While I have talked frequently about the concept of Analytic Profiles, I’ve never written a blog that details how Analytic Profiles work. So let’s create a “Day in the Life” of an Analytic Profile to explain how an Analytic Profile works to capture and “monetize” your analytic assets.
Python is a general-purpose language — sometimes referred to as utilitarian — which is designed to be simple to read and write. The point that it’s not a complex language is important.
While about 60% of KDnuggets readers think AI and Automation will improve society, the optimism drops significantly among those with 4 or more years experience developing AI systems. Should we pay more attention to the experts?
AI can now see, hear, and even bluff better than most people. We look into what is new and real about AI and Deep Learning, and what is hype or misinformation.
Here are three lessons for making and demonstrating a greater business impact to your organization, according to Domino Labs most successful customers.
Coming European GDPR directive affects data science practice mainly in 3 areas: limits on data processing and consumer profiling, a “right to an explanation” for automated decision-making, and accountability for bias and discrimination in automated decisions.
A lot is changing in the world of marketing analytics. Marketing scientist Kevin Gray asks Professor Michel Wedel, a leading authority on this topic from the Robert H. Smith School of Business at the University of Maryland, what marketing researchers and data scientists most need to know about it.
As emerging technologies like AI/machine learning are adopted across different parts of the business, enterprises require a “digital brain” to coordinate those efforts and generate systemic intelligence.
This ‘strange loop’ is in fact is the fundamental reason for what Yann LeCun describes as “the coolest idea in machine learning in the last twenty years.”
Will Apache Flink displace Apache Spark as the new champion of Big Data Processing? We compare Spark and Apache Flink performance for batch processing and stream processing.
I was asked this question recently via LinkedIn message: "What advice would you give your younger data scientist self?" The best piece of advice I honestly think I can give is this: Forget about "data science."
We will build this in a modular way and also focus on exposing the functionalities as an API so that it can serve as a plug and play model without any disruptions to the existing systems.
6 Reasons Why Python Is Suddenly Super Popular
AI and Deep Learning, Explained Simply
The 4 Types of Data Analytics
What Advice Would You Give Your Younger Data Scientist Self?
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