First event: business ideas, practical reproducible analysis, shiny development!

Hi everyone! We're kicking off our first event in October, sponsored by trivago (https://www.trivago.com/), with three short talks:

• How to find, evaluate and launch data business ideas (Daniel Kupka)
• Practical reproducibility of analyses (Alex Dolphin)
• Shiny development in an academic vs business setting (Praer Owlarn)

*** Agenda ***
• 18:30 - 19:00: Arrive, get a drink and socialise
• 19:00 - 19:10: Quick introduction
• 19:10 - 19:30: Daniel's Talk + Q&A
• 19:30 - 19:50: Alex's Talk + Q&A
• 19:50 - 20:10: Praer's Talk + Q&A
• 20:10 - Open End: Socialising

Please remember to bring your ID card - our security company requires it to provide you a guest badge!

*** How to find, evaluate and launch data business ideas (Daniel Kupka) ***

"For every business advice, there is equally someone smart who says the opposite" - Jason Cohen, Founder WPEngine.
This is not going to be a lecture on the latest and hottest startup advice but rather a story on how I came up with my business idea, evaluated it´s potential using R and tested it with customers in real life.

Daniel Kupka is an Economist by training and an entrepreneur at heart. After leaving his job at OECD without a plan, he tried various business models to pursue the "entrepreneurial dream" while freelancing on the side. But after much trial-and-error, FrontPage Data (https://frontpagedata.com), a Data Journalism on-Demand service, got some traction in the content marketing niche.

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*** Practical reproducibility of analyses (Alex Dolphin) ***

In the pursuit of robust, reproducible, and extendable analyses, one key aspect frequently gets overlooked: practicality. Reproducible analyses with a potential for good review are often cumbersome to write: they may require branches to be pulled, environments to be built, and variables to be changed in a well-hidden file. I will propose a step towards greater practicality with an example using three technologies many data scientists know to some extent:

• RMarkdown
• R shiny server
• Docker

By presenting this concept I hope to demonstrate that it is possible for:

• data scientists to review the live code with one link to a shiny-rendered page
• non-data-scientists to rerun and extend the analysis through an intuitive GUI
• the data scientist doing the task to achieve this only writing one file in their preferred coding language

Alex Dolphin is a Data Scientist in the Marketplace Intelligence team of trivago. He has worked for 3 years in the Data Science function, and comes from a background of Mathematics at university. He believes that data scientists should have access to highly-reliable data and well-maintained tools as a minimum to produce quality output.

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*** Shiny development in an academic vs business setting (Praer Owlarn) ***

Shiny is a popular R package for building interactive web apps – no web development knowledge required (https://shiny.rstudio.com)! Its ease-of-use and extendibility makes it popular in both academic and business settings, but implementation in the two environments can be rather different. I’ll share my experience writing shiny apps at a Max Planck Institute (a non-profit research institute) and trivago (a German hotel search engine), and how differences in task complexity, IT infrastructure and organisational mindset affected my approaches and the packages I used.

Praer Owlarn is a data analyst at trivago. After a decade as a biologist, she decided to learn a little R and immediately fell in love! Soon, she waved goodbye to the bench and is now having fun diving into big data sets, exploring/contributing to R packages and building shiny apps.

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Thanks to trivago for hosting & sponsoring this edition of the Düsseldorf R User Group Meetup. There will be food and drinks available.