Data science is not a single science as much as it is a collection of various scientific disciplines integrated for the purpose of analyzing data. These disciplines include various statistical and mathematical techniques, including:
Domain-specific knowledge and approaches
With the advent of cheaper storage technology, more and more data has been collected and stored permitting previously unfeasible processing and analysis of data. With this analysis came the need for various techniques to make sense of the data. These large sets of data, when used to analyze data and identify trends and patterns, become known as big data.
This in turn gave rise to cloud computing and concurrent techniques such as map-reduce, which distributed the analysis process across a large number of processors, taking advantage of the power of parallel processing.
The process of analyzing big data is not simple and evolves to the specialization of developers who were known as data scientists. Drawing upon a myriad of technologies and expertise, they are able to analyze data to solve problems that previously were either not envisioned or were too difficult to solve.
Early big data applications were typified by the emergence of search engines capable of more powerful and accurate searches than their predecessors. For example, AltaVista was an early popular search engine that was eventually superseded by Google. While big data applications were not limited to these search engine functionalities, these applications laid the groundwork for future work in big data.
The term, data science, has been used since 1974 and evolved over time to include statistical analysis of data. The concepts of data mining and data analytics have been associated with data science. Around 2008, the term data scientist appeared and was used to describe a person who performs data analysis. A more in-depth discussion of the history of data science can be found at http://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/#3d9ea08369fd.
This book aims to take a broad look at data science using Java and will briefly touch on many topics. It is likely that the reader may find topics of interest and pursue these at greater depth independently. The purpose of this book, however, is simply to introduce the reader to the significant data science topics and to illustrate how they can be addressed using Java.
There are many algorithms used in data science. In this book, we do not attempt to explain how they work except at an introductory level. Rather, we are more interested in explaining how they can be used to solve problems. Specifically, we are interested in knowing how they can be used with Java.