Data Science And Machine Learning. With Java? | Artificial Intelligence and Financial Services - Finextra

In this blog, I outline briefly: - Common Applications of Data Science - Definitions: Machine learning, deep learning, data engineering and data science - Why Java for data science workflows, for both production and research.

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Steve Wilcockson, Senior Director - Java Products at Azul Systems explains,  Common Applications of Data Science
-      Definitions: Machine learning, deep learning, data engineering and data science
-      Why Java for data science workflows, for both production and research.

Photo: JumpStory

Common Applications of Data Science
The blogosphere is full of descriptions about how data science and “AI’ is changing the world. In financial services, applications include personalized financial offers, fraud detection, risk assessment (e.g. loans), portfolio analysis and trading strategies, but technologies are relevant elsewhere, e.g. customer churn in telecomms, personalized treatment in healthcare, predictive maintenance for manufacturers, and demand forecasting in retail...

Key Definitions: Machine Learning, Data Science, etc
For practitioners, definitions are well understood. For those less familiar and curious, here are some quick definitions and introductions to baseline everyone.
At their heart, data science workflows transform data, from heterogenous sources of information, through models and learning, to derive information from which “useful” decisions can be expedited.
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Source: Finextra