An engineered pathway taking you from basic algebraic principles to professional statistical modeling and big data systems.
Querying tables, database schema design, and filtering data from relational systems.
Reading data, selection indices, handling arrays, and manipulating tabular structures.
Charting, plotting with Matplotlib/Seaborn, correlation patterns, and report creation.
Probability distributions, confidence intervals, A/B testing, and p-values.
Handling missing values, scaling features, encoding categories, and dimensional reduction.
Regression models (linear, polynomial) and classification models (logistic, KNN).
Decision trees, random forests, gradient boosting (XGBoost), and model metrics.
K-Means clustering, PCA, hierarchical clustering, and structural detection.
Building interactive reports in PowerBI / Tableau and designing custom KPIs.
Queries on large-scale datasets, MapReduce concepts, and cloud cluster setups.
Presenting end-to-end analytical models and dashboards directly to active industry sponsors.