We provide an overview of data science methods for industrial analytics. Data science for business provost pdf propose a toolbox for predictive modeling in manufacturing.
The toolbox is illustrated by means of a large-scale real world case study. Manufacturing companies today have access to a vast number of data sources providing gigantic amounts of process and status data. Consequently, the need for analytical information systems is ever-growing to guide corporate decision-making. However, decision-makers in production environments are still very much focused on static, explanatory modeling provided by business intelligence suites instead of embracing the opportunities offered by predictive analytics. We develop a data science toolbox for manufacturing prediction tasks to bridge the gap between machine learning research and concrete practical needs. We provide guidelines and best practices for modeling, feature engineering and interpretation.