About the Course
The University of Chicago’s eight-week Machine Learning for Finance course focuses on collecting, organizing, and using data to perform advanced financial analysis with algorithms and statistical techniques and tools. You will engage with real-world case studies and examples, allowing you to apply the theory you will learn to financial models.
After completing the course, you will be able to:
- Apply basic concepts of statistics and probability to finance.
- Understand what exploratory data analysis is and how to perform it with Python and Pandas.
- Engineer new features and functions from existing data.
- Comprehend how unsupervised machine learning models work and when they can be useful.
- Use simulation to solve portfolio risk and allocation problems and answer financial questions.
Participants who successfully complete the course will receive credentials certifying completion from the University of Chicago, including a digital badge, and become part of the UChicago network.
Methodologies and Techniques:
- Pandas
- Python
Career Outlook
Today’s businesses need data-based financial analysis to gain deeper insights that will enable them to connect operations to long-term value, model scenarios in real time, and allocate resources efficiently. The increasing demand for advanced finance functions and technological advancements in cloud-based services have led to the financial analytics market’s significant growth.
43
bThe anticipated value of the financial analytics market by 2030.
Source: Global Market Insights
15
%The projected CAGR of the financial analytics industry from 2022 to 2030.
Source: Global Market Insights
- Accountant
- Asset/Wealth Manager
- CFO
- Commercial Banker
- Economist
- Finance Manager
- Financial Advisor
- Financial Analyst
- Investment Banker
Meet Your Instructor
These instructors teach this course regularly. Please speak to your enrollment advisor if you wish to know who the current teacher is.

Sourav Ghosh, MSc
Vice President at an Investment Bank
Sourav Ghosh has worked in high-frequency algorithmic trading firms for over a decade. Along with fintech, his areas of interest include probability theory and stochastic processes, statistical learning and inference, and natural language processing.

Lara Kattan, MPP
Data Science Educator and Curriculum Writer
Lara Kattan develops curriculum for institutions like the University of Chicago and data science learning start-ups. A former consultant in risk practice at McKinsey, she has an MA in public policy with a concentration in econometrics from UChicago.

Do you have questions?
The University of Chicago Approach to Online Learning
Our online learning programs are crafted with your specific needs in mind. Programs combine e-learning with live, interactive sessions to strengthen your skill set while maximizing your time. We couple academic theory and business knowledge with practical, real-world application. Through online learning sessions, you will have an opportunity to grow your professional network and interact with University of Chicago instructors and your classmates.
What Our Participants Say

"This course is valuable for gaining further knowledge of ML content for finance. It enhanced my understanding of application principles and increased my confidence in my ability to make decisions related to problems that can be solved by applying the concepts covered."
Derrick Walker

"This course helped me refresh concepts, learn about new use cases, and bolster my capabilities as a leader in a company where we create AI solutions."
Luis Sepúlveda H.
CEO, Alaya Digital Solutionsi