OS-DAA · LEVEL I

OrionStats Data Analyst Associate

Level I — foundational, junior-level analyst competence

The Associate certificate confirms that a candidate can perform the everyday work of a junior data analyst under normal supervision: retrieve data with SQL, prepare it in a spreadsheet or Python, calculate correct business metrics, build a clear basic dashboard and explain what the numbers mean.

It is not awarded for course completion. Candidates must pass an independent written examination and a practical assessment reviewed by an OrionStats examiner.

Intended candidate

Career starters, graduates and career changers who have completed foundational study (Academy Modules 1–3, basics of 4–8 or equivalent experience).
Professionals in Finance, Sales, Marketing, HR or Operations who already work with spreadsheets and want to prove structured analytical skill.

Examination blueprint

Domain weights of the written examination and the competencies assessed.

Data retrieval with SQL

25%

SELECT, filtering, sorting · Aggregation with GROUP BY / HAVING · INNER and LEFT joins · CASE WHEN and NULL handling

Academy modules: M2

Spreadsheets

20%

SUMIFS / COUNTIFS / XLOOKUP · PivotTables · Data cleaning and validation

Academy modules: M3

Business intelligence basics

15%

Import and shape data · Simple measures · Basic dashboard layout

Academy modules: M4, M5

Python fundamentals

10%

Core syntax · pandas loading, filtering, grouping

Academy modules: M6

Descriptive statistics

10%

Mean/median/spread · Percentiles and outliers

Academy modules: M7

Business analytics & KPIs

15%

KPI definitions · Plan vs fact · Conversion and growth rates

Academy modules: M1, M8

Analytical thinking & communication

Translating questions · Stating findings clearly

Academy modules: M1, M15

Difficulty distribution: level 1: 25% · level 2: 40% · level 3: 30% · level 4: 5%

Practical assessment

Level I Practical — Retail Monthly Performance Pack

Scenario. The Head of Retail at Orion Retail wants a one-page view of 2025 performance by store and category, and answers to five questions before Monday's management meeting.

Data. The retail dataset (download from Datasets or query it in the SQL Lab).

Tasks.

Write SQL that returns 2025 net revenue (completed orders only, after discounts), order count and average order value by store and month. Paste the SQL.
Clean customers_raw: remove exact duplicates, standardise city names, convert all signup dates to ISO format. Document each step and the row counts before/after.
Calculate for each store: 2025 revenue vs sales_targets (absolute and % variance).
Build a basic dashboard in Power BI or Tableau (your declared primary BI tool) showing revenue trend, revenue by store vs target, and top 10 products. Submit a PDF/PNG export and the file link.
Answer in writing (≤ 300 words): Which store most missed its plan in 2025 and what in the data might explain it? Which category grew fastest from 2024 to 2025?

Time allowed: 72 hours from start. Submission: SQL, cleaning log, workbook/notebook, dashboard export, written answers.

Scoring rubric

Criterion	Weight	Excellent	Proficient	Developing	Insufficient
SQL correctnessCRITICAL	25%	Correct filters (completed only), discount applied, correct grain; readable and commented.	Correct result with minor style issues.	Partially correct (e.g. includes cancelled orders or ignores discounts).	Incorrect or missing.
Data cleaning	15%	All issues found and fixed; steps reproducible and row counts reconciled.	Main issues fixed; log mostly complete.	Some issues remain or log incomplete.	Not attempted / data still dirty.
Calculations	20%	Variance and % variance correct with sign convention explained.	Correct with small presentation issues.	Errors in some stores.	Incorrect method.
Dashboard	20%	Clear hierarchy, correct visuals, labelled units, consistent with SQL figures.	Clear and correct, minor design issues.	Hard to read or inconsistent in places.	Missing or wrong.
Interpretation & communication	20%	Direct answers backed by figures; sensible caveats.	Answers correct but thinly supported.	Vague or partially wrong.	Missing or 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 50% in every blueprint domain.
Practical: at least 70% on the rubric with every critical criterion at least “Proficient”.
Critical failure conditions: Any practical deliverable missing; Numbers in the dashboard that contradict the candidate's own SQL output; Evidence of copied work or undisclosed assistance.
Examiner review: Practical assessment is always examiner-reviewed; Examiner confirms identity name before issuance.
Prerequisites: none (Level I is recommended preparation, not required).
Retakes: up to 4 candidacies per year; 14 days after an unsuccessful decision.
Validity: no expiry.

Preparation pathway

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