Descriptive, predictive, and prescriptive analytics paradigms.

GDPR compliance, data security, and avoiding algorithmic bias.

Supervised, unsupervised, and reinforcement learning overview.

Monte Carlo simulations for financial and operational uncertainty.

Linear programming, sensitivity analysis, and resource assignment.

Seasonality, trend analysis, ARIMA models, and demand prediction.

Logistic regression, decision trees, and ROC curves.

Simple and multiple regression modeling for revenue prediction.

Executing statistical tests on enterprise datasets.

A/B testing, t-tests, ANOVA, and p-value evaluation.

Normal distribution, sampling, and confidence intervals.

Principles of visual design using PowerBI and Tableau dashboards.

Data cleaning, outlier detection, and summary statistics.

Databases, data warehouses, data lakes, and ETL pipelines.

Submitting a complete data-driven analytics report.