Investment Management with Python and Machine Learning Specialisation
Written on 16 November 2019.
Gideon Ozik, EDHEC PhD (2011) and Vijay Vaidyanathan, EDHEC PhD (2012) are contributing as instructors in a series of MOOCs offered by EDHEC Business School, EDHEC-Risk Institute Executive Education and Coursera.
The specialisation in Data Science and Machine Learning for Asset Management 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.
The two courses “Introduction to Portfolio Construction and Analysis with Python”, and “Advanced Portfolio Construction and Analysis with Python” are taught by Dr Vijay Vaidyanathan, CEO of Optimal Asset Management Inc. together with EDHEC-Risk Institute Director, Professor Lionel Martellini
The first course provides an introduction to the underlying science, with the aim of giving a thorough understanding of that scientific basis; the secondone covers the estimation, of risk and return parameters for meaningful portfolio decisions, and also introduces 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.
Another course “Python and Machine-Learning for Asset Management with Alternative Data Sets” is taught by Dr Gideon Ozik, Founder and managing partner of MKT MediaStats, and Sean McOwen, Quantitative Analyst; it introduces the core concepts around alternative data, the most recent research in this area, as well as practical portfolio examples and actual applications.
More information on this specialisation can be found at: https://www.coursera.org/specializations/investment-management-python-ma...
Before starting these courses, a free & basic introduction to Python code prepared by Vijay Vaidyanathan is available on the EDHEC-Risk Institute website at: https://risk.edhec.edu/python-code-lab-session