Kai Waehner

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Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is a game changer in analytics, when to use it, and how Visual Analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist.

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I want to highlight a new presentation about Data Preparation in Data Science projects:

“Comparison of Programming Languages, Frameworks and Tools for Data Preprocessing and (Inline) Data Wrangling  in Machine Learning / Deep Learning Projects”

Data Preparation as Key for Success in Data Science Projects

A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project.