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Big Data adoption can enable the sort of innovation that fundamentally alters the structure of a business, either in its products, services or organization. However, innovation management requires care: too many controlling forces can stifle the initiative and dampen the results, and too little oversight can turn a best intentioned project into a science experiment that never delivers promised results. It is against this backdrop that this chapter from Big Data Fundamentals: Concepts, Drivers & Techniques addresses Big Data adoption and planning considerations.
This chapter from Getting Started with Data Science: Making Sense of Data with Analytics introduces the basic concepts of random numbers and probability distributions. It provides a formal introduction of Normal and t-distributions, which are commonly used for statistical models. Finally, it explores hypothesis testing for the comparison of means and correlations.
Aggregate functions can be useful and are quite simple to use. In this chapter from SQL in 24 Hours, Sams Teach Yourself, 6th Edition, you learn how to count values in columns, count rows of data in a table, get the maximum and minimum values for a column, figure the sum of the values in a column, and figure the average value for values in a column.
In this excerpt from Oracle SQL LiveLessons (Video Training), Downloadable Version, Dan Hotka discusses analytical functions.
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