AI News, Off The Charts: An Interview with John Foreman, Chief Data Scientist at MailChimp

Off The Charts: An Interview with John Foreman, Chief Data Scientist at MailChimp

Specifically, Excel, SAS, Teradata, and Oracle seem to rule the enterprise where terms like 'business intelligence' are bandied about, whereas R, Python, and Hadoop have a better hold in companies talking data science.

So I asked myself, how would I teach the important techniques of the data science world to folks in the enterprise world?

It's hard to teach machine learning and data mining in R where most tutorials seem to start with 'now load the relevant library that's done all the work for you.'

So I asked myself, how would I teach the important techniques of the data science world to folks in the enterprise world?

We think data literacy is the new computer literacy, meaning we think businesses will eventually come to expect all employees to understand how to represent data and do some sort of analysis.

But I decided to try something a little more applied, so I worked for a professor studying the mathematics of knot-tyingwhich has applications in protein folding and physical cosmology.

Awesome math, not so awesome clients, so I left the government to consult for large enterprises like Coca-Cola, building supply and revenue optimization models.

I believe that the revenue optimization models that companies like Intercontinental Hotels and Royal Caribbean have been building in the enterprise world are great examples of data science before there was data science.

MailChimp sends four hundred million emails a day for customers all across the globe, and we track engagement (opens, clicks, unsubscribes, abuse reports, Google analytics data, e-commerce data, custom triggered events, etc.) on those newsletters to the tune of another hundred million events a day.

Or maybe they'll release an infographic of their aggregate data to prove to their investors that their big data investment was worth it.

That should always be the goal of a good data science team -- leading from the back, giving people the data-driven insight they need to do their work better.

That should always be the goal of a good data science team -- leading from the back, giving people the data-driven insight they need to do their work better.

But at a high level, we try to avoid tracking a lot of ugly metrics like ARPUWe've seen from our competitors that when you track these company-level revenue metrics, you start trying to do good by the metric rather than by your customers.

Rather than assume that all senders and subscribers are the same, the model uses data (what a novel idea!) to figure out when your readership will most likely engage with your content.

recently discussed in a post on the MailChimp blogthe age and browser preference of email addresses from the big free email providers.

For both email providers though, the number one way the readers view email is on the iPhone (for AOL, the AOL Explorer browser still plays a role which is frightening).

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