📊 Data

You receive a dataset with 50,000 rows. Before any analysis, you identify outliers: values more than 3 standard deviations from the mean. Z-scores and IQR are the tools that separate real signals from noise.

Stakeholders want to know which customers are most likely to churn. You run a regression analysis, interpret the coefficients, and explain which variables have the strongest relationship with churn. Linear regression is the mathematical engine.

The product team launches a new feature to 5% of users. After two weeks, you determine whether the engagement lift is statistically significant or happened by chance. A z-test or binomial probability calculation gives you a defensible answer.

You summarize weekly active users: mean, median, standard deviation, and week-over-week percent change. These numbers tell the real story faster than a dashboard full of unlabeled charts.

You present a churn model that is 82% accurate. A stakeholder asks what that means in plain English. You walk through precision, recall, and confidence intervals, translating statistical math into terms the business can actually use.

Explores and summarizes data to answer business questions.

Builds predictive models and tests hypotheses using advanced techniques.

Creates dashboards and performance reports from structured data.

Builds and maintains the infrastructure that moves and transforms data.

Deploys and optimizes ML models at scale in production environments.