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Generative and Analytical Models for Data Analysis · Simply Statistics
Describing how a data analysis is created is a topic of keen interest to me and there are a few different ways to think about it. Two different ways of thinking about data analysis are what I call the “generative” approach and the “analytical” approach. Another, more informal, way that I like to think about these approaches is as the “biological” model and the “physician” model. Reading through the literature on the process of data analysis, I’ve noticed that many seem to focus on the former rather than the latter and I think that presents an opportunity for new and interesting work.
r  dataanalytics  advice 
14 days ago by markogara
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Am Beispiel : Die zentrale Bedeutung von für den modernen Profifußball. Von…
LiverpoolFC  DataAnalytics  from twitter
4 weeks ago by joha04
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A5. solutions are changing virtually every industry. I'm excited because now we can take , t…
DataAnalytics  BigData  from twitter_favs
5 weeks ago by TomRaftery
XGBoost Algorithm – Towards Data Science
XGBoost algorithm was developed as a research project at the University of Washington. Tianqi Chen and Carlos Guestrin presented their paper at SIGKDD Conference in 2016 and caught the Machine Learning world by fire. Since its introduction, this algorithm has not only been credited with winning numerous Kaggle competitions but also for being the driving force under the hood for several cutting-edge industry applications.
article  xgboost  dataanalytics  datascience  algorithm 
7 weeks ago by markogara
If Correlation Doesn’t Imply Causation then What Does?
https://www.dropbox.com/s/4t8n5zfdhjcq0i0/If%20correlation%20doesn%E2%80%99t%20imply%20causation%2C%20then%20what%20does%20%20DDI.pdf?dl=0

Of course, while it’s all very well to piously state that correlation doesn’t imply causation, it does leave us with a conundrum: under what conditions, exactly, can we use experimental data to deduce a causal relationship between two or more variables?
...
Over the past few decades, a group of scientists have developed a theory of causal inference intended to address these and other related questions. This theory can be thought of as an algebra or language for reasoning about cause and effect. Many elements of the theory have been laid out in a famous book by one of the main contributors to the theory, Judea Pearl. Although the theory of causal inference is not yet fully formed, and is still undergoing development, what has already been accomplished is interesting and worth understanding.
Statistics  DataScience  DataAnalytics  Correlation  Causation  MichaelNielsen  DDI 
7 weeks ago by richardwinter
Twitter
AI Technology

* Identifyies relevant issues
* Uses solid methodology
*…
Healthcare  ConsumerHealth  DataAnalytics  from twitter
9 weeks ago by jhill5
(429) https://twitter.com/i/web/status/1118567578107043845
If you know your insights via - you help clients make a critical shift to measure…
DataScience  DataAnalytics  from twitter
9 weeks ago by jhill5
Twitter
If your business depends on , your isn't the end goal are.…
conversions  Sales  DataAnalytics  from twitter
10 weeks ago by jhill5

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