data

When people get sick, they often start looking online for information about their symptoms and illness. Sara Del Valle, a scientist and applied mathematician at Los Alamos National Laboratory, says that by training a computer model to analyze the specific health-related pages that users access on Wikipedia, public health experts may be able to identify—and even forecast—outbreaks of diseases such as influenza and Dengue fever. An article about the approach was published this week in the journal PLOS Computational Biology.

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Mining Wikipedia Data to Track Disease

Ryan Rykaczewski describes one of the most massive migrations on the planet, driven by the sun. Image by Tintinnidguy (Own work) [CC BY-SA 4.0 (http://creativecommons.org/licenses/by-sa/4.0)], via Wikimedia Commons 

#ExplainTheSun  - The Largest Migration on The Planet

Once, there was the ordinary incandescent light bulb. It shone brightly, but wasted much of the energy put into it as heat. Then came the compact fluorescent. And as costs have dropped, consumers have increasingly been switching to light-emitting diode (LED) technology for many lighting applications, in an ongoing search for a more efficient bulb. Now, in work published in the journal Nature Nanotechnology, researchers report that nanophotonic devices may be able to breathe new life into old incandescent technology. Marin Soljačić and Ognjen Ilic, two authors of the study, explain how they’ve been able to “recycle” some of the incandescent bulb’s wasted heat and re-emit it as visible light, boosting efficiency.

For the Lowly Incandescent Bulb, a Ray of Hope

As data scientists add deep learning to their arsenals, they need tools that integrate with existing platforms and frameworks. This is particularly important for those who work in large enterprises. In this episode of the Data Show, I spoke with Adam Gibson, co-founder and CTO of Skymind, and co-creator of Deeplearning4J (DL4J). Gibson has spent the last few years developing the DL4J library and community, while simultaneously building deep learning solutions and products for large enterprises.

The Key to Building Deep Learning Solutions for Large Enterprises

In this episode of the Data Show, I spoke with Jason Dai, CTO of big data technologies at Intel, and co-chair of Strata + Hadoop World Beijing. Dai and his team are prolific and longstanding contributors to the Apache Spark project. Their early contributions to Spark tended to be on the systems side and included Netty-based shuffle, a fair-scheduler, and the “yarn-client” mode. Recently, they have been contributing tools for advanced analytics. In partnership with major cloud providers in China, they’ve written implementations of algorithmic building blocks and machine learning models that let Apache Spark users scale to extremely high-dimensional models and large data sets. They achieve scalability by taking advantage of things like data sparsity and Intel’s MKL software. Along the way, they’ve gained valuable experience and insight into how companies deploy machine learning models in real-world applications.

Deep Learning for Apache Spark

In this episode of the SuperDataScience Podcast, I chat with Senior Insights Manager at LinkedIn Josh Coulson. You will talk about the implications of moving into the Gig Economy, learn about how to build a personal brand in data science, discuss about data democratization across organizations and get insights on how to enhance your career and build better opportunities in the space of data science.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/33

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 033: Building a Personal Brand in Data Science with Senior Insights Manager Josh Coulson

In this episode of the SuperDataScience Podcast, I chat with Data Science Course Critic David Venturi. You will discuss about a data-driven approach to building a masters degree in data science, learn about the alternative strategies necessary to secure employment and get some essential study hacks you would love to know. 

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/35

SDS 035: Build Your Own Data Science Masters Degree with David Venturi

Science, technology, and other cool stuff from public radio's Science Friday. It's brain fun, for curious people. From WNYC Studios.

Mining Wikipedia Data to Track Disease

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