Your machine learning model is only as good as the data you feed into it. That makes data preparation for machine learning (or cleaning, wrangling, cleansing,
Congratulations! You’ve embraced machine learning and data science and your organization is well on its way to building a system that helps you deploy predictive analytics
Pinpointing the most useful machine learning data and figuring out how to combine sources to create accurate, meaningful models is one of the most business-critical areas
The previous decade has been one of impressive growth and massive profits for financial services providers. As the global economy bounced back from the recession, new
"Only a crisis — actual or perceived — produces real change." - Milton Friedman The start of 2020 has been one of the most turbulent on
There are some insights that can only come from the data you produce or collect in-house. Historical sales figures, for example. Foot traffic through your store.
Data enrichment is a crucial step in the modeling process that data scientists tend to overlook due to the difficulty in finding and utilizing external sources.
You don’t know what you don’t know, as the old saying goes. But in the age of Big Data, you simply can’t afford to shrug off
If you’re familiar with Explorium then you know that we believe the core challenge for data science is data. Specifically, we believe data scientists need more
