How to Solve the Biggest Challenges in Enterprise AI
90% of data in existence has been generated in the last two years. On a daily basis, 7.5 sextrillion gigabytes of data are generated – around 147,000 gigabytes pe...
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Predictive Analytics and Machine Learning in Retail Industry
Introduction Predictive Analytics and Machine Learning are two related areas that are not mutually dependent, whereas Predictive Analytics can be introduced with or witho...
Upskilling: What Is A Learning Sprint?
Why do we need Learning Sprints? Virtually every company is under pressure to transform their business in order to sustain in the future. As part of these efforts, they a...
Personal updates and DSC
After more than 10 years being involved with Data Science Central, initially as the founder, and most recently being acquired by TechTarget, I have decided to pursue new ...
Is AI Getting Boring? And Some Thoughts on the Future of AI.
Summary: Things are getting repetitious and that can be boring. Still, looking at lessons from the 90s it’s clear there are at least one or two decades of important...
DSC Webinar Series: Natural Language Trends in Visual Analysis
Natural language processing has garnered interest in helping people interact with computer systems to make sense and meaning of the world. In the area of visual analytics...
Visualizing New York City WiFi Access with K-Means Clustering
Visualization has become a key application of data science in the telecommunications industry. Specifically, telecommunication analysis is highly dependent on the use of ...
Fitting Human IQ into AI – Where and When It's a Win-Win
There is much talk of AI automation of many critical business processes over the next decade as the technology matures. Management and workers throughout organizations ar...
Reinforcement Learning Starts to Deliver on Its Promise
Summary: Advances in very low cost compute and Model Based Reinforcement Learning make this modeling technique that much closer to adoption in the practical world. We k...
DSC Webinar Series: Your Model Will Probably Fail (And How to Prevent it)
Data science is more popular than ever, but many data scientists struggle with complicated workflows to run their models as well as how to best communicate the output to ...