This course introduces learners to foundational statistical concepts underpinning machine learning as well as advanced AI techniques used in the investment profession. Demonstrating core modeling frameworks along with carefully selected real-world investment practice examples, the course seeks to familiarize learners with two important programming languages — Python and R (no prior knowledge of Python or R necessary). The motivation is to demonstrate the elegance — and speed — simple programming brings to the investment decision-making process.
During this online course, you will have the opportunity to gain practical experience through immersive code labs that draw on real-world scenarios.
Course modules include:
- Module 1: Data and Patterns
- Module 2: Randomness and Probability
- Module 3: Linear Regression
- Module 4: Introduction to Advanced Regression Concepts
- Module 5: Introduction to Time Series Analysis
Prerequisites
This course is part of a data science series. It is suggested, but not required, that learners complete the courses in the recommended order below to ensure uniform foundational knowledge.
Recommended order:
- Data and Statistics Foundation for Investment Professionals
- Statistics for Machine Learning for Investment Professionals
- Machine Learning for Investment Professionals
- Natural Language Processing for Investment Professionals
- Mitigating Biases in the Data Science Pipeline for Investment Professionals
What You’ll Learn
Upon completion of this online course you will be able to:
- Describe the importance of identifying information patterns for building models
- Explain the probability concepts for solving investing problems
- Explain the use of linear regression and interpret related Python and R code
- Describe gradient descent, explain logistic regression, and interpret Python and R code
- Describe the characteristics and uses of time-series models
Topics Covered
- Fintech
- Quantitative Methods
