The big cloud players — Amazon, Google, IBM, and Microsoft — have all put their machine learning tools at your disposal. But figuring out how much they cost, and how those costs compare, can be a great mystery. In this article we’ll decipher the cost structures of the four biggest cloud players across a variety of scenarios.Read More
If you run marketing campaigns on Facebook, you might be frustrated by their limited built in reporting — or reliant on massive Excel spreadsheets to do your campaign analysis.
We decided to build a package for our favorite data science tool — R Studio — that pulls Facebook campaign insights directly from their marketing API.Read More
Google Affiliate Location Extensions have huge potential to drive offline sales. They make it possible for a manufacturer to advertise directly to active shoppers near a retail location that carries their product.
But if your product isn’t available at a national chain like Target or Costco, you are out of luck. We have a solution.Read More
We came to learn from a master. Instead we learned that Hadley is just like us. He makes mistakes. A lot of them. And it made all of us laugh. Not because Hadley was the fool, but rather because we all saw him fight through the same typos, errors, conflicts, and sometimes inexplicable bugs that we all face, every day.Read More
Machine Learning. Neural Networks. Hierarchical Clustering.
Does anyone know what these terms really mean? Sure, a handful of nerds know. Like the roughly 50,000 ranked Kaggle members.
But these esoteric terms only scratch the surface of what the world’s data scientists have come up with to describe, promote, and ultimately obfuscate their day to day jobs.Read More
Sherlock Holmes, Donald Trump, and the Data Science Paradox
Yes, “deducive” is a real word. And it’s the name we chose for our company. We chose it for its rational correlation to logic and the scientific method, as well as its emotional connection to a great fictional sleuth. But, in data science terminology, it may have been a bad choice.Read More
Successful SME marketers thoroughly understand what it takes to compete: a strong website, content strategy, CRM, and multichannel engagement plan. But as all the tactics stack up, so does the data. It’s harder and harder to know what works and which steps in the customer journey are most important.
This is not a new problem, but it is one that large enterprises have largely solved by investing in big data strategy and analytics platforms.