There is significant and growing demand for data-savvy professionals in businesses, public agencies, and nonprofits. The supply of professionals who can work effectively with data at scale is limited, and is reflected by rapidly rising salaries for data engineers, data scientists, statisticians, and data analysts.
The statistics listed below represent this significant and growing demand for data scientists.
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 behavior1.
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.
"This hot new field promises to revolutionize industries from business to government, health care to academia."
— The New York Times
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.
The field of data science is emerging at the intersection of the fields of social science and statistics, information and computer science, and design. The UC Berkeley School of Information is ideally positioned to bring these disciplines together and to provide students with the research and professional skills to succeed in leading edge organizations.
1 There is no agreed upon definition for "big data." The tools of data science are as appropriate for gigabyte as they are for petabyte scale datasets. "Big data" typically refers to data on the scale of terabytes (10 to the 12th power) and petabytes (10 to the 15th power). A petabyte is a million gigabytes.↑