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Ethical Data Standards to Drive Society Forward

I am constantly amazed by the energy and momentum around data science. Only a few years ago, I would be met with a blank stare when I told someone I planned on going to grad school for machine learning. Today, there is no need for my “it's like computer science, linear algebra, and statistics had a combined love child” analogy as most people instantly respond with “Oh, like AI!”

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Sparking Ideas for Visualizing Innovative Research Teams

A collaborative blog series about collaborative research: a data scientist and a cognitive psychologist combine perspectives.

Dr. Alicia Knoedler: For the past 18 years, I have sought opportunities and means to advocate for researchers working to develop and accelerate their research programs. I had the very fortunate opportunity to meet Dave King in 2014 when he relocated his company to Oklahoma City.

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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.

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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.

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Exploring Tech Stocks: A Data Application Versus Data Visualization

A crucial aspect that sets a data application apart from an ordinary visualization is interactivity. In an application, visualizations can interact with each other. For example, clicking on a point in a scatterplot may send corresponding data to a table. In an application, visualizations are also enhanced with simple filtering tools, e.g. selections in a list can update results shown a heat map. 

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A Graph-Analytics Data Application Using a Supercomputer!

We recently had to prototype a data application over a supercomputer tuned for graph analysis. We built a proof-of-concept leveraging multiple APIs, Cray’s Urika-GX and Graph Engine (CGE), and a handful of programming languages in less than a week.

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A Data Application for the _______ Genome Project

The ability to reuse and repurpose - exaptation - is often a catalyst for exciting breakthroughs. The Astronomical Medicine Project (yes, astronomical medicine) was founded on the realization that space phenomena could be visualized using MRI software, like highly irregular brains. The first private space plane, designed by Burt Ratan, reenters the atmosphere using wings inspired by a badminton birdie. Anecdotes like this abound in many fields, and the principle applies to working with data and creating data applications, as much as it does any innovation. 

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A Data Exploration Journey with Cars and Parallel Coordinates

Parallel coordinates is one way to visually compare many variables at once and to see the correlations between them. Each variable is given a vertical axis, and the axes are placed parallel to each other. A line representing a particular sample is drawn between the axes, indicating how the sample compares across the variables.

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Use a Network Diagram to Uncover Relationships in Your Data

Often times, when we're looking at a mass of data, we're trying to get a sense for relationships within that data. Who is the leader in this social group? What is a common thread between different groups of people? Such relationships can be represented hundreds of ways graphically, but few are as powerful as the classic network diagram.

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Finding Netflix's Hidden Trove of Original Content with a Basic Network Diagram

Nexflix has collected an impressive amount of data on Hollywood entertainment, made possible by tracking the viewing habits of its more-than 90 million members. In 2013, Netflix took an educated guess based on that data to stream its own original series, risking its reputation and finances in the process. When people were subscribing to Netflix to watch a trove of television series and movies created by well-established networks and studios, why create original content? Now, few would question the move.

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What is a Data Application?

There are data visualizations. There are web applications. If they had a baby, you'd get a data application.

Data applications are a big part of where our data-driven world is headed. They're how data science gets operationalized. They are how end-users - whether they're subject matter experts, business decision makers, or consumers - interact with data, big and small. We all use a data application when we book a flight, for instance. 

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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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Finding Abstractions that Give Data Applications 'Flight'

Continuing with our recent theme of abstraction in data applications, Dave King gave a talk last month explaining his design principles for "Making Code Sing: Finding the Right Abstractions." Nailing the best abstractions is a quintessential software challenge. We strive for generality, flexibility, and reuse, but we are often forced to compromise in order to get the details right for one particular use case. We end up with projects that we know have amazing potential for use in other applications but are too hardcoded to make repurposing easy. It’s frustrating to see the possibilities locked away, just out of reach.

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Epiphanies on Abstraction, Modularity, and Being Combinatorial

Six months ago I didn't understand the concept of abstraction. Now it comes up almost daily. It’s foundational to my thinking on everything from software to entrepreneurship. I can’t believe how simple it seems. When I finally grokked abstraction, it felt like my first taste of basic economics. Given a new framework, something that had always been there, intuited but blurry, came into focus.

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A Novice Coder, a Finance Data Application, and the Value of Rapid Prototyping

I like to build things. I like analysis. I like programming. Interestingly, you often need to reverse that order before you’re in a position to build an application for analyzing something. You need programming knowledge to turn the analysis into a “thing.” The problem is, while I like programming, I’m still new to it. I mean, I’m Codecademy good, but that doesn’t translate into a user facing application leveraging Python, Javascript, and D3. So, when I recently sat down to build a minimally viable data application for looking at airline stocks, I wondered how long it might take to get to viable and, frankly, feared how minimal it might be.

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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.

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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. 

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