Machine Learning for Finance
AI and Data Science introduce new tools that will ensure your business is future-fit in this digital age. Learn about the opportunities that AI and data science present.
- Start Date: December 5, 2022
- Online learning with live, interactive sessions
- Tuition: USD $2,500
- English, Spanish, and Portuguese
- 4.6 Continuing Education Units (CEUs)
Sign up now with early-enrollment pricing—10% tuition reduction available only until October 25, 2022
About the Course
Our 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. During the course, you will have an opportunity to work through real-life case studies and examples, giving you an opportunity to apply theory to financial models.
You will learn to:
- Review statistics, probability, and apply basic concepts of statistics to finance.
- Understand linear regression, when to use it, as well as how to apply linear regression metrics to a model.
- Make models more rigorous by adding things like train/test split and cross-validation.
- Backtest a model and understand why this is particularly important for finance.
- Use simulation to solve a portfolio allocation problem.
- Converse at a high level about several advanced topics in financial machine learning.
You will be able to:
- Understand and review statistics and probability.
- Perform Exploratory Data Analysis in Python/Pandas.
- Monitor a model for performance.
- Define risk in finance.
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Career Outlook
$
72
k
The average annual base pay for a financial analyst in the US
$11B
The anticipated size of the financial analytics market by 2026
11.2%
The projected CAGR of the industry from 2021 to 2026
Potential Job Titles in Financial Analytics
- Accountant
- Asset/Wealth Manager
- CFO
- Commercial Banker
- Economist
- Finance Manager
- Financial Advisor
- Financial Analyst
- Investment Banker
Meet Your Instructors
Lara Kattan, MPPMachine Learning for Finance Read More
Read more
Lara Kattan is a data science educator and curriculum writer. She is currently developing curriculum for institutions such as the University of Chicago and data science learning startups. Prior to embarking on a career in curriculum development, she was a consultant in risk practice at McKinsey & Co. She has an MA in public policy with a concentration in econometrics from the University of Chicago and a BA in economics and political science from Northwestern. Kattan is a lifelong learner and is pursuing an MA in computer science and another in mathematics from DePaul University.
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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.
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