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DATA SCIENCE - Coggle Diagram
DATA SCIENCE
The science of data science must go beyond reporting the correlative results of data analytics to developing the predictive and prescriptive causal models that are the basis of science-driven understanding, engineering, and policy making.
First are the problems that arise from the ‘‘big’’ in big data. The amount of data can be extremely large and the data may be multimodal and thus hard to integrate using current mathematical frameworks.
The nature of the modes of sensing and sampling that current data collection systems often provide. In these systems, the discrete nature of data is at odds with the continuous nature of the problems being attacked, and this leads to a failure in a number of our current mathematical and/or algorithmic solution approaches.
The need for data infrastructures, which we contend will need to be powered by semantic technologies. These are needed to provide computational approaches that will allow researchers to search for and discover data resources, rapidly integrate large-scale data collections from heterogeneously collected resources or multiple data sets, and compare these results across datasets to allow generation and validation of hypotheses.
“A Data Scientist is a person who is better at statistics than any software engineer and better at software engineering than any statistician.”
The development of Data Science promoted a new concept in decision-making in general (including business) where decisions are data-driven, and the added value to organizations (either institutions or companies) is not more technology, nor capital, but information, where data is considered to be a primary source of knowledge.
Multidisciplinary field that combines data analysis with data processing methods and domain expertise, transforming data into understandable and actionable knowledge relevant for informed decision making
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Computational Perspective. Focuses on the algorithmic implementation of those methods, and it provides a way to understand and compare their computational footprints.
Human Perspective. Understanding a problem domain, deciding which data to acquire and how to process it, exploring and visualizing the data, selecting appropriate statistical models and computational methods, and communicating the results of the analyses.
Data science as an academic discipline. Once a body of literature is in place, academic courses can begin at universities. We should now be starting single modules, perhaps initially to graduate students. Textbooks need to follow.
‘‘Fourth paradigm’’ data-driven science, where the scientific method is enhanced by the integration of significant data sources into the practice of scientific research.