Status: planned. This page intentionally does not present Python, Snowflake, dbt, Git/GitHub, or statistical analysis as professional production experience. The project will be updated as the lab is actually built.
Planned architecture
Synthetic / public CSV, JSON, or REST API data
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Python + pandas ingestion, cleaning, validation
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Snowflake RAW
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dbt STAGING
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dbt INTERMEDIATE / business logic
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dbt DIMENSIONAL MARTS
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Power BI reporting
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Python statistical analysis / business recommendations
What I will demonstrate
- Python and pandas for file/API ingestion, JSON handling, data cleaning, reconciliation, and validation.
- Snowflake warehouse organization across raw, staging, intermediate, and analytical layers.
- dbt models, tests, documentation, lineage, and reusable dimensional marts.
- Git/GitHub version control for the project code and development workflow.
- Power BI reporting built from curated analytical models.
- Practical statistical analysis such as confidence intervals, hypothesis testing, A/B-style comparisons, regression fundamentals, and anomaly analysis.
Evidence to be published
GitHub code, Python scripts, architecture notes, dbt documentation screenshots, model lineage, data-quality test results, Power BI evidence, statistical analysis, and a walkthrough of key business and design decisions.
Status
Planned / learning
Tools
Python/pandas, Snowflake, dbt, SQL, Git/GitHub, Power BI, statistics
Data
Synthetic / public only
Goal
End-to-end modern analytics proof