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This is Data Analytics testing 101! So you've been dumped into testing software that contains some sort of fancy-pants data analytics. The only problem is, you don't/can't/won't(!?) understand it and nobody is going to pay or wait to catch you up to speed. Where do you get started? What can you test about algorithms without any experience or in-depth knowledge? Is it time to give up and start looking for a new job already? Hopefully, there was a resounding No! to that question.

Daniel will address these questions as he was in a similar situation himself only 12 months ago! Traversing such buzzwords as machine learning, data science and predictive analytics we'll take a look at some simple methods, common pitfalls and general approaches that will make sense out of the data. There's nothing better than proving that testers can test more than we're supposed to!

Takeaways
Learn how to follow the data; not the analytics.
Discover some common ways that data can cause headaches.
Find sources of truth wherever you can.
Confirming correctness is hard but finding flaws is easier.

Daniel hunt
Daniel has been working as a software tester at a data analytics consultancy after a brief foray into games testing. He has worked on a huge range of different projects across a number of different sectors, each with an analytics component.

Despite being primarily charged with testing the software that houses the data analytics, Daniel likes to find problems wherever they present themselves. This often means testing analytics without deeply understanding it!