DESKTOP APPLICATION
Interactive visualizations.
Export publication-ready figures.
# Quick analysis
$ pca analyze data.csv
$ pca validate spectra.csv
$ pca transform model.json
COMMAND-LINE TOOL
Automation-ready tool.
CSV input format.
JSON output format.
CSV EDITOR
Prepare data for PCA analysis.
Handle missing values.
Import CSV files with numerical data.
Use GoCSV Desktop for easy import and editingConfigure components and algorithm. View results instantly.
Execute PCA computation in GoPCA Desktop or pca CLISix real datasets. Six guided tutorials. From Iris flowers to EEG brain signals — each dataset teaches a specific skill: reading scores and loadings, choosing preprocessing, handling spectroscopic data, unrolling nonlinear structure, analysing time series, and monitoring dynamic processes.
The same application you use to learn is the one you use professionally. No tutorial mode. No feature limits. Load your own CSV, run the analysis, export your results. GoPCA is ready when you are.
The datasets were chosen because they expose real problems: scale imbalance, outlier domination, multiplicative scatter in spectra, curved manifolds, temporal dynamics, and process monitoring. Not toy examples — real data with real lessons.
Iris → Wine → Corn → Swiss Roll → Eye State → CSTR. Each tutorial builds on the last. By the end you will have used every major feature of GoPCA and encountered every major challenge that real datasets present.
All computations run on your machine. No external dependencies.
Full source code publicly viewable. Free binary redistribution. No proprietary black box.
SVD, NIPALS, Kernel, and SSA algorithms. Validated against scikit-learn.
No telemetry. No analytics. No data collection.