Ementorhub

What you'll learn

Gain an intuitive understanding for the underlying theory behind Modern Portfolio Construction Techniques

Write custom Python code to estimate risk and return parameters

Utilize powerful Python optimization libraries to build scientifically and systematically diversified portfolios

Build custom utilities in Python to test and compare portfolio strategies

Skills you'll gain

Financial Modeling

Risk Management

Python Programming

Investment Management

Asset Management

Portfolio Management

Matplotlib

NumPy

Probability Distribution

Risk Analysis

Pandas (Python Package)

Financial Analysis

Simulations

Data Manipulation

There are 4 modules in this course

This course is the first in a four course specialization in Data Science and Machine Learning in Asset Management but can be taken independently. In this course, we cover the basics of Investment Science, and we'll build practical implementations of each of the concepts along the way. We'll start with the very basics of risk and return and quickly progress to cover a range of topics including several Nobel Prize winning concepts. We'll cover some of the most popular practical techniques in modern, state of the art investment management and portfolio construction. As we cover the theory and math in lecture videos, we'll also implement the concepts in Python, and you'll be able to code along with us so that you have a deep and practical understanding of how those methods work. By the time you are done, not only will you have a foundational understanding of modern computational methods in investment management, you'll have practical mastery in the implementation of those methods.

An Introduction to Portfolio Optimization

Beyond Diversification

Introduction to Asset-Liability Management