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This weeks guest post comes from KDD (KnowledgeDiscovery and Data Mining). Every year they host an excellent and influential conference focusing on many areas of datascience. Honestly, KDD has been promoting datascience way before datascience was even cool. 1989 to be exact.
This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as KnowledgeDiscovery from Data (KDD). You might be wondering what benefit you can get out of these techniques?
For super rookies, the first task is to understand what data analysis is. Data analysis is a type of knowledgediscovery that gains insights from data and drives business decisions. One is how to gain insights from the data. Data is cold and can’t speak. From Google. There are two points here.
For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. One reason to do ramp-up is to mitigate the risk of never before seen arms.
This tutorial will show how easy it is to integrate and use Pumas in the Domino DataScience Platform , and we will carry out a simple non-compartmental analysis using a freely available dataset. The Domino datascience platform empowers data scientists to develop and deliver models with open access to the tools they love.
These companies often undertake large datascience efforts in order to shift from “data-driven” to “model-driven” operations, and to provide model-underpinned insights to the business. The typical datascience journey for a company starts with a small team that is tasked with a handful of specific problems.
by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of datascience. Because individual observations have so little information, statistical significance remains important to assess. We must therefore maintain statistical rigor in quantifying experimental uncertainty.
In this post we explore why some standard statistical techniques to reduce variance are often ineffective in this “data-rich, information-poor” realm. Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant.
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