A panel of experts discuss how to professionalize the data science discipline.

Data Science is a structured approach to extracting valuable insights from data, and it involves several key stages to ensure success. Let's explore each phase in detail: By following this structured ...

Discover what data science is, its benefits, techniques, and real-world use cases in this comprehensive guide.

In data science, that is not the case. During the data science creation phase, a complex process has been built that optimizes how and which data are being combined and transformed.

The past of data science was all about descriptive analytics, or describing what has already taken place, Gualtieri says. But the future of data science will hinge on advanced analytics—specifically ...

Kaskada says it aims to democratize feature engineering, an often laborious process that requires data scientists to select, clean and validate the data to be fed into machine learning training ...

Researchers study how organizations can quantify decision making in data science and present a solution to quantifying the value of data acquisition and modeling in a return on investment (ROI ...

AI is at the heart of a massive transformation for the modern enterprise. However, managing the data science process that powers AI can be time-consuming, frustrating, and expensive. Advanced ...

John W. Baker, Steve Henderson, The Cyber Data Science Process, The Cyber Defense Review, Vol. 2, No. 2 (SUMMER 2017), pp. 47-68 ...

Deploying data science into production is still a big challenge. Not only does the deployed data science need to be updated frequently but available data sources and types change rapidly, as do ...