Understanding the Policy Risks of Big Data Applications

Hamish Robertson and Joanne Travaglia discuss concerns about the rapid growth in big data methods being used to inform and shape social policy strategies and practices, and particularly the underlying assumptions such data are used to support and “verify”.

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Bev Skeggs on social media siloing

"Basically 90 percent of Facebook profit is made from advertising — selling your data to advertising companies so that they can place an advert on your browser..." says Bev Skeggs in a new interview with Social Science Bites. Bev Skeggs joins the podcast in order to reveal interesting new findings in her research that studies how social networks were structuring or restructuring friendships. 

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Skills Skills

What aspiring data scientists are looking for in hiring companies

"Positions in data science require a unique set of job skills that many professionals simply don’t possess.  The level of programming knowledge, understanding of statistics and business sense make for a difficult position to fill. Because of this, many businesses find it difficult to hire appropriately for the position of data scientist." Kayla Matthews gives pointers on ways that companies, looking for data scientists, could stand out in this demanding market for data engineers. 

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In Praise of the Digital in Social Research

"The collection of “big data” and the ability to do experiments using the internet, may be the start of a scientific revolution in social research. But there are important ethical considerations that also need to be factored in." says Roby Muhamad in this piece which both praises the innovations big data has been able to allow us to explore and puts caution to the wind for the future of what these advances may entail. 

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Statistical Decision Theory and Data Collection: More or Better?

When designing data collection, researchers must take important decisions on how much data to collect and what resources to devote to enhancing the quality of the collected data. But the threshold for choosing better over bigger data may be reached long before the sample numbers in the thousands, write Jeff Dominitz and Charles F. Manski.

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Big data rich and big data poor

Data is being created faster than ever before however without access to these data-sets or the expertise to analyse them, research is confronted with a replication crisis and is vulnerable to commercial motivations. The problem is growing as Katie Metzler points out, "Firstly, because replication is the engine of science, and irreproducible research slows progress... secondly the motivations of industry researchers and social scientists may differ in ways that may really matter." 

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