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The Practice of Data Science

The practice of Data Science is based on the recognition that large quantities of data are: (i) Available before a question is posed and (ii) Collected for purposes that may not be related to the specific questions of interest. 

Data Science extracts meaning from data revealing insights that lie hidden beneath the surface of "big data" increasingly produced by routine individual behavior.  Data Science is a fundamentally interdisciplinary enterprise because it integrates multiple areas of scientific inquiry, each characterized by different skills, training, and professional development trajectories. Data Science projects aim to make sense of data, whether it is big data, complex data, qualitative, hidden, incomplete or corrupted data.

The Data Science process generally includes the following phases:

 

Interpret

Understanding the data generating process
social science, communication, behavioral and cognitive science;

 

Manage

data engineering, acquisition, storage, indexing, retrieval, pre-processing, quality assurance

 

Visualize

visualization, validation

 

Analyze

statistical modeling, estimation and prediction, profiling, pattern recognition

 

Learn

synthesizing analysis, supporting individuals and organizations to address specific problems or improve and re-engineer a process

 

Act

Providing actionable knowledge for decision making

 

This Data Science process represents a value chain and is universal across many empirical domains of application.

 

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