Python and Machine-Learning for Asset Management with Alternative Data Sets

Over-utilization of market and accounting data over the last few decades has lead to portfolio crowding, mediocre performance and systemic risks, incentivizing financial institutions which are looking for an edge to quickly adopt alternative data as substitute to traditional data. This course introduces the core concepts around alternative data, the most recent research in this area, as well as practical portfolio examples and actual applications. The approach of this course is somewhat unique because while the theory covered is still a main component, practical lab sessions and examples of working with alternative datasets are also key. As a result, participants will learn new data and research techniques applied to the financial markets while strengthening data science and python skills. This course covers a broad array of alternative data types including consumption based datasets, corporate filings, social media, and various datasets scraped directly from the web. The course teaches tools such as analytics, text mining, sentiment analysis, and data visualization required to process the data in order to gain insight for financial market applications.