Your state
Why it matters.
Every workplace and household runs on numbers nobody reads. The person who can turn a spreadsheet into one honest sentence — and one decision — becomes the one people trust with the truth.
What success looks like.
- A spreadsheet built from real data with working sum, average and count formulas.
- One chart of the data, chosen to actually fit the question.
- A plain-words explanation of what the numbers show — and one thing they hide.
The component skills.
Typical mistakes.
- — Charts that start the y-axis wherever it flatters the story.
- — Averaging things that shouldn't be averaged (one outlier owns the mean).
- — Formatting the sheet for beauty instead of sorting it for sense.
Practise — activities that produce artifacts.
Structure the honest columns ~30 min
Take one real dataset from your life — a month of expenses, a week of sales, attendance, anything with 20+ rows. Give every column a name, a type and one line saying where the data came from.
Artifact: The structured sheet with a source note per column
The sum-average-count trio ~30 min
On your structured sheet, add three formulas that answer three real questions: total, typical, and how many. Write each question above its formula — formulas without questions are decoration.
Artifact: The sheet with three labelled formulas
Missions — do it for real.
Mentors for this capability.
Matched from real, opted-in DISHA members through the Mentor Intelligence Network — with their trust states shown as stored. Source-listed is not the same as verified.
No opted-in mentors match this capability yet — none are invented. See the Mentors view or the full directory.
How assessment works.
Every submission gets a transparent first-pass against the mission's rubric — what was looked for and what was observed. Verified state requires a real review; the system never upgrades self-reported evidence on its own.
audience_fit
Words and comparisons the audience can follow
accuracy
Concrete numbers, units and exact values instead of vague amounts
completeness
All parts of the task covered — nothing skipped or assumed away
evidence_quality
An observable artifact: worksheet, receipt, photo reference, recording note
self_awareness
Honest signals: what you noticed, what was hard, what you'd change
Accepted evidence: mission_submission, mentor_review, observed_application, work_sample.
Stated plainly — what this doesn't do
- — Spreadsheet analysis is only as honest as its inputs — figures from unreliable sources stay unreliable, whatever the chart looks like.
- — Submissions are private, self-reported evidence — they never become verified without a real review.
- — Mentor review for capabilities arrives in a later phase of this module.
Source: DISHA MY LIFE SKILLS Module Master Specification v1.0 §4 · v1 · last checked 2026-10-25 · your data rights