Answer to Question 1
Answer: Data analytics means using statistical and mathematical analysis and algorithms to find relationships and make predictions. For example, when online bookstores use algorithms to predict which books you're most likely to buy based on things like what books you've already bought and similarities between you and other groups, they are using data analytics. Data mining is crucial for data analytics and is the set of activities used to find new, hidden, or unexpected patterns in data. Data mining sifts through huge amounts of employee data to identify correlations that employers then use to improve their employee-selection and other practices. Thanks to data mining, the manager can discover patterns that he or she can then use to make predictions.
Big data is basically data analytics on steroids, using very large data sets. The basic idea (of scientifically analyzing data to find relationships and make predictions) is the same. However, with big data the volume, velocity, and variety of data that are analyzed are much greater. In terms of volume, for example, Walmart now collects about 2.5 petabytes of data2.5 million gigabytesevery hour from its customer transactions. Similarly, in terms of velocity, all these data are being created more or less instantaneously (as at Walmart); that means companies can use them to more quickly to adapt in real time (for instance, to who's buying what products, and therefore how to adjust online promotions). Finally, big data capitalizes on the huge variety of data now available. For instance, data come not just from Walmart's transactions but from customers' mobile phones, GPS, and social networks too.
Answer to Question 2
Answer: D
The cladistic approach from molecular genetic data
The carbon cycle. Numbers are storage as 1015 g or fluxes as 1015 g per year (data from Schlesinger
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The data link connector (DLC) can be located in various locations.
Demographic Data for Nigeria