Posted on June 29, 2017 by kyadav .
Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.
Data science is a “concept to unify statistics, data analysis and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the sub domains of machine learning, classification, cluster analysis, data mining, databases, and visualization.
An Explosion of Data
Data is increasingly cheap and ubiquitous. We are now digitizing analog content that was created over centuries and collecting myriad new types of data from web logs, mobile devices, sensors, instruments, and transactions. IBM estimates that 90 percent of the data in the world today has been created in the past two years.
At the same time, new technologies are emerging to organize and make sense of this avalanche of data. We can now identify patterns and regularities in data of all sorts that allow us to advance scholarship, improve the human condition, and create commercial and social value. The rise of “big data” has the potential to deepen our understanding of phenomena ranging from physical and biological systems to human social and economic behavior
A Challenge Identified
Virtually every sector of the economy now has access to more data than would have been imaginable even a decade ago. Businesses today are accumulating new data at a rate that exceeds their capacity to extract value from it. The question facing every organization that wants to attract a community is how to use data effectively — not just their own data, but all of the data that’s available and relevant.
Our ability to derive social and economic value from the newly available data is limited by the lack of expertise. Working with this data requires distinctive new skills and tools. The corpuses are often too voluminous to fit on a single computer, to manipulate with traditional databases or statistical tools, or to represent using standard graphics software. The data is also more heterogeneous than the highly curated data of the past. Digitized text, audio, and visual content, like sensor and blog data, is typically messy, incomplete, and unstructured; it is often of uncertain provenance and quality; and frequently must be combined with other data to be useful. Working with user-generated data sets also raises challenging issues of privacy, security, and ethics