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EDA-Pro: A Web-Based Automated Exploratory Numerical Data Analysis System
Authors:
| Nadiba Zaman Kaifa | Department of Computer Science and Engineering, Sonargaon University, Dhaka, Bangladesh |
| B. M. Salahuddin | HafizLab |
| S. M. Alauddin | Department of Computer Science and Engineering, Shyamoli Engineering College, Dhaka, Bangladesh |
| Muhammad Shihab | HafizLab |
| Md. Hafizur Rahman | HafizLab, Bangladesh |
| Md. Masud Reza | Department of Computer Science and Engineering, The People's University of Bangladesh |
| M. Naderuzzaman | Department of Computer Science and Engineering, Sonargaon University, Dhaka, Bangladesh |
Submission Date: 15-04-2026, Accepted Date: 12-05-2026, Publication Date: 15-05-2026
https://oajea.hafizlab.com/article/01-02-007 
https://doi.org/10.64886/oajea.0102.007 
Index Terms:
Exploratory Data Analysis, Statistical Testing, Web-Based Analytics, Data Visualization, Automated Analysis, Bootstrap Methods, Hypothesis TestingAbstract:
Exploratory Data Analysis (EDA) is a critical first step in any data science workflow, yet existing tools often require software installation, programming knowledge, or lack comprehensive statistical testing capabilities. We present **EDA-Pro**, a browser-based automated system for exploratory numerical data analysis that integrates descriptive statistics, hypothesis testing, time series analysis, data transformation, and AI-powered insight generation in a unified interface. Unlike existing tools such as pandas-profiling and Sweetviz, EDA-Pro operates entirely client-side without server dependencies, supports up to 50MB datasets, and provides publication-ready HTML reports with statistical rigor. The system includes 20+ analytical modules covering univariate/bivariate/multivariate analysis, seven hypothesis tests (Shapiro-Wilk, ANOVA, Chi-Square, Kolmogorov-Smirnov, independent/paired t-tests), bootstrap confidence intervals, multiple regression, and automated data quality assessment. Comparative evaluation demonstrates 35\% faster workflow completion compared to Python-based alternatives for standard EDA tasks, with no installation overhead. EDA-Pro is freely available as open-source software, making advanced statistical analysis accessible to researchers, students, and practitioners without programming expertise.Conclusion:
We have presented EDA-Pro, a browser-based system for exploratory data analysis that integrates descriptive statistics, hypothesis testing, time series analysis, data transformation, and AI-powered insights without requiring installation or server infrastructure. Through client-side JavaScript computation, EDA-Pro provides statistical rigor comparable to desktop software while eliminating deployment barriers.Our evaluation demonstrates:
• Statistical accuracy: Algorithms match R and Python implementations to 4+ decimal places
• Performance: 4.1× faster end-to-end workflows for first-time users; acceptable performance on datasets up to 50K rows
• Usability: 93% task completion rate in controlled study; 87% of users would recommend to colleagues • Real-world impact: Successful deployment in educational settings (0% installation failures vs. 23% with Python), collaborative research (4-week time savings), and field research (enabled analysis on low-spec equipment)
EDA-Pro makes three primary contributions to the data analysis ecosystem:
1. Accessibility: By eliminating installation requirements, EDA-Pro democratizes access to rigorous statistical analysis. Researchers in resource-constrained settings, students without programming backgrounds, and practitioners requiring immediate analysis can now perform sophisticated EDA.
2. Privacy: Client-side architecture provides cryptographic guarantees that sensitive data never leaves the user’s machine—critical for medical research (HIPAA), financial analysis, and proprietary business data.
3. Pedagogical Value: The combination of zero-setup deployment, transparent calculations, and automated interpretation makes EDA-Pro an effective educational tool. Students can focus on statistical reasoning rather than software troubleshooting.
As data analysis becomes increasingly central to research and decision-making across disciplines, tools that balance statistical rigor with accessibility play a crucial role. EDA-Pro demonstrates that comprehensive statistical analysis can be delivered through zero-installation web applications, expanding the reach of evidence-based practice.
Future work will extend EDA-Pro’s capabilities through advanced modeling (PCA, clustering, logistic regression), enhanced collaboration features (session sharing, real-time co-analysis), and performance optimizations (WebAssembly acceleration). The open-source nature of EDA-Pro invites community contributions and domain-specific extensions.
We believe browser-based statistical tools represent a promising direction for democratizing data analysis. By prioritizing accessibility alongside analytical power, such tools can help bridge the gap between statistical sophistication and practical adoption across diverse user communities.
Availability
EDA-Pro is freely available as open-source software under the MIT License:
• Live demo: https://hafizurfpbd.github.io/eda-pro.html
• Source code: https://github.com/hafizurfpbd/eda-pro
• Documentation: https://github.com/hafizurfpbd/eda-pro/wiki
Bug reports, feature requests, and contributions are welcome via GitHub Issues and Pull Requests.
License:
Articles published in OAJEA are licensed under a Creative Commons Attribution 4.0 International License.Cite This Paper:
N.Z. Kaifa, B. M. Salahuddin, S.M.A. Kabir, M. Shihab, M.H. Rahman, M.M. Reza, and M. Naderuzzaman “EDA-Pro: A Web-Based Automated Exploratory Numerical Data Analysis System”, Open Access Journal on Engineering Applications (OAJEA), Volume No. 01, Issue No. 02, Page 60-85, May, 2026. https://doi.org/10.64886/oajea.0102.007Reference:
Journal Information
| Frequency: Quarterly |
| Submission to First Decision: 7 days |
| Submission to Acceptance: 30 days |
| Accept to Publish: 15 days |
| Article Processing Charges (APCs): None |
