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OrionStats
OS-DAP · LEVEL II

OrionStats Data Analyst Professional

Level II — independent professional analyst

The Professional certificate confirms that a candidate can work independently as a data analyst: take an unfamiliar business requirement, retrieve and model the data, apply the right analytical method (segmentation, variance, funnels, regression, forecasting basics), build an executive dashboard and defend recommendations with stated limitations.

Candidates must pass the written examination (with a minimum score in every domain) and an examiner-graded practical case.

Intended candidate

  • Analysts with roughly 1–3 years of experience, or Associates who have completed Academy Modules 2–10.
  • Candidates must be comfortable with window functions, DAX or Tableau calculations, pandas and regression interpretation.

Examination blueprint

Domain weights of the written examination and the competencies assessed.

Advanced SQL
20%
Window functions · CTEs and subqueries · Cohorts and period comparisons · Reconciliation
Academy modules: M2
Advanced spreadsheets & Power Query
10%
Power Query merges/appends · Variance analysis · Reconciliation
Academy modules: M3
BI modeling & calculations
15%
Star schema · CALCULATE and filter context · Time intelligence · Tableau LOD
Academy modules: M4, M5
Python analysis
10%
pandas merges, groupby, dates · Cleaning pipelines
Academy modules: M6
Statistics & regression
15%
Confidence intervals · Hypothesis tests / A/B · Regression interpretation
Academy modules: M7
Business & financial analytics
20%
RFM, ABC/XYZ · Campaign and conversion analytics · P&L analysis · Forecasting fundamentals
Academy modules: M8, M9, M10
Documentation & stakeholder communication
10%
Recommendations · Limitations · Documentation
Academy modules: M12, M15

Difficulty distribution: level 2: 20% · level 3: 45% · level 4: 30% · level 5: 5%

Practical assessment

Level II Practical — Telecom Retention Case

Scenario. Orion Telecom's churn has risen. The Commercial Director asks: Who is leaving, why, and what should we do in the next quarter — and how will we know it worked?

Data. The telecom dataset.

You must:

  1. Restate the business requirement as analytical questions and success metrics.
  2. Retrieve and prepare data (SQL and/or Python), documenting validation checks.
  3. Analyse churn by cohort (signup month), contract type, payment method, region, plan and support experience. Use window functions for at least one cohort/retention calculation.
  4. Analyse the lead funnel: conversion by source, time to convert, and campaign contribution.
  5. Quantify at least one relationship statistically (e.g. churn rate difference with a confidence interval, or a regression) and interpret it correctly — including whether it is causal.
  6. Build an executive dashboard (Power BI or Tableau) with no more than 6 visuals.
  7. Write a 2-page memo: findings, 3 prioritised recommendations with estimated impact and cost logic, a measurement plan (e.g. A/B test), and limitations.

Time allowed: 7 days.

Scoring rubric

CriterionWeightExcellentProficientDevelopingInsufficient
Problem framing10%Precise questions, metrics and scope; assumptions explicit.Clear framing with minor gaps.Vague framing.Absent.
Data preparation & validationCRITICAL15%Reconciled counts, documented checks, correct handling of churn timing.Sound with minor gaps.Unvalidated or partially wrong.Incorrect data basis.
Analytical depth25%Cohort retention, segment comparison and funnel analysis correctly executed and connected.Most analyses correct.Shallow or partly incorrect.Missing.
Statistical reasoningCRITICAL15%Correct method, interval/significance interpreted, causal limits stated.Correct with minor interpretation issues.Misinterpreted.Absent or wrong.
Dashboard15%Executive-ready, focused, consistent with analysis.Good with minor issues.Cluttered or unclear.Missing.
Recommendations & communicationCRITICAL20%Prioritised, quantified, feasible, with measurement plan and limitations.Reasonable but under-quantified.Generic.Unsupported.

Levels score 100 / 75 / 40 / 0% of each criterion's weight. Critical criteria must be at least “Proficient”, whatever the total.

Passing rules

  • Written exam: at least 70% overall and at least 55% in every blueprint domain.
  • Practical: at least 70% on the rubric with every critical criterion at least “Proficient”.
  • Critical failure conditions: Recommendation not supported by the candidate's own analysis; Material calculation error that changes the conclusion; No statement of limitations; Plagiarism or undisclosed assistance.
  • Examiner review: Practical case is examiner-graded against the rubric; Examiner may request a short clarification call.
  • Prerequisites: none (Level I is recommended preparation, not required).
  • Retakes: up to 3 candidacies per year; 30 days after an unsuccessful decision.
  • Validity: 36 months, then renewal.

Preparation pathway

  1. Study the modules listed in the blueprint and complete their module exams.
  2. Complete related projects in the Project Lab.
  3. Take the timed mock exam until you are consistently above the pass mark in every domain.
  4. Open a candidacy, sit the official written exam, and submit the practical.