What is Data Science?
Data Science is about turning data into knowledge. By using cross-disciplinary principles from information science, statistics, and computer science, data science strives to sort through the data deluge and extract the information needed for decision-making.
Online, we can track who sees what publications, when, from where, and with what level of engagement. Altmetrics uses this data to complement traditional measurements of scholarly works’ reach and impact. Altmetrics, for instance, can measure the number of times a work has been downloaded, shared, covered in the news, or commented on, or even the spread of viewership across the world.
Big Data refers to a giant leap in the quantity of data produced in the 21st century due to recent technological advancements. Research in big data is interdisciplinary and seeks to understand the issues surrounding big data and to develop solutions for the management, analysis, and use of huge quantities of data.
Data visualization uses visual tools such as graphs and maps to enhance understandings of data.
Machine learning is the ability for computers to improve results through experience. The more a user interacts with the machine, the more data it receives; the more data it receives, the more accurate its determination of relevancy.
Natural Language Processing
Research in natural language processing investigates the use of artificial intelligence and linguistics to create systems that can use context to “understand” the content of texts and speech. The computer’s enhanced ability to grasp the meaning of words can then be used to increase the accuracy of information retrieval.
- Hu, J. M. & Zhang, Y. (2018). Measuring the interdisciplinarity of big data research: A longitudinal study. Online Information Review, 42(5), 681-696. https://doi.org/10.1108/OIR-12-2016-0361
- Hu, J. M. & Zhang, Y. (2017). Discovering the interdisciplinary nature of big data research through social network analysis and visualization. Scientometrics, 112(1), 91-109.