Aim and Scope
Established in 2003, the Journal of Data Science aims to advance and promote data science methods, computing, and applications in all scientific fields where knowledge and insights are to be extracted from data. The journal publishes research works on the full spectrum of the data science including statistics, computer science, and domain applications. The topics can be about any aspect of the life cycle (collecting, processing, analyzing, communicating, etc.) of data science projects from any field that involves understanding and making effective use of data. The emphasis is on applications, case studies, statistical methods, computational tools, and reviews of data science.
To help the authors better understand the aim and scope of the journal, we proposed new sections as follows.
·Statistical Data Science
This section is the homebase of the reformed journal covering statistical methods that are motivated from real world data science or big data. It is not for papers with technical proofs that push the frontiers of theoretical developments. In addition to classic topics in Statistics, cutting-edge works on big data, visualization, machine learning, and artificial intelligence are also welcome.
·Computing in Data Science
This section covers the following types of articles.
1. Software: Articles here are similar to those in Journal of Statistical Software. They are not reference manuals but vignettes that introduce the methods being implemented as well as usage of the software with reproducible code chunks. The software implementation can be in any computer language with a sufficiently large user base.
2. Algorithms: Articles here focus on the performance side of the computing needs arising from domain applications. For example, one can propose algorithms which makes infeasible tasks feasible or speed up existing algorithms.
3. Methods: Articles here are similar to those in Journal of Computational and Graphic Statistics or Statistics and Computing. The computing methods need to be motivated by a domain application with the properties carefully studied.
·Data Science in Action
This section considers entries from data science competitions, such as Kaggle, Kesci, and DataCastle, among others. We welcome winning teams of data science competitions to share their work in the form of an academic paper, with our trainings (the copyright issue on data needs to be solved). Articles can present detailed case studies of data science or big data applications. The data and the computing code should be make available such that any interested reader can reproduce everything in the article from the data and the code. The problems can come from any research domain that involves data science and computing, including science, technology, engineering, medicine, health, social science, humanities, arts, among others.
·Data Science Reviews
Reviews and tutorial articles on latest data science techniques can be of interest to readers who want to get into a new field or pick up new skills. Experts of certain fields are invited to write tutorial articles.
·Education in Data Science
Education in data science is ever-evolving along with new methodologies, technologies, and application fields. Discussion on curriculum of data science and tools in teaching data science need a good outlet.
This section contains articles that do not clearly fall into the sections above. For example, the articles that are not directly motivated from data science applications, such as those on new probability distributions that the journal published, can be published in this section.
For the online version, it is free. For the hard copy version, it is 300 USD per year.
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