Statistics for Machine Learning for Investment Professionals
Examine modeling frameworks and get familiar with Python and R languages to improve investment decision making.
About This Course
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
This course is part of the Data Science for Investment Professionals Certificate.
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.
- 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
- Quantitative Methods
Machine Learning for Investment Professionals
Gain an understanding of machine learning along with the technical and soft skills needed to use it in the investment process.
Natural Language Processing for Investment Professionals
Learn to leverage NLP in sentiment analysis for use in investment valuation models and the decision-making process.
Data Science for Investment Professionals Certificate
Gain practical understanding of data science and machine learning applications in the investment process.