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

Over-utilization in market and accounting data has resulted in portfolio crowding, poor performance, and systemic risk. Financial institutions are now looking to gain an advantage to adopt alternative data to replace traditional data. This course covers the fundamental concepts of alternative data and the most recent research. It also includes practical examples and real-world applications. This course has a unique approach. While the theoretical content is important, there are practical lab sessions and examples of using alternative datasets. This course is for you if your goal is to become a data scientist in financial market, or to improve your analytical skills. You will need to have some background in Python programming, Investment theory, and statistics. This course will allow you to acquire new data and research methods that can be applied to financial markets. It will also help you improve your data science and Python skills.