Discover the power of alternative data in financial markets with the Python and Machine-Learning for Asset Management with Alternative Data Sets course. Gain insights into consumption data, textual analysis, processing corporate filings, and using media-derived data through hands-on lab sessions and real-world applications.
This course is designed for individuals with a background in Python programming, investment theory, and statistics, and is ideal for those aspiring to pursue a career as a data scientist in financial markets or looking to strengthen their analytics skillset for the financial industry.
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This course encompasses modules on consumption data, textual analysis for financial applications, processing corporate filings, and using media-derived data for predicting financial market variables. Each module provides practical lab sessions and real-world applications, enabling participants to gain hands-on experience in using alternative data for asset management.
Explore the fundamentals of consumption data, including geolocation, foot-traffic, and the use of real-world datasets like Uber. Gain insights into consumption-based proxies for private information and managers' behavior, and learn to analyze data biases and perform relevant code and data operations.
Delve into the applications of textual analysis for financial purposes, with a focus on web scraping, textual data processing, and similarity analysis on corporate filings to predict returns. Learn about processing text into vectors, normalizing textual data, and web scraping techniques.
Discover the intricacies of processing corporate filings, including working with 10-K and 13-F data, risk analysis, and measuring home bias to predict returns. Gain hands-on experience in utilizing TF-IDF, network centrality, and competition links to analyze stock returns.
Uncover the potential of using media-derived data for predicting financial market variables, including sentiment analysis, network visualization, and replicating PageRank. Explore the application of media information to predict financial market variables and gain insights into network analysis techniques.
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