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To explain the different contributions, each sector is briefly explained with more emphasis on information science. Citation: Journal of Data and Information Science 1, 2; The knowledge domain sector will surely consume the most data scientist practitioners and each domain will strongly influence the kinds of data that are collected and the decisions made based on analysis. Chemists, political scientists, physicians, journalists, accountants, and scholars from all fields will adopt and adapt the tools and techniques created by data scientists and many of these professionals will want to specialize in data science in their domains.

Disciplinary context strongly determines what data are collected, what metadata is most essential to understanding the data, how data quality is determined, and the value of analytic outcomes.

Statistical model - Wikipedia

Any data science training program will have to consider how domain expertise is incorporated into curricula, capstone projects, and student portfolios. Statistics first emerged as an applied subfield of mathematics and over the past years leveraged principles of probability to establish theories for sampling, estimation and error, and significance.

The first statistics departments and first dedicated journal were founded at the turn of the 20 th century and statisticians today create new techniques for summarizing datasets, making and evaluating inferences from data, and invent new models for analyzing meaning and making predictions based on data. Statistical theory and techniques contribute to data science by informing decisions about what data to collect or include in analysis e. Computer science first emerged in the first part of the 20 th century as mathematicians and physicists engineered machines that counted and compared data at speeds well beyond human abilities.

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Key ideas such as storing instructions programs as well as data and estimating computational complexity led to computational theory that blossomed in the middle of the 20 th century into the new field that gave form to academic departments, journals, and professional associations. Computer scientists create or adapt mathematical and statistical algorithms that make high-speed and high-volume data processing practical.

Increasingly, computer scientists work with domain experts to model data distributions, patterns, and dynamics using empirical methods e. Computer science contributes to data science by providing the machine and algorithmic techniques that domain experts can apply to ask and answer questions.

This movement shifted the focus of librarians from collecting historical knowledge sources to systematic management of those sources and reflected the development of scientific management practices Taylorism in industry.

The development of information theory by scientists like Alan Turing and Norbert Wiener led to analog and digital computers in the s. World War II drove advances in communications and intelligence gathering, management, and interpretation cryptography and statistical modeling that preluded data mining as well as information control techniques operations research to manage huge numbers of people and supplies moving around the entire planet. Today, information science investigates architectures and ontologies for data collections; curatorial and management processes for digital assets; information ethics and policies; knowledge genesis, flow, and preservation; human information interactions; and a variety of applications of information science principles and practices to health health informatics , humanities digital humanities , commerce financial informatics , law and government legal informatics , and environment environmental informatics.

As noted above information scientists are concerned with the entire data life cycle and also with the socio-cultural issues associated with data collection and use. Some examples include:. Ethical and legal conditions associated with data collection e.

Why Use Mathematical and Statistical Models

It could be argued that data science is a subset of information science and some data science training programs may be housed in information schools, however, it is more strategic to view information science as an essential component of data science so that the emerging field can benefit from the diversity of perspectives that interdisciplinary collaborations bring. Information science programs can participate as key partners to ensure that students are prepared to take the lead on the socio-cultural issues noted above.

Additionally, information schools that have strong technical faculty can help students develop technical skills to manage distributed data e. HIVE toolkit. As the field of data science continues to develop, the specific skills that data scientists and practitioners need will continue to evolve. Information science programs should be active participants in the interdisciplinary teams that will shape the field.

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