Skip to content
Hara

Hara for Data Scientists

Hara is useful when an analysis needs to become a live, inspectable part of a web application—not just a notebook result or a batch job.

Use a kernel session to keep the values, transformations, and evidence for an experiment together. A browser workspace, a command-line session, and a tool can inspect the same running system.

(def observations [12 19 23 18 28])

(def mean
  (/ (reduce + observations) (count observations)))

{:mean mean
 :count (count observations)}

The important distinction is that the values are not only printed: they remain available for inspection, controlled changes, visual output, and a durable project record.

A practical workflow

  1. Load or create a bounded dataset.
  2. Evaluate one transformation at a time.
  3. Inspect the value and any visual result.
  4. Keep the verified step in source.

Hara does not replace a specialised Python or R ecosystem. It provides a portable kernel boundary for applications that need their data work, runtime state, and visible output to remain connected.