Recently we had the 17th edition of our Data & AI Meetup. This meetup focused on Data & AI in Healthcare. Let’s have a quick recap!
16:00 – Willkommen & Intro
16:05 – BI as a Service für eine bessere Healthcare Supply Chain
Christopher Glogger, Sana Einkauf & Logistik
16:35 – AI Trends und Use cases
Andreas Kopp, Microsoft
17:20 – Visuelle Datenanalyse rund um CoViD-19
Markus Raatz, Ceteris AG
17:55 – Wrap up
Darren Cooper and I had the pleasure to welcome 200 Data & AI enthusiasts! Furthermore, we were happy to announce that our Data & AI Meetup group has 1,070 members and our brand new Data & AI LinkedIn group already has 580 members.
Reinforcement Learning of Train Dispatching at Deutsche Bahn
Dr. Tobias Keller, Data Scientist at DB Systel, showed in his session how Deutsche Bahn aims at increasing the speed of the suburban railway system in Stuttgart (S-Bahn) using Artificial Intelligence. In particular, a simulation-based reinforcement learning approach provides promising first results.
Sascha Dittmann, Cloud Solution Architect for Advanced Analytics & AI at Microsoft, showed in his presentation, how TensorFlow and other ML frameworks can be used better in a team through appropriate Microsoft Cloud services. He presented different ways of how data science experiments can be documented and shared in a team. He also covered topics such as versioning of the ML models, as well as the operationalization of the models in production.
Visual Analytics: from messy data to insightful visualization
Daniel Weikert, Expert Consultant at SIEGER Consulting, showed in his session the ease of use of Microsoft Power BI Desktop. He briefly highlighted the AI Capabilities which Power BI provides and showed a way on how to get started with messy data, doing data cleaning and visualize results in an appealing way to your audience.
How can a Tableau dashboard that displays contacts (name & company) automatically lookup LinkedIn profile URLs?
Of course, researching LinkedIn profiles for a huge list of people is a very repetitive task. So let’s find a solution to improve this workflow…
1. Python and TabPy
We use Python to build API requests, communicate with Azure Cognitive Services and to verify the returned search results. In order to use Python within Tableau, we need to setup TabPy. If you haven’t done this yet: checkout my TabPy tutorial.
2. Microsoft Azure Cognitive Services
One of many APIs provided by Azure Cognitive Services is the Web Search API. We use this API to search for name + company + “linkedin”. The first three results are then validated by our Python script. One of the results should contain the corresponding LinkedIn profile.
3. Calculated Field in Tableau
Let’s wrap our Python script together and create a Calculated Field in Tableau:
4. Tableau dashboard with URL action
Adding a URL action with our new Calculated Field will do the trick. Now you can click on the LinkedIn icon and a new browser tab (or the LinkedIn app if installed) opens.
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