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KDD 2020 Opens Call for Papers

Data Science 101

This weeks guest post comes from KDD (Knowledge Discovery and Data Mining). Honestly, KDD has been promoting data science way before data science was even cool. KDD 2020 is a dual-track conference, offering distinct programming in research and applied data science. 1989 to be exact. The details are below. 22-27, 2020.

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KDD 2020 Call for Research, Applied Data Science Papers

KDnuggets

ACM SIGKDD Invites Industry and Academic Experts to Submit Advancements in Data Mining, Knowledge Discovery and Machine Learning for 26 th Annual Conference in San Diego.

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Fundamentals of Data Mining

Data Science 101

Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). This data alone does not make any sense unless it’s identified to be related in some pattern.

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Data Mining: The Knowledge Discovery of Data

Analytics Vidhya

Introduction We are living in an era of massive data production. When you think about it, almost every device or service we use generates a large amount of data (for example, Facebook processes approximately 500+ terabytes of data per day).

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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

Recently, we presented some basic insights from our effort to measure and predict long-term effects at KDD 2015 [1]. This work has also resulted in advances for item #1, as it helped us define more useful objectives for A/B tests in Search Ads which include long-term impact of experimental treatments.

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Density-Based Clustering

Domino Data Lab

Due to its importance in both theory and applications, this algorithm is one of three algorithms awarded the Test of Time Award at the KDD conference in 2014. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is the most well-known density-based clustering algorithm, first introduced in 1996 by Ester et.

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Using Empirical Bayes to approximate posteriors for large "black box" estimators

The Unofficial Google Data Science Blog

Brendan McMahan et al, "Ad Click Prediction: a View from the Trenches" , Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2013. [3] References [1] Omkar Muralidharan, Amir Najmi "Second Order Calibration: A Simple Way To Get Approximate Posteriors" , Technical Report, Google, 2015. [2]

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