Build practical quantitative finance and financial analytics skills using Python, SAS, Excel, predictive modeling, and Bayesian statistics.
Transform financial data into forecasts, interpretable models, and evidence-based decisions.
This Specialization provides an applied pathway for analyzing financial and economic data across multiple analytical tools. You will prepare datasets, perform descriptive and inferential analysis, examine correlations, build regression models, evaluate variance, and interpret financial time series.
You will use Python to manage financial data, analyze trends, develop visualizations, and communicate decision-ready insights. With SAS, you will apply statistical procedures to economic data, financial markets, exchange rates, and forecasting scenarios.
The Specialization also develops predictive modeling skills through CART classification models for term deposit investment decisions. You will construct decision trees, tune model parameters, apply pruning techniques, reduce overfitting, and validate performance on unseen data.
Finally, you will apply Bayesian inference, MCMC sampling, PyMC, hierarchical models, and A/B testing methods to evaluate uncertainty and update predictions as new evidence becomes available. By completion, you will be prepared to support data-driven decisions in finance, banking, investment analysis, business analytics, and predictive modeling.
Applied Learning Project
Learners will complete applied projects using financial and economic datasets to analyze trends, visualize insights, test statistical relationships, and forecast outcomes. They will also build, tune, and validate predictive and Bayesian models for authentic investment, financial marketing, and decision-making scenarios.
