I created a stakeholder map and role summary for the case manager in a mental healthcare setting.

I mapped coordination failures after psychiatric discharge and performed root cause analyses (Ishikawa and 5 Whys) on missed first follow-up appointments.

I chose a county-level public dataset and committed to a specific outcome–SDOH pairing for analysis.

I mapped the County Health Rankings 2025 file, documented its structure, and prepared a county-level analytic subset for analysis.

I selected and committed a paired dataset linking California county asthma ED visit rates (2015–2023) with EPA annual PM2.5 and ozone summaries, then documented join method and data limitations.

I mapped and documented two public datasets, cleaned and joined them to county-year, and prepared an analysis-ready file for a question about PM2.5 and pediatric asthma ED rates.

I ran a linear regression with interaction and year indicators, inspected diagnostics, and used AI to critique and refine my interpretation of the model output.

I produced a revised interpretation and stress-test documenting limits in my ARCeH externship finding on county-level PM2.5 and children's asthma ED visit rates.

I documented a structured orientation to the CIYJ dataset and produced exploratory notes summarizing file contents, row counts, units of analysis, and codebook coverage.

I wrote and defended a focused, testable research question linking youth fear to recorded assault rates in the CIYJ dataset, and documented preprocessing and validation for the merged facility-period dataset.

I ran a Spearman rank correlation testing whether facility-level fear rates and assault rates moved together.

I revised my Project 3 claim into a defensible, scoped finding about facility-level fear and recorded assaults.

I mapped the workforce structure at an Amazon Fulfillment Center, compiling roles, responsibilities, tools, and interaction partners into a single reference spreadsheet.

I cleaned and exported a Glassdoor-derived dataset of Amazon fulfillment-center reviews, documenting columns removed, data issues, and decisions to retain IDs and flags for analysis traceability.

I cleaned, preserved, and documented text fields from Glassdoor reviews to enable keyword analysis while keeping originals for sentiment validation.

I built a keyword-tagged theme dataset and quantified how themes related to ratings, then shared the analysis and code used to tag each review.

I mapped my personal online purchase journey for a recent trip, documenting stages, emotions, and persuasive touchpoints that influenced each booking decision.

I analyzed an exported referral dataset, calculated weighted metrics, and summarized channel-level performance and data quality flags in a shared Google Sheet.

I wrote a data-driven story comparing Paid Search and Direct visitors using GA4 exports, and recommended tagging and landing-page fixes.

I audited paid ad datasets, identified measurement gaps, and recommended concrete reallocations and fixes to improve campaign evaluation and spend efficiency.

I rebuilt the Shopify sales story and recommended UX and marketing changes to reduce checkout abandonment and raise average order value.

I built a seven-tab Claude dashboard combining Google Ads, Meta, Analytics and Shopify data to surface spend, traffic quality, orders and product signals.

I documented week‑1 findings and started a living Holiday Game Plan used to track data inventory, SQL queries, and prompts for Claude.

I designed and populated a dim_product table, cleaned product identifiers, and connected product records across four source tables to enable unified joins.

I built a Q4 ad allocation plan tying Meta campaign spend to on-site revenue and recommending which campaigns to scale, pause, or cut.

I analyzed checkout logs, identified bot-like sessions, and recalculated cart abandonment after filtering noise.