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From Raw Data to Decision-Making: Building an End-to-End Analytics Pipeline
In this project, I built a complete analytics workflow — from raw data storage in the cloud to interactive dashboards — using GCP, BigQuery, dbt, and Power BI. The goal was to demonstrate not only how to visualize data, but how to model, validate, and organize it properly along the way. This post walks through…
A Complete Guide to A/B Testing in Python: p-Values, Z-Tests, and Business Metrics
Oftentimes, when we compare two versions of a product, it’s difficult to tell whether the changes made in the alternative version are actually producing the expected results or if the differences we see could simply be due to chance. This is where statistical significance comes in; it represents the idea of measuring how likely it…
A/B Testing Explained: Metrics, Methods, and Why It Matters
A/B tests are designed to answer targeted business questions with evidence instead of guesswork. Once a problem is detected (such as a drop in sales) the team proposes hypotheses to explain what might be driving that change. From there, a second version of the product or feature is created, ideally with small and controlled adjustments.…
A/B Testing AI AI Basics Artificial Intelligence BigQuery Classification Classification Algorithm Cross-validation Dashboard Data Analytics Data Science Data Visualization dbt Deep Learning Exploration Rate Fundamentals GCP K-Means Machine Learning ML for Beginners Model-Free Learning Overfitting Power BI Python Q-Learning Regularization Reinforcement Learning Relational Database SQL Support Vector Machine SVM UX Research
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The Data Viewfinder 