  We live in a digital world that, in 2010, crossed the mark of one zettabyte
data. This huge amount of data processed on computers extremely fast with
optimized techniques allows one to find insights in new and emerging types of
data and content and to answer questions that were previously considered beyond
reach. This is the idea of Big Data. Google now offers the Google Correlate
analysis public tool that, from a search term or a series of temporal or
regional data, provides a list of queries on Google whose frequencies follow
patterns that best correlate with the data, according to the Pearson
determination coefficient R2. Of course, correlation does not imply causation.
We believe, however, that there is potential for these big data tools to find
unexpected correlations that may serve as clues to interesting phenomena, from
the pedagogical and even scientific point of view. As far as we know, this is
the first proposal for the use of Big Data in Science Teaching, of
constructionist character, taking as mediators the computer and the public and
free tools such as Google Correlate. It also has an epistemological bias, not
being merely a training in computational infrastructure or predictive
analytics, but aiming at providing students a better understanding of physical
concepts, such as phenomena, observation, measurement, physical laws, theory,
and causality. With it, they would be able to become good Big Data specialists,
the so needed 'data scientists' to solve the challenges of Big Data.
