Data Science newsletter – October 10, 2016

A team of researchers has traced a type of rice grown in the South American country of Suriname to a similar type found in the fields of Ivory Coast, revealing additional linkages between “New World” and “Old World” crops that likely resulted from the slave trade.

“This evidence is an incentive to re-examine historical sources to trace the way African rice ended up in the New World and why it was cultivated by enslaved Africans and their descendants,” write the authors of the study, which was led by researchers at Wageningen University in the Netherlands.

The work, which appears in the journal Nature Plants, also included NYU Biology Professor Michael Purugganan.

Since PipelineDB released the developer preview of its realtime analytics API, Stride, one is tempted to ask whether APIs in connection with machine learning may replace the growing role of human data scientists in the data analytics space. To further explore this concept, ProgrammableWeb caught up with PipelineDB’s President and Co-Founder, Jeff Ferguson. Ferguson and his team believe that machine learning can easily replace the role humans play in a number of data analytics tasks, and this shift has already begun to take place.

By providing easy access to curated federal and local datasets at opportunity.census.gov, and facilitating collaboration between technologists, issue experts, and community leaders, the Opportunity Project is transforming government data into digital tools that create more just and equitable communities and help people solve problems in their everyday lives.

To advance artificial intelligence research, Tambe and Professor Eric Rice of the USC Suzanne Dworak-Peck School of Social Work have joined forces to co-direct the Center on Artificial Intelligence for Society, or CAIS. At the center, one of the first such university-based institutes dedicated to studying AI as a force for good, researchers will leverage artificial intelligence to address myriad problems ranging from climate change to security to health to homelessness.

Our ability to collect, manipulate, analyze, and act on vast amounts of data is having a profound impact on all aspects of society. This transformation has led to the emergence of data science as a new discipline[1]. The explosive growth of interest in this area has been driven by research in social, natural, and physical sciences with access to data at an unprecedented scale and variety, by industry assembling huge amounts of operational and behavioral information to create new services and sources of revenue, and by government, social services and non-profits leveraging data for social good. This emerging discipline relies on a novel mix of mathematical and statistical modeling, computational thinking and methods, data representation and management, and domain expertise. While computing fields already provide many principles, tools and techniques to support data science applications and use cases, the computer science community also has the opportunity to contribute to the new research needed to further drive the development of the field. In addition, the community has the obligation to engage in developing guidelines for the responsible use of data science.

Ecosystem services (ES) are an increasingly popular policy framework for connecting biodiversity with human well-being. These efforts typically assume that biodiversity and ES covary, but the relationship between them remains remarkably unclear. Here we analyse >500 recent papers and show that reported relationships differ among ES, methods of measuring biodiversity and ES, and three different approaches to linking them (spatial correlations, management comparisons and functional experiments). For spatial correlations, biodiversity relates more strongly to measures of ES supply than to resulting human benefits. For management comparisons, biodiversity of ‘service providers’ predicts ES more often than biodiversity of functionally unrelated taxa, but the opposite is true for spatial correlations. Functional experiments occur at smaller spatial scales than management and spatial studies, which show contrasting responses to scale. Our results illuminate the varying dynamics relating biodiversity to ES, and show the importance of matching management efforts to the most relevant scientific evidence. [full text]

Ron Wyden, a Democratic senator from Oregon, has been a leading voice on the side of encryption and against giving the Justice Department more power to get consumer data from tech companies. Mr. Wyden, a member of the Senate Select Committee on Intelligence, recently talked to The New York Times about the privacy-versus-security debate.

Something remarkable is happening in the world of artificial intelligence. At the O’Reilly AI Conference in New York, people weren’t just talking about AI as a far-off dream; they were talking about AI as something that exists in real products today.

In this episode of the O’Reilly Bots podcast, I talk with three artificial-intelligence practitioners about the real practice of AI: Hilary Mason, Jimi Smoot, and Roger Chen. [audio, 32:26]

Applications for inaugural class of PhD students accepted now for fall 2017 start date.

Admitted students are guaranteed financial support in the form of tuition and a stipend in the fall and spring semesters for 5 years. [video, 4:28]

A Python library for unevenly-spaced time series analysis.

I’m using my discovery of Altair as an opportunity to step back — to investigate how Python’s statistical visualization options hang together. I hope this investigation proves helpful for you as well.

If you want to be a great software developer, without doubt you should constantly master trending technologies (like React, Redux, Angular). And in the meantime put the same amount of effort to learn regularly fundamentals like algorithms, data structures, the principles of writing scaling and clean code.

The article is my own thoughts on how to deal with the development complexity. Also you’ll find an interesting division of developer’s mastery progress. I name these phases upside-down triangle, rectangle and triangle.