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5 Tricks to Secure Insight Engagement and Memorability

Editor’s Note: This post is part of our Big Ideas series, a column highlighting the innovative thinking and thought leadership at IIeX events around the world. Mayra Munguia will be speaking at IIeX Europe 2019 in Amsterdam, Feb. 25-26. If you liked this article, you’ll LOVE IIeX Europe. Click here to learn more.

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Researchers Should Do More Market Research

Editor’s Note: This post is part of our Big Ideas series, a column highlighting the innovative thinking and thought leadership at IIeX events around the world. Jan Zwang will be speaking at IIeX Europe 2019 in Amsterdam, Feb. 25-26. If you liked this article, you’ll LOVE IIeX Europe. Click here to learn more. 
We’ve all been there, the sales pitch starts with full enthusiasm and drive.

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The Last Frontier for Lean Methodology: Iterative Questions

Editor’s Note: This post is part of our Big Ideas series, a column highlighting the innovative thinking and thought leadership at IIeX events around the world. Athena Lam will be speaking at IIeX Europe 2019 in Amsterdam, Feb. 25-26. If you liked this article, you’ll LOVE IIeX Europe. Click here to learn more. 
Lean has been one of the guiding principles in the last decade.

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Real-time music recommendations for new users with Amazon SageMaker

This is a guest post from Matt Fielder and Jordan Rosenblum at iHeartRadio. In their own words, “iHeartRadio is a streaming audio service that reaches tens of millions of users every month and registers many tens of thousands more every day.”
Personalization is an important part of the user experience, and we aspire to give useful recommendations as early in the user lifecycle as possible.

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Recommender Systems: We’re doing it (all) wrong

A few days back, there was an interesting post by Judy Robertson in the Communications of the ACM blog. The post, entitled “Stats: We’re doing it wrong”, builds upon a paper from last year’s CHI conference in which they report that more than 90% of the HCI researchers used the wrong statistical tools when analyzing and reporting on likert scale type of data.

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Scraping Responsibly with R

I recently wrote a blog post here
comparing the number of CRAN downloads an R package gets relative to its number of
stars on GitHub. What I didn’t really think about during my analysis was whether or
not scraping CRAN was a violation of its Terms and Conditions. I simply copy and
pasted some code from R-bloggers
that seemed to work and went on my merry way.

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Is Matthew Walker’s “Why We Sleep” Riddled with Scientific and Factual Errors?

Asher Meir points to this hilarious post by Alexey Guzey entitled, Matthew Walker’s “Why We Sleep” Is Riddled with Scientific and Factual Errors.
Just to start with, the post has a wonderful descriptive title. And the laffs start right away:
Positively Nabokovian, I’d say. I mean it. The above table of contents makes me want to read more.

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Re-exporting the magrittr pipe operator

… or how I stoped worrying and wrote a blog post to remember it ad infinitum.
Magrittr’s pipe operator is one of those newish R-universe features that I
really want to have around whenever I put some lines into an R-console.
This is even TRUE when writing a package.
So the first thing I do is put magrittr into the DESCRIPTION file and add
an __imports.

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Geocoding Paradise Papers Addresses In Neo4j To Build Interactive Geographical Data Visualizations

This post explores how to build spatial data visualizations using address data from the Paradise Papers leak of offshore corporations and the people connected to them. First, we geocode all addresses in the leaked data, then build a heatmap and interactive map for exploring the data of offshore legal entities.

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covrpage, more information on unit testing

In this post, we shall explore the first R package that received Locke Data’s new support, covrpage by Jonathan Sidi! With this nifty package you can better communicate the unit testing completeness and goodness of your package!
What’s covrpage?
Trust is earned not inherited importedFrom. Now that you’ve built a cool package, you want potential users to trust it so that they might adopt it.

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AI adoption is being fueled by an improved tool ecosystem

[A version of this post appears on the O’Reilly Radar.]We now are in the implementation phase for AI technologies.In this post, I share slides and notes from a keynote that Roger Chen and I gave at the 2019 Artificial Intelligence conference in New York City. In this short summary, I highlight results from a — survey (AI Adoption in the Enterprise) and describe recent trends in AI.

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Keras for Beginners: Building Your First Neural Network – victorzhou.com

Keras is a simple-to-use but powerful deep learning library for Python. In this post, we’ll see how easy it is to build a feedforward neural network and train it to solve a real problem with Keras.
This post is intended for complete beginners to Keras but does assume a basic background knowledge of neural networks.

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will wolf

This post is meant to serve as an introduction to what Docker is and why and how to use it for Kaggle. For simplicity, we will primarily speak about Sesame Street and cupcakes in lieu of computers and data.
One Monday morning, Ernie from the ‘Street climbs out from under his red-and-blue pinstriped covers, puts both feet on the ground and opens his bedroom window.

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will wolf

I address this post to my peers – to my liberal, driven, University-educated and multi-cultural counterparts.
Like most of you, I spent the day of Donald Trump’s election in a state of disbelief, paralysis and exasperation. Like many more, I had several long, critical conversations about what had just happened and where we go from here.

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How to Improve Your Subscription-Based Business by Predicting Churn

A couple of weeks ago I wrote a guest post on churn prediction for Kissmetrics, and they just published it.
Churn prediction is one of the most popular Big Data use cases in business. It consists in detecting which customers are likely to cancel a subscription to a service based on how they use the service.

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Juiced baseballs, part II

Last post, I showed how MGL found the variation (SD) of MLB baseballs to be in the range of about 7 feet difference for a typical fly ball. I wondered if that were truly the case, or if some of it wasn’t real, just imprecision due to measurement error.After some Twitter conversations that led me to other sources, I’m leaning to the conclusion that the variance is real.

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