Build practical AI, machine learning, and quantitative finance skills for modern investing and trading.
Learn to analyze financial data, forecast markets, evaluate AI trading strategies, and extract investment signals from sentiment and alternative data.
This Specialization combines AI-driven investing, financial time-series analysis, quantitative trading, financial NLP, sentiment intelligence, and generative AI. You'll examine machine-learning models, feature engineering, ARIMA and nonlinear forecasting, stochastic processes, cointegration, PCA, neural networks, transformers, LLMs, and alternative financial data.
You'll also evaluate algorithmic strategies using realistic backtesting, transaction costs, liquidity, slippage, volatility, drawdown, leverage, and out-of-sample testing. Explore how news, earnings calls, market narratives, social signals, and multimodal data influence financial decisions.
By completing the Specialization, you'll be able to evaluate AI-enabled investment workflows, interpret quantitative and sentiment signals, assess trading strategies, manage model and execution risks, and apply responsible AI practices to financial decision-making.
Applied Learning Project
Learners will work on practical financial analysis and trading projects involving machine-learning models, financial time-series forecasting, sentiment analysis, and quantitative strategy evaluation. They will analyze market and alternative data, interpret investment signals, assess trading performance through backtesting, and apply risk management techniques to address realistic investment and trading challenges.
