SPICE: Simulated Pore Interactive Computing Environmen

Interactive computing environments consisting of screen and keyboard provides a means to relax and enjoy the program output. Leisurely, ways to slow and relax program execution is delved with system calls like delay execution, synthesis execution and file management execution. The leisure time can be the exact delay time used in slowly the chances of output activity.

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Jupyter: Thinking and Storytelling with Code and Data

10.22541/au.161298309.98344404/v2 ◽

2021 ◽

Author(s):

Brian Granger ◽

Fernando Pérez

Keyword(s):

Machine Learning ◽

Open Source ◽

Community Of Practice ◽

Scientific Computing ◽

Data Science ◽

Climate Science ◽

Three Dimensions ◽

Open Source Project ◽

Interactive Computing ◽

The Impact

Project Jupyter is an open-source project for interactive computing widely used in data science, machine learning, and scientific computing. We argue that even though Jupyter helps users perform complex, technical work, Jupyter itself solves problems that are fundamentally human in nature. Namely, Jupyter helps humans to think and tell stories with code and data. We illustrate this by describing three dimensions of Jupyter: interactive computing, computational narratives, and the idea that Jupyter is more than software. We illustrate the impact of these dimensions on a community of practice in Earth and climate science.

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An overview of two open interactive computing environments useful for data science education

JAMIA Open ◽

10.1093/jamiaopen/ooy040 ◽

2018 ◽

Vol 1 (2) ◽

pp. 159-165

Author(s):

Robert Hoyt ◽

Victoria Wangia-Anderson

Keyword(s):

Science Education ◽

Programming Languages ◽

Data Science ◽

Predictive Analytics ◽

Data Exploration ◽

Online Data ◽

Education Benefits ◽

Interactive Computing ◽

Data Analyses ◽

Computing Environments

Abstract Objective To discuss and illustrate the utility of two open collaborative data science platforms, and how they would benefit data science and informatics education. Methods and Materials The features of two online data science platforms are outlined. Both are useful for new data projects and both are integrated with common programming languages used for data analysis. One platform focuses more on data exploration and the other focuses on containerizing, visualization, and sharing code repositories. Results Both data science platforms are open, free, and allow for collaboration. Both are capable of visual, descriptive, and predictive analytics Discussion Data science education benefits by having affordable open and collaborative platforms to conduct a variety of data analyses. Conclusion Open collaborative data science platforms are particularly useful for teaching data science skills to clinical and nonclinical informatics students. Commercial data science platforms exist but are cost-prohibitive and generally limited to specific programming languages.

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