Exploring Interdisciplinary Data Science Education for Undergraduates: Preliminary Results

The interest in data science is increasing in recent years. Data science, including mathematics, statistics, big data, machine learning, and deep learning, can be considered as the intersection of statistics, mathematics and computer science. Although the debate continues about the core area of data science, the subject is a huge hit. Universities have a high demand for data science. They are trying to live up to this demand by opening postgraduate and doctoral programs. Since the subject is a new field, there are significant differences between the programs given by universities in data science. Besides, since the subject is close to statistics, most of the time, data science programs are opened in the statistics departments, and this also causes differences between the programs. In this article, we will summarize the data science education developments in the world and in Turkey specifically and how data science education should be at the graduate level.

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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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