Five artifacts to build the week of July 6–12, 2026. Each: what it is, what to produce, and what “done” looks like.
Thu 7/9 · ~1 hour
1. Excel Workbook Scrub
A recruiter-viewable copy of a real workbook you already use.
Steps
Copy your current pipeline-tracking / financial-reconciliation workbook (the one you use for SST reporting).
Replace every real name, address, phone, email, and MLS number with fakes (Client A, 123 Main St). Keep dollar amounts realistic but round/shift them so no real transaction is identifiable.
Keep the formulas, conditional formatting, lookups, and pivot tables intact — those are the artifact.
Add a one-paragraph “About this workbook” note on the first tab: what it tracks, what decisions it feeds.
Done when: a stranger could open it, see structure and formulas, and learn nothing about any real client.
Fri 7/10 · ~1 evening, one page
2. Data Quality Case Study
A written story proving you do data validation for real. A document, not code.
Structure (use these exact headings)
The Problem: one real incident where incoming client data broke (or nearly broke) something in Closing Day.
Detection: how you caught it — what check, report, or anomaly flagged it.
Root Cause: what was actually wrong and how you traced it.
The Fix: what you changed, including the validation rule/process that now catches it automatically.
Result: what has not happened since, stated plainly.
Rules
400–600 words, no jargon inflation, anonymize the customer. Write it like you’d tell it out loud.
Done when: you could read it aloud in an interview as the answer to “tell me about a time you found a data quality issue.”
Sat 7/11 · ~half a day
3. Power BI Dashboard
One dashboard built in Power BI Desktop (free) from Closing Day data.
Steps
Export Closing Day pipeline data to CSV: deals with stage, dollar value, probability score, created date, close date, agent, activity counts. Anonymize names on export.
Import to Power BI Desktop. Build a simple model (deals table + agents table is enough for a relationship).
Table: deals with conditional formatting on probability score
Add one slicer (by agent or by stage) so it’s interactive.
Save the .pbix and take 2–3 clean screenshots.
Scope warning: four visuals, one page, stop. Done when: you can open the .pbix, click the slicer, and explain each visual in one sentence.
Sun 7/12 start · your study reps, documented
4. SQL Analysis Doc
A doc of 15–20 queries against the Closing Day database, each answering a business question.
Format for every entry
Question in plain English (“Which stage loses the most deals?”)
The query (formatted SQL)
Result (small table or 1–2 lines)
So what (one sentence of business meaning)
Coverage checklist
Basic SELECT + WHERE filters (2–3)
JOINs across at least two tables (3–4)
GROUP BY with COUNT/SUM/AVG (3–4)
At least one subquery, one CTE (WITH), one window function, one date-based query
Done when: 15+ entries complete and you can rewrite any from memory. This doc IS your SQL study plan — do 2–3 per day.
Sun 7/12 · ~1 evening once 1–4 exist in draft
5. Data Portfolio Page
One page (e.g. aiopsforrealestate.com/data) a recruiter reaches in one click.
Page structure, top to bottom
Two-sentence intro: who you are, what this page proves. Data-analyst framing, not AI-ops.
Closing Day — two sentences + link to closingday.info. Frame as “production platform where I own the full data lifecycle.”
Power BI Dashboard — screenshots + three-sentence description + downloadable .pbix if hosting allows.
SQL Analysis — link to the doc (page section or linked PDF).
Data Quality Case Study — full text on the page (it’s short).
Excel Workbook — download link + one-line description.
Contact line: email, LinkedIn, phone.
Then link it from: LinkedIn Featured + About, résumé header, Indeed & ZipRecruiter summaries. Done when: one URL shows all four artifacts and your contact info.