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.
What's inside
Four courses
Introduction to Portfolio Construction and Analysis with Python
(0 hours)
Introduction to Portfolio Construction and Analysis with Python
(0 hours)
Advanced Portfolio Construction and Analysis with Python
(0 hours)
Advanced Portfolio Construction and Analysis with Python
(0 hours)
Python and Machine Learning for Asset Management
(0 hours)
Python and Machine Learning for Asset Management
(0 hours)
Python and Machine-Learning for Asset Management with Alternative Data Sets
(0 hours)
Python and Machine-Learning for Asset Management with Alternative Data Sets
(0 hours)Learning objectives
- Write custom python code and use existing python libraries to build and analyse efficient portfolio strategies.
- Write custom python code and use existing python libraries to estimate risk and return parameters, and build better diversified portfolios.
- Learn the principles of supervised and unsupervised machine learning techniques to financial data sets
- Gain an understanding of advanced data analytics methodologies, and quantitative modelling applied to alternative data in investment decisions
- Write custom python code and use existing python libraries to build and analyse efficient portfolio strategies.
- Write custom python code and use existing python libraries to estimate risk and return parameters, and build better diversified portfolios.
- Learn the principles of supervised and unsupervised machine learning techniques to financial data sets
- Gain an understanding of advanced data analytics methodologies, and quantitative modelling applied to alternative data in investment decisions
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