Andre Obereigner | Blog
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Tonight Andrew Marritt and I will host our first People Analytics Switzerland meeting at the offices of Ernst & Young in Zurich, Switzerland. The topic will center around gender diversity issues.   Inaugural Meeting - Understanding Gender Diversity Issues January 24 · 6:30 PM EY, Maagplatz 1, Zurich, Switzerland   We're...

Even the best personal laptop reaches its limits when faced with analytics tasks, and that pretty quickly. While contestants of various Kaggle competitions report that they often do pretty well with 4 cores and 8 - 16 GB RAM, my own experience tells me that building many models with even moderate-sized data sets as well as parameter tuning requires a different sort of machine. Amazon and its Elastic Compute Cloud (Amazon EC2) come to our rescue.

The text prediction app is the result from the Coursera Data Science Capstone project in collaboration with SwiftKey. The objective of the capstone project was to (1) build a model that predicts the next term in a sequence of words, and to (2) encapsulate the result in an appropriate user interface using Shiny.  You can try out the Text Prediction App on the Shiny server.

From plenty of unstructured information to valuable insights using text mining and analytics! The text analytics project which I led at Groupon aimed at the successful analysis of survey comments and the accurate prediction of topic labels for each of the comments. I am happy to read that our project was short-listed again - this time for the US Workforce Analytics Award 2016.

From plenty of unstructured information to valuable insights using text mining and analytics! The text analytics project which I led at Groupon aimed at the successful analysis of survey comments and the accurate prediction of topic labels for each of the comments. I am happy to read that our project was short-listed for the European Workforce Analytics Award 2016.

"​In Data We Trust: Improving Data Quality To Add Credibility To People Analytics". That was the topic of the presentation which I gave at the Workforce Analytics Summit sponsored by IBM in New York City in June 2015. Data quality was an important topic at the conference because it represents the very foundation for the analysis of data. While concerns about the completeness and precision of available data should not hold companies back from launching their people analytics initiatives, a continuous and systematic improvement of data quality is essential for building trust in the insights derived from organizational information. My aim was to raise awareness for data quality as a prerequisite for any analytical endeavor in general and workforce analytics in particular.