The practice of investment management has been transformed in recent years by computational methods. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language. In this course, we cover the estimation, of risk and return parameters for meaningful portfolio decisions, and also introduce a variety of state-of-the-art portfolio construction techniques that have proven popular in investment management and portfolio construction due to their enhanced robustness.
- 5 stars81.62%
- 4 stars13.25%
- 3 stars3.95%
- 2 stars0.69%
- 1 star0.46%
來自ADVANCED PORTFOLIO CONSTRUCTION AND ANALYSIS WITH PYTHON的熱門評論
This is one the best course to learn how to implement portfolio optimization in real world. Thank you Edhec Risk Institute and Coursera for such a beautiful course.
This course gives a good understanding of Fama-French, GARCH, Black-Litterman and risk parity models among many others, not only theoretically, but also through hands-on Lab sessions.
Very interesting course with a lot practice stuff. A very proficient mentors with strong theoretical background in finance and good Python skills.
Enjoyed the part on the implementation of the Black-Litterman model and the Risk Parity portfolios. Looking forward to the third course.
關於 Investment Management with Python and Machine Learning 專項課程
The Data Science and Machine Learning for Asset Management Specialization has been designed to deliver a broad and comprehensive introduction to modern methods in Investment Management, with a particular emphasis on the use of data science and machine learning techniques to improve investment decisions.By the end of this specialization, you will have acquired the tools required for making sound investment decisions, with an emphasis not only on the foundational theory and underlying concepts, but also on practical applications and implementation. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language through a series of dedicated lab sessions.