Data Science @ Piano (Roman Gavuliak)
Piano, despite being a tech company puts a lot of focus on analytics. I would like to share some learnings we’ve gathered along the way for the following steps:
Data: Analytics friendly data and data friendly analysts Tools: Right tools for the right job Methods: How can programming knowledge provide more robust statistical results Outcomes: Some interesting data products under development.
Mathematics, surprisingly ( David Majda, jut.io)
Do programmers need advanced mathematics? And what if they are designing and implementing a programming language? In the talk I will show where I needed mathematics in my work at Jut and how bad job I would have done without understanding it. You can think of this as a small contribution to a discussion about education of programmers and the role of mathematics and other theoretical subjects in it.
Slevomat case study ( Peter Irikovsky, ex-Slevomat.cz) - activation and reactivation campaigns (GoodData) - identification of important segments - tailoring the message - key takeaways - identification of web optimisation potential (GA, Segmentacia, GoodData) - key improvements - optimising offer structuring (GA, GoodData) - simple processes - multiple views - great process - optimising mailing (GoodData) - data vs customer feedback - key improvements
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