Aims and Scope

Journal of Statistics and Data Analytics

Published by PT. Gammabeta Edu and Tech

Quick Summary: Journal of Statistics and Data Analytics (JSDA) is a peer-reviewed scholarly journal dedicated to the development, advancement, and application of statistical methods, computational techniques, and data analytics. The journal welcomes theoretical, methodological, computational, and applied research in statistics, statistical modeling, data analytics, machine learning, data mining, predictive modeling, and interdisciplinary applications of quantitative methods.

A. ABOUT THE JOURNAL

Journal of Statistics and Data Analytics (JSDA) is a peer-reviewed scholarly journal published by PT. Gammabeta Edu and Tech. The journal serves as an academic platform for the development and dissemination of research in statistics, statistical methodology, computational statistics, data analysis, and data-driven approaches.

JSDA brings together statisticians, data analysts, data scientists, researchers, academics, and practitioners who develop or apply quantitative methods to theoretical and real-world problems. The journal particularly encourages manuscripts that demonstrate statistical rigor, methodological innovation, computational reproducibility, appropriate interpretation of data, and meaningful analytical contributions.

Mission: To provide a scholarly platform for advancing statistical knowledge, quantitative methodology, computational approaches, and data analytics, while promoting rigorous, transparent, reproducible, and meaningful data-driven research.

B. AIMS

Journal of Statistics and Data Analytics aims to advance knowledge in statistical science and data analytics through the publication of high-quality theoretical, methodological, computational, and applied research.

The journal aims to:

Promote the development of statistical theories, methods, models, and computational techniques;
Disseminate rigorous applications of statistical methods to real-world data and problems;
Encourage methodological innovation in statistical modeling and data analytics;
Support the development and evaluation of data-driven and predictive methods;
Promote reproducible, transparent, and responsible quantitative research;
Encourage integration between statistics, computing, machine learning, and data science;
Facilitate interdisciplinary applications of statistics and data analytics across scientific and professional fields;
Strengthen scholarly communication among statisticians, data analysts, researchers, and practitioners at national and international levels.

C. FOCUS AND SCOPE

JSDA welcomes original research, methodological contributions, computational studies, reviews, and interdisciplinary applications that advance theory or practice in statistics and data analytics. The scope includes, but is not limited to, the following areas:

Statistical Theory and Inference

Probability-based statistical methods, estimation, hypothesis testing, confidence procedures, asymptotic methods, decision theory, and statistical inference.

Applied Statistics

Application of statistical methods to scientific, economic, business, health, educational, environmental, agricultural, engineering, and social problems.

Statistical Modeling

Linear and nonlinear regression, generalized linear models, generalized additive models, mixed models, multilevel models, latent variable models, and related modeling approaches.

Computational Statistics

Statistical computing, simulation, Monte Carlo methods, resampling, numerical methods, optimization, computational algorithms, and statistical software.

Multivariate Data Analysis

Multivariate modeling, principal component analysis, factor analysis, discriminant analysis, canonical methods, dimension reduction, and multivariate classification.

Time Series and Forecasting

Time series modeling, forecasting, state-space models, financial time series, longitudinal patterns, trend analysis, and temporal data analytics.

Spatial and Spatio-Temporal Statistics

Spatial analysis, geostatistics, spatial regression, spatial dependence, geographical data analysis, and spatio-temporal modeling.

Bayesian Statistics

Bayesian inference, hierarchical Bayesian models, Bayesian computation, Markov Chain Monte Carlo, Bayesian decision methods, and Bayesian applications.

Nonparametric and Semiparametric Methods

Nonparametric estimation, kernel methods, smoothing, rank-based methods, semiparametric regression, and distribution-free procedures.

Sampling and Survey Methodology

Survey sampling, complex survey design, estimation methods, questionnaire methodology, small-area estimation, and official statistics.

Experimental Design

Design and analysis of experiments, factorial designs, response surface methods, randomized designs, optimization, and experimental data analysis.

Survival and Reliability Analysis

Survival models, event-history analysis, hazard modeling, reliability analysis, lifetime distributions, and risk modeling.

Data Mining and Knowledge Discovery

Pattern discovery, classification, clustering, association analysis, feature extraction, anomaly detection, and knowledge discovery from data.

Machine Learning and Predictive Analytics

Statistical machine learning, supervised and unsupervised learning, predictive modeling, feature selection, model validation, and interpretable machine learning.

Big Data Analytics

Statistical approaches to large, high-dimensional, complex, heterogeneous, and large-scale data, including scalable analytical methods.

Data Visualization

Statistical graphics, exploratory visualization, interactive visualization, visual analytics, and methods for communicating complex data.

Statistical Quality Control

Statistical process control, control charts, quality improvement, industrial statistics, process capability, and reliability monitoring.

Optimization and Decision Analytics

Optimization for data analysis, decision modeling, multi-criteria methods, risk analysis, and quantitative decision-support systems.

Biostatistics and Health Analytics

Statistical applications in medicine, public health, epidemiology, clinical research, biomedical sciences, and health data analytics.

Econometrics and Business Analytics

Econometric methods, financial analytics, business intelligence, marketing analytics, economic forecasting, and quantitative business decision-making.

Educational and Social Statistics

Quantitative methods in education, psychometrics, social sciences, population studies, demography, and behavioral data analysis.

Environmental and Agricultural Statistics

Statistical methods applied to environmental, ecological, climate, agricultural, and natural-resource data.

Interdisciplinary Applications of Statistics and Data Analytics

Rigorous quantitative and data-analytic applications in science, technology, engineering, health, economics, business, education, agriculture, environment, social sciences, and other interdisciplinary domains.

Scope Note: JSDA welcomes machine learning, data mining, artificial intelligence, and computational studies when the manuscript demonstrates a clear contribution to statistical methodology, data analysis, model evaluation, quantitative inference, or data-driven decision making. Manuscripts focused primarily on software engineering, computer networks, cybersecurity, hardware development, or general computer science without a substantive statistical or data-analytic contribution are outside the journal's primary scope.

D. TYPES OF MANUSCRIPTS

Original Research Articles	Review Articles	Methodological Papers
Applied Statistical Studies	Computational Studies	Data Analytics Case Studies

E. RESEARCH APPROACHES

JSDA welcomes a broad range of quantitative and computational research approaches, provided that the methodology is scientifically justified, clearly described, and appropriately evaluated. Relevant approaches include:

Theoretical and mathematical development	Simulation and Monte Carlo studies
Empirical statistical analysis	Comparative methodological evaluation
Predictive modeling and validation	Computational and algorithmic analysis
Interdisciplinary quantitative research	Real-world data analytics

F. TARGET AUDIENCE

Statisticians	Data Scientists	Data Analysts
Researchers	Academics	Quantitative Scientists
Econometricians	Biostatisticians	Graduate Students
Industry Practitioners	Decision Analysts	Policymakers

G. PEER REVIEW AND PUBLICATION QUALITY

Double-Blind Peer Review

Manuscripts submitted to Journal of Statistics and Data Analytics are evaluated through a double-blind peer-review process. Manuscripts that pass the initial editorial screening are evaluated by reviewers with expertise relevant to the methodological, statistical, computational, or applied subject matter of the manuscript.

The review process considers, among other aspects, originality, methodological rigor, validity of statistical analysis, appropriateness of model selection, quality of data interpretation, reproducibility, clarity of presentation, and significance of the contribution.

H. JOURNAL INFORMATION

Journal Title	Journal of Statistics and Data Analytics
Initials	JSDA
Abbreviation	J. Stat. Data Anal.
Publisher	PT. Gammabeta Edu and Tech
Peer Review	Double-Blind Peer Review
Primary Language	English
e-ISSN	To be announced
DOI	To be announced
Country	Indonesia

I. CALL FOR SUBMISSIONS

Ready to Share Your Research?

Journal of Statistics and Data Analytics invites statisticians, data scientists, researchers, academics, and practitioners to submit original and high-quality manuscripts that contribute to the advancement of statistics and data analytics. The journal particularly welcomes research that combines rigorous statistical methodology with meaningful analytical applications, innovative computational approaches, transparent model evaluation, and clear interpretation of results.

Submit Your Manuscript

J. CONTACT INFORMATION

Editorial Office
Journal of Statistics and Data Analytics
PT. Gammabeta Edu and Tech
Jl. Rijali RT 003 RW 001
Ambon 97123, Maluku, Indonesia
E-mail: jsda@gammabeta.id

For questions regarding the journal's aims and scope, manuscript suitability, submission requirements, or publication process, authors are encouraged to contact the Editorial Office through the official journal communication channels.

Advancing Statistics and Data-Driven Research

Journal of Statistics and Data Analytics seeks to connect statistical theory, methodological development, computational approaches, and real-world data applications to support rigorous, reproducible, and meaningful quantitative research.

Journal of Statistics and Data Analytics
Statistics • Data • Analytics

Published by PT. Gammabeta Edu and Tech