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.
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.

I was recently told that statistical graphics are commonly used throughout the modeling process and that the term "data visualization" appears frequently in conjunction with the term "analytics". The question that followed was: How are statistical graphics used in exploratory data analysis and is there a difference...

— Statistical graphs are central to effective data analysis
My initial thought was that Predictive Analytics refers to an overall field of expertise while Predictive Modeling refers to an activity in which individuals apply potentially relevant mathematical algorithms to data sets in order to learn its structure which can then be applied to new observations to make predictions.