Valuation challenges in statistics and data science curricula

2019 ◽  
Vol 41 (3) ◽  
pp. 77-78
Author(s):  
Helen MacGillivray
Keyword(s):  
Data Science ◽  
Science Curricula

scholarly journals What is Data Science? An Operational Definition based on Text Mining of Data Science Curricula

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Zhiyong Zhang ◽  
Keyword(s):  
United States ◽  
Text Mining ◽  
Computer Science ◽  
Topic Modeling ◽  
Data Science ◽  
The United States ◽  
Operational Definition ◽  
Bottom Up ◽  
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.

scholarly journals The democratization of data science education

2017 ◽  
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.

scholarly journals The democratization of data science education

2017 ◽  
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.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification
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