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Fundamentals of Data Mining

Data Science 101

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 Knowledge Discovery from Data (KDD). Machine learning provides the technical basis for data mining.

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Data Mining Use Cases

TDAN

Information is the oil of the 21st century, and analytics is the combustion engine,” says Peter Sondergaard, former Global Head of Research at Gartner. Given that the global big data market is forecast to be valued at $103 billion in 2027, it’s worth noticing. As the amount of data generated […]. And he has a point.

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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). Every year they host an excellent and influential conference focusing on many areas of data science. Honestly, KDD has been promoting data science way before data science was even cool. 1989 to be exact. The details are below.

KDD 81
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Business Intelligence System: Definition, Application & Practice

FineReport

With the advancement of information construction, enterprises have accumulated massive data base. Because the greater the amount of data, the greater the value of the data that can be obtained. Companies employ BI systems to deliver right information to right person at the right time with a right format.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

The unreasonable effectiveness of data. The electrotopological state: Structure information at the atomic level for molecular graphs. Data mining for direct marketing: Problems and solutions. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79.

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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

A/B testing is used widely in information technology companies to guide product development and improvements. References [1] Henning Hohnhold, Deirdre O'Brien, Diane Tang, Focus on the Long-Term: It's better for Users and Business , Proceedings 21st Conference on Knowledge Discovery and Data Mining, 2015. [2]

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

Since so many users were given the feature in earlier epochs, there’s a lot of information being left on the table if you were to ignore these epochs. So combining data from epochs is attractive. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2017. [4]