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Statistics - Computational Statistics | Call For Papers: Special Feature—Perspectives on Data Science for Advanced Statistics

Call For Papers: Special Feature—Perspectives on Data Science for Advanced Statistics

Editor: Makoto Aoshima (Institute of Mathematics, University of Tsukuba, Japan)

Data Science is certainly a hot term right now. Some PhD students mentioned that to get a job, you should list "Data Science" on your PhD whether you know anything about it or not! It seems to be becoming increasingly known that there are a lot of unqualified "data scientists" out there.
What is "Data Science"? I took the following snippet from the Wikipedia page on "Data Science": ‘to extract knowledge or insights from data’. For me, it is really interesting that this is so similar to the definition of statistics that I offer, and have been offering for the past 25 years, when I teach elementary statistic courses.
That being said, is there any value added from this new terminology? I feel that there is, and the reason is that by renaming statistics it somehow seems to free up folks to start thinking about statistical matters, especially those who would probably not do so without that naming. In particular, lots of very talented people from fields such as math, CS and various areas of engineering want to call themselves "Data Scientists", and they are in the process of bringing lots of exciting new ideas, approaches and ways of thinking to statistics.
The aim of this special feature is to gain perspectives on data science for advanced statistics. We welcome original research articles, reviews, theoretical articles and methodological articles. Experimental and theoretical contributions are also welcomed.

Possible topics could include, but are not limited to, the following

- Interplay between machine learning, signal processing, and statistics for Big Data
- Integration of complex and diverse data types
- Model selection for high-dimensional data
- Natural language processing
- Data heterogeneity
- Stochastic processes and statistical learning theory toward modeling of dependencies
- Estimation pitfalls when the noise is not i.i.d.
- History and statistical inferences of intrinsic stationary random fields
- Required skills for data scientists and curriculum of data science

When you submit

Please select
Special Feature: Perspectives on Data Science for Advanced Statistics
at the “Additional Information” stage.

Submission deadline:

December 31, 2017