• Covers methods of managing data and extracting insights from real-world financial sources. Topics include machine learning, natural language processing, predictive analytics, regression methods, and time series analysis. Applications include algorithmic trading, portfolio risk management, high-frequency market microstructure, and option pricing. Studies major sources of financial data, raw data cleaning, data visualization, and data architecture. Provides instruction in tools used in the financial industry to process massive data sets, including SQL, relational and multidimensional databases. Emphasizes computer implementations throughout.

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