Statistical Analysis and Data Mining: The ASA Data Science Journal

Cover image for Vol. 10 Issue 1

Edited By: Niall Adams

Impact Factor: 0.574

ISI Journal Citation Reports © Ranking: 2015: 93/123 (Statistics & Probability); 95/104 (Computer Science Interdisciplinary Applications); 111/130 (Computer Science Artificial Intelligence)

Online ISSN: 1932-1872


Aims and Scope

Statistical Analysis and Data Mining addresses the broad area of data analysis, including data mining algorithms, statistical approaches, and practical applications. Topics include problems involving massive and complex datasets, solutions utilizing innovative data mining algorithms and/or novel statistical approaches, and the objective evaluation of analyses and solutions. Of special interest are articles that describe analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce.

The focus of the journal is on papers which satisfy one or more of the following criteria:

  • Solve data analysis problems associated with massive, complex datasets
  • Are application and solution oriented with a focus on solving real problems
  • Describe innovative data mining algorithms or novel statistical approaches
  • Compare and contrast techniques to solve a problem, along with an objective evaluation of the analyses and the solutions

The goals of this interdisciplinary journal are to encourage collaborations across disciplines, communication of novel data mining and statistical techniques to both novices and experts involved in the analysis of data from practical problems, and a principled evaluation of analyses and solutions.

The 21st Century has become a Century of Data, with most domains striving for useful general models for their mountains of data. Data mining and statistical analysis are amongst the most effective bodies of methodology and technology capable of producing useful general models from massive, complex datasets.

Statistical Analysis and Data Mining will be a useful resource to those solving practical problems, at the same time enabling them to benefit from ideas developed in other domains. It will be an international journal, with an interdisciplinary focus, covering areas which are becoming increasingly important, and likely to remain so in the foreseeable future.

Guidelines for Reviewers

1) Scan the paper to identify the key contribution, if any.

2) If the key contribution is minor, reject the paper.

3) If the key contribution is substantial:
      a) Synopsize the main ideas of the paper in your own summary;
      b) If you know of any closely related research not covered in the paper, mention it;
      c) Check the paper for accuracy and note corrections;
      d) Check the paper for clarity and suggest alternative wordings where appropriate;
      e) If you find the paper incomplete, consider writing your own publishable comment.

4) Do not hesitate to ask the Associate Editor to obtain any of the following, as needed:
      a) Reference papers not easily accessible;
      b) Original source data;
      c) Original code.

5) Make your recommendation for:
      a) Acceptance – paper publishable as is;
      b) Minor Revision – no serious errors;
      c) Major Revision – poorly written or containing potentially correctable flaws;
      d) Rejection – paper would need to be totally rewritten or should be abandoned as a bad idea.


Statistical Analysis and Data Mining seeks to inform upper-level undergraduates and postgraduate students, teachers and researchers in data intensive fields, and scientists and research managers in industry that focus on data; moreover, Statistical Analysis and Data Mining includes reviews and tutorial-like articles that are especially useful for individuals entering data science fields.


Data science; applied statistics; artificial intelligence; biostatistics and bioinformatics; computational Bayesian methods; computationally intensive statistical methods; data analysis; data mining; data structures; data visualization; machine learning; statistical learning; modeling and simulation; numerical analysis; optimization; statistical methods.
Abstracting and Indexing Information

  • Current Contents: Engineering, Computing & Technology (Clarivate Analytics)
  • Current Index to Statistics (ASA/IMS)
  • Mathematical Reviews/MathSciNet/Current Mathematical Publications (AMS)
  • Science Citation Index Expanded (Clarivate Analytics)
  • SCOPUS (Elsevier)
  • The DBLP Computer Science Bibliography (University of Trier)
  • Web of Science (Clarivate Analytics)
  • ZBMATH (Zentralblatt MATH)