This module introduces consumption-based alternative data analysis in finance. It covers aggregating online and offline consumer purchase activity, geolocation data, transaction data, and social media interactions to predict company performance ahead of earnings announcements. The module includes practical demonstrations using real datasets like Uber data and mapping tools.

This module covers text mining methodologies for financial applications, including web scraping, vectorization of text, stop words filtering, and term frequency-inverse document frequency (TF-IDF). It demonstrates how to transform textual data into mathematical representations and derive financial market insights.

This module focuses on analyzing 10-K and 13-F corporate filings using Python. It covers methodologies for quantitative analysis of large documents, automatic data extraction, and metrics definition. The module includes practical applications in analyzing company statement similarities and fund holdings.

This module covers sentiment analysis of textual data and network analysis of firm connectivity in financial contexts. It includes analysis of social media data, corporate filing tone analysis, and network representations of financial relationships. The module demonstrates practical applications using Twitter data and corporate communications.