While the field of data science is not tied directly to Big Data, advances in one tends to produce advances in the other. Big Data increases our ability to harvest and process data, while data science allows us to dig into it for insights.
Bitcoin is currently trading at over $1250 and if you are someone who invested a grand in bitcoins back in 2011, your investments are potentially worth over $600K. The most valuable contribution of the bitcoin community is not in the financial returns itself, but in the introduction of blockchain technology.
Many process manufacturing owner-operators in this next phase of a digital shift have engaged in technology pilots to explore options for reducing costs, meeting regulatory compliance, and/or increasing overall equipment effectiveness (OEE). Despite this transformation, the adoption of advanced analytics tools still presents certain challenges. The extensive and complicated tooling
It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based
Deep neural networks and Deep Learning are powerful and popular algorithms. And a lot of their success lays in the careful design of the neural network architecture. I wanted to revisit the history of neural network design in the last few years and in the context of Deep Learning. For
Financial markets offer countless ways of making (or losing) money. A key distinction among them is the investment horizon, which can range from fractions of a second to years. Walnut Algorithms and Global Systematic Investors are new investment management firms representing the high-frequency and low-frequency sides, respectively. I sat down
For those of you similarly interested (obsessed?) with the changing role of government statistics relative to the explosion of highly dimensional private sector data, I recommend having a look at Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy from the National Academy of Sciences. It’s an easy read and offers a solid
The term AI (Artificial Intelligence) is being thrown around left and right these days. Many companies claim they have an AI play even when they don’t. But there’s another type of AI—an algorithmic approach to intelligence—that is smart and is emerging as the type of AI that IT organizations of
Categorical data is a kind of data which has a predefined set of values. Taking “Child”, “Adult” or “Senior” instead of keeping the age of a person to be a number is one such example of using age as categorical. However, before using categorical data, one must know about various
We’ve published a white paper, where we look back at the big data and business intelligence trends over the past years and highlight examples of successful Data-as-a-Service products with deep dives into Social Media Monitoring, Self-Service BI and Visual Data Discovery and Analytics Merging the Physical and Virtual Worlds, complete with lessons
It used to be a maxim that expanding too fast was the quickest way to kill a successful business. Rapid growth brings risks as well as opportunities. But utilizing augmented intelligence, an already-popular technology which surfaces patterns in data without humans having to look at it, means that organizations can