Information Retrieval in Data Science Curricula

Data science has maintained its popularity for about 20 years. This study adopts a bottom-up approach to understand what data science is by analyzing the descriptions of courses offered by the data science programs in the United States. Through topic modeling, 14 topics are identified from the current curricula of 56 data science programs. These topics reiterate that data science is at the intersection of statistics, computer science, and substantive fields.

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The democratization of data science education

10.7287/peerj.preprints.3195v1 ◽

2017 ◽

Cited By ~ 1

Author(s):

Sean Kross ◽

Roger D Peng ◽

Brian S Caffo ◽

Ira Gooding ◽

Jeffrey T Leek

Keyword(s):

Machine Learning ◽

Science Education ◽

Data Analysis ◽

Data Science ◽

Online Data ◽

The Past ◽

The Us ◽

Science Curricula ◽

The Impact ◽

And Training

Over the last three decades data has become ubiquitous and cheap. This transition has accelerated over the last five years and training in statistics, machine learning, and data analysis have struggled to keep up. In April 2014 we launched a program of nine courses, the Johns Hopkins Data Science Specialization, which has now had more than 4 million enrollments over the past three years. Here the program is described and compared to both standard and more recently developed data science curricula. We show that novel pedagogical and administrative decisions introduced in our program are now standard in online data science programs. The impact of the Data Science Specialization on data science education in the US is also discussed. Finally we conclude with some thoughts about the future of data science education in a data democratized world.

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Module and Kemp Instructional Design Approaches to Integrate STEM Issues and Public Policy into Data Science Curricula at a Native Hawaiian and Pacific Islander-serving Institution

10.18260/1-2--33118 ◽

2020 ◽

Author(s):

Rylan Chong ◽

Helen Turner ◽

Mark Speck

Keyword(s):

Public Policy ◽

Instructional Design ◽

Data Science ◽

Native Hawaiian ◽

Pacific Islander ◽

Science Curricula

Download Full-text

The democratization of data science education

10.7287/peerj.preprints.3195 ◽

2017 ◽

Cited By ~ 1

Author(s):

Sean Kross ◽

Roger D Peng ◽

Brian S Caffo ◽

Ira Gooding ◽

Jeffrey T Leek

Keyword(s):

Machine Learning ◽

Science Education ◽

Data Analysis ◽

Data Science ◽

Online Data ◽

The Past ◽

The Us ◽

Science Curricula ◽

The Impact ◽

And Training

Over the last three decades data has become ubiquitous and cheap. This transition has accelerated over the last five years and training in statistics, machine learning, and data analysis have struggled to keep up. In April 2014 we launched a program of nine courses, the Johns Hopkins Data Science Specialization, which has now had more than 4 million enrollments over the past three years. Here the program is described and compared to both standard and more recently developed data science curricula. We show that novel pedagogical and administrative decisions introduced in our program are now standard in online data science programs. The impact of the Data Science Specialization on data science education in the US is also discussed. Finally we conclude with some thoughts about the future of data science education in a data democratized world.

Download Full-text