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How Data Visualization Supports the Formation of Better Hypotheses

Since Exaptive launched in 2011, we’ve worked with many researchers, particularly in medicine and the natural sciences. PubMed®, a medical journal database, pops up repeatedly as a key tool for these researchers to develop hypotheses. It’s a tool built in a search-and-find paradigm with which we’re all familiar. Execute a keyword search. Get a list of results. Visualization can make search - and, therefore, research - much more meaningful.

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Modern Research: Faster Is Different

Faster is different. It sounds strange at first because we expect faster to be better. We expect faster to be more. If we can analyze data faster, we can analyze more data. If we can network faster, we can network with more people. Faster is more, which is better, but more is different.

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Machine Learning Helps Humans Perform Text Analysis

The rise of Big Data created the need for data applications to be able to consume data residing in disparate databases, of wildly differing schema. The traditional approach to performing analytics on this sort of data has been to warehouse it; to move all the data into one place under a common schema so it can be analyzed.

This approach is no longer feasible with the volume of data being produced, the variety of data requiring specific optimized schemas, and the velocity of the creation of new data. A much more promising approach has been based on semantic link data, which models data as a graph (a network of nodes and edges) instead of as a series of relational tables.

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Moving Beyond Data Visualization to Data Applications

One thing we love doing at Exaptive – aside from creating tools that facilitate innovation – is hiring intelligent, creative, and compassionate people to fill our ranks. Frank Evans is one of our data scientists. He was invited to present at the TEDxOU event on January 26, 2018.

Frank gave a great talk about how to go beyond data visualization to data applications. The verbatim of his script is below the video. Learn more about how to build data applications here

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Alleviating Uncertainty in Bayesian Inference with MCMC sampling and Metropolis-Hastings

Bayesian inference is a statistical method used to update a prior belief based on new evidence, an extremely useful technique with innumerable applications. Uncertainty about probabilities that are hard to quantify is one of the challenges of Bayesian inference, but there is a solution that is exciting for its cross-disciplinary origins and the elegant chain of ideas of which it is composed.

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A Data Application to Foretell the Next Silicon Valley?

Can we predict what the next hub of tech entrepreneurship will be? Could we pinpoint where the next real estate boom will be and invest there? Thanks to advances in machine learning and easier access to public data through Open Data initiatives, we can now explore these types of questions.

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How a Data Scientist Built a Web-Based Data Application

I’m an algorithms guy. I love exploring data sets, building cool models, and finding interesting patterns that are hidden in that data. Once I have a model, then of course I want a great interactive, visual way to communicate it to anyone that will listen. When it comes to interactive visuals there is nothing better than JavaScript’s D3. It’s smooth and beautiful.

But like I said, I’m an algorithms guy. Those machine learning models I’ve tuned are in Python and R. And I don’t want to spend all my time trying to glue them together with web code that I don't understand very well and I’m not terribly interested in.

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Topic Modeling the State of the Union: TV and Partisanship

Do you feel like partisanship is running amok? It’s not your imagination. As an example, the modern State of the Union has become hyperpartisan, and topic modeling quantifies that effect. 

Topic modeling finds broad topics that occur in a body of text. Those topics are characterized by key terms that have some relationship to each other.  Here are the four dominant topic groups found in State of the Union addresses since 1945.

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Affecting Change Using Social Influence Mapping

If you've ever tried to get a company to adopt new software you know how challenging it can be. Despite what seem to you like obvious benefits and your relentless communication, people selectively ignore or, worse, revolt against the change. Change efforts will even stumble in the face of this wisdom of the ages:

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How I Made a Neural Network Web Application in an Hour

Computer vision is an exciting and quickly growing set of data science technologies. It has a broad range of applications from industrial quality control to disease diagnosis. I have dabbled with a few different technologies that fall under this umbrella before, and I decided that it would be a worthwhile endeavor to rapid prototype an image recognition web application that used a neural network.

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Text Analysis with R: Does POTUS Write the State of the Union or Vice Versa?

In this post, I apply text clustering techniques – hierarchical clustering, K-Means, and Principal Components Analysis – to every presidential state of the union address from Truman to Obama. I used R for the setup, the clustering, and the data vis.

It turns out that the state of the union writes the State of the Union more than the president does. The words used in the addresses appear linked to the era more than to an individual president or his party affiliation. However, there is one major exception in President George W. Bush, whose style and content marks a sharp departure from both his predecessors and contemporaries. You can see the R scripts and more technical detail on the process here. The State of the Union addresses up to 2007 are available here and the rest you can get here.

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Data Science Wanderlust: Analyzing Global Health with Protein Sequences

Fifteen years ago, I had the unique opportunity to go on Semester at Sea, an around-the-world trip on a converted cruise ship that combined college coursework stops at nine countries on four continents. This once in a lifetime trip instilled in me a strong sense of wanderlust and a deep desire to give back to the global community.

Every Journey Begins with a Single Step

Fast-forward to a few months ago, when I joined Exaptive on an exciting new project. A large NGO enlisted us to analyze a massive set of historical data for countries. The goal: to develop a better, more granular means of grouping countries than the outdated and crude approach of "developed" and "developing." This large, complex, messy dataset and thorny problem were a great fit for my background in artificial intelligence and data science.

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Cowboys and Inventors: The Myth of the Lone Genius

I recently moved from Boston to Oklahoma City. My wife got offered a tenure-track position at the University of Oklahoma, which was too good an opportunity for her career for us to pass up. Prior to the move, I had done a lot of traveling in the US, but almost exclusively on the coasts, so I didn't know what living in the southern Midwest would bring, and I was a bit trepidatious. It has turned out to be a fantastic move. There is a thriving high-tech startup culture here. I've been able to hire some great talent out of the University, and we're now planning to build up a big Exaptive home office here. Even more important, I was delighted to find a state that was extremely focused on fostering creativity and innovation. In fact, the World Creativity Forum is being hosted here this week, and I was asked to give a talk about innovation. As I thought about what I wanted to say, I found myself thinking about . . . cowboys.

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The Data Scientific Method

The Oxford English Dictionary defines the scientific method as "a method or procedure that has characterized natural science since the 17th century, consisting in systematic observation, measurement, and experiment, and the formulation, testing, and modification of hypotheses." With more scientists today than ever, the scientific method is alive and well, and generating more data than ever. This explosion of data has brought about the field of data science and an associated plethora of analytics tools. Controversially, some have claimed, such as in this Wired magazine article, that data science is so powerful that it has made the scientific method obsolete. Google's founding philosophy is that “we don't know why this page is better than that one. If the statistics of incoming links say it is, that's good enough.” The implication is that with enough data, people will no longer need to know why something happens, it just does, and that’s good enough. Is it, really?

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