscure-but-delightful-o-meter, “Images of faces showing expressions of contempt”.

One of the cool things about this diverse collection of datasets freely available for you to play with is how many different formats they’re available in. You can practise loading data in Qlik Sense from CSV, or find a dataset that’s available via an API to have a go at setting up a REST connection. Another great source of clean, interesting data for your learning pleasure is the Qlik data market, which you’ll find in the third tab of the data sources menu. The Essentials Free data sets include currency, demographic, economic and weather data. These are fun to play with by themselves, or you could have a go at linking them up with data you’ve found elsewhere, to practise matching and associations.

I wanted to practice setting up associations, so I loaded up the craft beer and brewery data I’d found in CSV files, then grabbed a table of US state populations in an excel spreadsheet from the United States Census Bureau. To my great disappointment, although the beers and breweries tables linked up very happily, I didn’t have the same joy when I popped the population figures into the data model viewer. This was because the breweries dataset had recorded the states they were in by their postcode abbreviations. I tracked down a table of state name abbreviations and added that to the model. Sure enough, Qlik associated the tables for me with the click of a button.

By linking up the data I’d thrown together in the data manager, I was able to produce some charts to examine the craft beer habits of Americans.

I learned that West Virginia, New Mexico and DC brew their craft beer the bitterest on average; that Nevada, DC and Kentucky brew their craft beers with the highest average alcohol by volume, but Colorado, Indiana and California produce the greatest variety of craft beer types.

Well, that was fun. So which of the US states is the best destination for craft beer enthusiasts? To find out, I threw together a bubble chart to look at which states have the most craft beer breweries per head of population. It turns out the answer is: Vermont!

This was a very simple exercise which I was able to perform in under an hour, and in the process I practised loading data from different sources, making associations, and building some basic visualisations.

If you’re taking your first steps with Qlik Sense, I can’t recommend an exercise like this enough. Finding some fun data to load, associate and chart is the best way to develop confidence and familiarity. Have a go!

Data is beautiful – Sarah

 

Sarah blogs about how data can be made aesthetic as well as informative. 

Want to read more? Try … Sorting it out with dual() or more from Sarah.

We run regular Data Requirements and Agile data warehouse training courses with an Agile business intelligence slant in both Wellington and Auckland

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