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The rules for random things are somewhat different than the ones you know for 'smooth' things. In a university course you get proofs based on Martingales, Wiener measures and whatnot but it all can feel very abstract. Even the basic examples can be confusing. So, here I want to show you that without knowing any high-tech maths you can see from basic examples in R how and that it works.
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The literature and information around monads and categories is sometimes confusing because it has many aspects and depending on the background many overlapping or equivalent terms are used.
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A tutorial on forecasting time series with R.
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Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. In the more narrow context of data science…
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yFiles HTML tutorial with an ASP.NEt MVC backend.
Docker is this wonderful new way to play around with appliances without hurting your machine or stealing away heaps of time.
This is about one of those projects where you have to dig into some new cool technology and wonder what the buzz is about.
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Connecting R to SQL Server is easy on Windows but is kinda challenge on Mac, so I figured out that doing time series analysis on SQL Server itself is also an option.
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Numerical algorithms and statistical theory is quite robust and universal, but once you look into the various software implementations you discover that presumed standards are not so universal.
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The shortlist of graph databases I keep an eye on. Many other NoSQL engines can store graph-like data, of course. Neo4j Neo4j is a graph database boasting massive performance improvements versus relational databases. It is very agile and…