Data Science 101 would like to welcome Community and Sponsored Posts

After receiving some interest, I have decided to open up the posting more to the data science community. There are more details on the Contribute Page.
If there is enough interest, I will be posting community contributed posts on Wednesdays and sponsored posts on Thursdays.
What is the difference?
Community contributed posts are free and are intended for individuals.

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The Ultimate Guide to Content Distribution

Year after year, hundreds of marketers report increased efforts and spending on their content marketing — or the intention to do so.
But great content is a waste if your audience doesn’t know it exists.
Content distribution is an integral part — if not the most important part — of your content strategy.
This guide will equip you with the tools you need to distribute the content you create.

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Benefit People: Thinking Through Culture and Values

After watching Michael Skok’s excellent talk on Culture, Vision and
Mission, I was tempted
to have a bash at defining the values that I care about. Here’s my
attempt.
Benefit People
Benefit as many people as much as possible. The best kind of benefit
is that which helps people benefit people.
Make it Easy
Make everything easy. Everything is a conversation. Make conversations
easy.

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11 Tips for Improving Customer Experience and Driving Conversions

Struggling to drive conversions?  The issue might be with customer experience. After having worked with several brands, big and small, I can assure you that you don’t have to make sweeping changes to drive better results. Many times even small changes and little bit process can lead to happy customers and big impacts. In this post I have complied 11 tips that you can use today.

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Double blind review at theory conferences: More thoughts.

I’ve had a number of discussions with people both before and after the report that Rasmus and I wrote on the double-blind experiment at ALENEX. And I think it’s helpful to lay out some of my thoughts on both the purpose of double blind review as I understand it, and the logistical challenges of implementing it.

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What’s new in PyMC3 3.1

We recently released PyMC3 3.1 after the first stable 3.0 release in January 2017. You can update either via pip install pymc3 or via conda install -c conda-forge pymc3.
A lot is happening in PyMC3-land. One thing I am particularily proud of is the developer community we have built. We now have around 10 active core contributors from the US, Germany, Russia, Japan and Switzerland.

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Key learnings from Strata Barcelona 2014

Immediately after PAPIs.io ’14 — write-up coming soon! — I spent a couple of days at Strata in Barcelona.
Strata has several tracks and I ended up going mostly to “business” sessions, but this synthesis of things I heard at the conference will be of interest to technical people as well.

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Upcoming Speaking Engagements

After taking a break from speaking at conferences for a while, I will be speaking at two in the next month. Both events are here in Boston.This Friday (9/14)  I will be at Big Data Innovation talking about how Tripadvisor for Business models subscriber happiness and what we can do to improve a subscriber’s probability of renewal.

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Uncovering a model’s secrets (Model Inversion Part I)

Photo by Jaroslav Devia on UnsplashHave you ever woken up in the middle of the night after having a terrifying dream that your boss found that embarrassing Facebook picture of you in high school with a blue mohawk? You immediately go to facebook and delete the photo and think all is well. Well I’m here to tell you that it may not be the case.

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Calculating new stats in Major League Baseball with Amazon SageMaker

The 2019 Major League Baseball (MLB) postseason is here after an exhilarating regular season in which fans saw many exciting new developments. MLB and Amazon Web Services (AWS) teamed up to develop and deliver three new, real-time machine learning (ML) stats to MLB games: Stolen Base Success Probability, Shift Impact, and Pitcher Similarity Match-up Analysis.

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Calculating new stats in Major League Baseball with Amazon SageMaker | Amazon Web Services

The 2019 Major League Baseball (MLB) postseason is here after an exhilarating regular season in which fans saw many exciting new developments. MLB and Amazon Web Services (AWS) teamed up to develop and deliver three new, real-time machine learning (ML) stats to MLB games: Stolen Base Success Probability, Shift Impact, and Pitcher Similarity Match-up Analysis.

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Making AI Interpretable with Generative Adversarial Networks

It has been quite awhile since I have posted, largely because soon after I started my job at Square I had a child! I hope to have some newer blog post soon. But along those lines I want to share a blog post I did with a coworker (Juan Hernandez) for Square that gives a taste of some of the cool data science work we have been up to.

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