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We live in an era of unprecedented automation and rapidly expanding data. In 2019 alone, the internet generated 2.5 billion bytes of data every day, but only about .5% of that data was meaningfully analyzed! This data deluge, combined with the want to derive more value ever faster, has
As practitioners and consumers of data science, how do we make ethical decisions when confronted with thorny dilemmas in artificial intelligence (AI)? How do we even recognize when those dilemmas emerge before they become public relations catastrophes? Humanities and social
AI is one of the most widely talked about yet poorly understood terms in organizations. While over 50% of organizations considered AI a priority in 2019, 77% of organizations are failing to successfully adopt AI-enabled solutions. Having a poor definition
Whether you are a business owner wading through the sea of marketing tools or the lone marketer in an organization trying to keep up, there are some tried and true basics to set the foundation for marketing success. “Success” isn’t
During our internship at Pandata, we have grown in our knowledge of data science, and gratefully so. Being able to use artificial intelligence and data science as a way of simplifying a problem you are trying to solve is the
You’ve started collecting some simple data in your plant and the missed opportunities are both frustrating and exciting. Your mind is racing with improvement opportunities here, optimizations there, and a huge profit at the end of the rainbow (followed by
PyCon is the largest annual gathering of the Python user community, a group to which Pandata enthusiastically belongs. Several members of Team Panda were able to participate in this year’s PyCon, conveniently held in Cleveland. While there were too many
The Cleveland Museum of Art is a world-renowned art museum with a substantial collection of over 61,000 artworks. In January of 2019, they launched their Open Access Initiative in which they made over 30,000 public domain works and metadata for
Call it Machine Learning or Artificial Intelligence, the goal is to solve problems using data. Solving problems using data is the driving force behind data science. Everything else is just a weapon in our arsenal. Yet, we see the buzzwords
By Hannah Arnson & Joseph Homrocky Data science is often thought of as a black box – in goes data, outcomes actionable information. Inside that black box lives a complex mix of programming, mathematics, statistics, hardware, and software, each
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