Online Course

Statistics for Machine Learning for Investment Professionals

Examine modeling frameworks and get familiar with Python and R languages to improve investment decision making.

USD 339.00

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.

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

Digital Badge

Receive a digital badge upon finishing the course.
Study time

12–15 hours to complete
12-month access to learning materials
Online self-paced

Everything can be completed online at your own pace.
10 PL credits

Members can claim credits toward professional learning goals
Who should enroll

Ideal for those with some background in finance and investments.