Guided Tutorials

June 13, 2026 ยท View on GitHub

Guided Tutorials are scenario-driven walkthroughs that show how to solve real tasks with Insyra.

Purpose

This section is for readers who want to learn by doing.
Each tutorial starts from a practical scenario and walks through an end-to-end workflow.

How to use this section

  1. Pick one tutorial based on your current task.
  2. Follow the steps in order and run the code as you read.
  3. Adapt the final pipeline to your own dataset or automation flow.

Tutorials

TutorialCategoryLearning goalEstimated timeLink
Sales Analysis End-to-EndData EngineeringBuild a complete sales analysis pipeline from raw CSV to enriched output using DataTable + CCL.15-20 minOpen
Data Quality and Column EngineeringData EngineeringClean messy records, apply replacements/filters, and engineer business columns with CCL.15-20 minOpen
Parquet Inspection, Streaming, and CCL FilteringData EngineeringInspect schema/row-groups, stream safely, and filter parquet data with CCL.20-25 minOpen
A/B Test Decision with StatisticsAnalyticsTurn experiment samples into a ship/hold decision with statistical evidence.15-20 minOpen
Nonparametric Tests When Normality FailsAnalyticsRoute ordinal / small-n / heavy-tailed data to Wilcoxon, Mann-Whitney U, Kruskal-Wallis, or Friedman.15-20 minOpen
GLM for Binary Outcomes and CountsAnalyticsChoose logistic, Poisson, or generic GLM, including offsets and over-dispersion checks.15-20 minOpen
Customer Segmentation with RFM and CAIAnalyticsSegment customers by value and activity momentum for retention/upsell actions.20-25 minOpen
Market Trend Forecasting with Yahoo FinanceAnalyticsFetch market data, apply offline fallback, fit trend regression, and export outputs.20-25 minOpen
Interactive KPI Dashboard with plotAnalyticsBuild browser-ready KPI charts and export HTML/PNG artifacts.15-20 minOpen
Static Executive Report with gplotAnalyticsGenerate static PNG charts for executive reports and slide decks.15-20 minOpen
Capacity Planning with lp and lpgenOptimizationBuild and solve a linear-programming model for constrained capacity planning.20-25 minOpen
Python Enrichment and Parallel Batch ProcessingIntegrationCombine Python feature engineering with parallel KPI computation in Go.20-25 minOpen

Planned next topics

  • Cohort retention deep dive
  • Anomaly detection playbook
  • Demand scenario stress testing