JSDA
Statistics • Analytics • Data Science
Statistics • Data • Analytics

Journal of Statistics and Data Analytics (JSDA) is a peer-reviewed scholarly journal dedicated to the development and application of statistical methods, data analysis, and data-driven approaches across diverse fields of research. The journal provides an academic platform for researchers, academics, practitioners, statisticians, and data analysts to disseminate original research, methodological developments, computational approaches, and applied studies.

JSDA welcomes contributions in statistical theory, applied statistics, statistical modeling, computational statistics, multivariate analysis, time series analysis, spatial statistics, Bayesian statistics, data mining, machine learning, predictive analytics, and interdisciplinary applications of statistics and data analytics. The journal particularly encourages studies demonstrating rigorous statistical reasoning, methodological innovation, and meaningful applications of data analytics to real-world problems.

Applied Statistics Statistical Modeling Data Analytics Computational Statistics Machine Learning Predictive Analytics Data Mining
Journal Information
Journal Title : Journal of Statistics and Data Analytics
Initials : JSDA
Abbreviation : J. Stat. Data Anal.
Frequency : Biannual [April and October]
DOI : Prefix 10.68215 by Crossref
ISSN : 3164-7820
Language : Bahasa, English
Peer Review : Double-Blind Peer Review
Editor in Chief : Ahmad Zaenal Arifin, M.Si. (Doctoral Candidate)
Publisher : PT. Gammabeta Edu and Tech
Country : Indonesia
Open Access
Free access for readers
Double-Blind Review
Independent peer review
Statistics
Theory and applications
Data Analytics
Data-driven research
Focus and Scope
Advancing statistical methodology and data-driven research.

Journal of Statistics and Data Analytics publishes original research, methodological studies, computational approaches, and applied investigations in statistics and data analytics. The journal welcomes theoretical, empirical, and interdisciplinary contributions that advance statistical methodology or demonstrate meaningful applications of analytical approaches to real-world data.

Statistical Theory & Methods
Probability, statistical inference, estimation, hypothesis testing, nonparametric methods, and Bayesian statistics.		 Applied Statistics
Statistical applications in science, health, education, economics, business, agriculture, environment, and social sciences.
Statistical Modeling
Regression, generalized linear models, multilevel models, longitudinal analysis, survival analysis, and related methods.		 Computational Statistics
Simulation, Monte Carlo methods, statistical computing, numerical methods, optimization, and statistical algorithms.
Data Analytics & Data Mining
Exploratory analysis, classification, clustering, pattern recognition, knowledge discovery, and predictive analytics.		 Machine Learning
Statistical machine learning, predictive modeling, model evaluation, feature selection, and interpretable analytical methods.
Specialized Statistical Analysis
Time series, spatial statistics, multivariate analysis, experimental design, sampling, reliability, and quality control.		 Interdisciplinary Data Analytics
Biostatistics, business analytics, educational analytics, environmental analytics, social data analytics, and related applications.
Before Submission
Please review the journal requirements before submitting your manuscript.

Authors are required to prepare manuscripts using the official Journal of Statistics and Data Analytics template and comply with the Author Guidelines. All manuscripts should be carefully proofread, scientifically rigorous, and screened for originality before submission.

Important Submission Requirements
  • Manuscripts must be prepared using the official JSDA template.
  • The manuscript must fall within the journal's aims and scope.
  • Authors must comply with the Author Guidelines and Publication Ethics.
  • Statistical methods and analytical procedures must be clearly described and scientifically justified.
  • Data analysis should be reproducible where reasonably possible.
  • Manuscripts should be thoroughly proofread before submission.
  • Submissions are subject to originality and similarity screening.
  • Manuscripts that do not meet minimum editorial requirements may be declined during initial screening.
Journal of Statistics and Data Analytics
Advancing Statistics, Data Analytics, and Data-Driven Research
STATISTICS • ANALYTICS • DATA SCIENCE

Current Issue

Vol. 1 No. 1 (2026): Journal of Statistics and Data Analytics
View Vol. 1 No. 1 (2026): Journal of Statistics and Data Analytics
Published: 2026-09-14

Articles

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