The practice of investment management has been transformed in recent years by computational methods. This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. However, 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.
- 5 stars85.33%
- 4 stars12.72%
- 3 stars1.13%
- 2 stars0.40%
- 1 star0.40%
INTRODUCTION TO PORTFOLIO CONSTRUCTION AND ANALYSIS WITH PYTHON からの人気レビュー
Highly recomand you to type all the codes in Lab-Session, after this course you will improve both of your programming skills and theoretical fundament in investment management.
Vijay simplifies learning whereas others make the simple concept so complicated. Vijay outshines others in this specialization. Others are subpar. especially the princeton guy
A well-balanced course between theory, applications and coding. If you are an intermediate finance student that is looking for a practical toolkit with python, this is the right course
Enjoyable course. One has to be conversant with basic Phyton to follow this course. What I learnt the most is the ability to use Phyton coding to demonstrate the concept of portfolio investment.
Investment Management with Python and Machine Learning専門講座について
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.