Deep learning is an increasingly important part of the AI toolkit, yet it is often misunderstood.
- By Brian J. Dooley
- February 23, 2017
Statistical models and machine learning algorithms are often mysterious and confusing for average business and data professionals. However, even the most math-adverse can reach some understanding.
- By William McKnight
- February 22, 2017
Data lakes have been widely misunderstood, but they are now benefiting from new best practices -- helping organizations gain significant value.
- By Dale Kim
- February 16, 2017
Data science today is extremely labor-intensive. The automated or quasi-automated features widely used in self-service BI aren't commonplace in data science, but Gartner says that's about to change.
With the increasing demand for data science professionals, what skills are necessary to succeed in our new digital world?
- By Devavrat Shah
- February 14, 2017
Three things that distinguish data prep from the traditional extract, transform, and load process.
- By Wei Zheng
- February 10, 2017
To get full business value from big data and other new data sources, many organizations use a data lake atop Hadoop to capture, process, and manage diverse data at scale for business analytics.
- By Philip Russom
- February 2, 2017
As enterprises look to put their best foot forward in 2017, many are increasingly turning to text analysis to improve customer experiences and business processes.
- By Terry Lawlor
- February 1, 2017