
The main objective of this chapter is to provide an overview of the modern field of data science and some of the current progress in this field. The overview focuses on two important paradigms: (1) big data paradigm, which describes a problem space for the big data analytics, and (2) machine learning paradigm, which describes a solution space for the big data analytics. It also includes a preliminary description of the important elements of data science. These important elements are the data, the knowledge (also called responses), and the operations. The terms knowledge and responses will be used interchangeably in the rest of the book. A preliminary information of the data format, the data types and the classification are also presented in this chapter. This chapter emphasizes the importance of collaboration between the experts from multiple disciplines and provides the information on some of the current institutions that show collaborative activities with useful resources.
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 42 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
