Machine Learning for Finance
Leverage data-driven analysis to identify relevant financial trends.
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At a Glance
- Enrollment:
- Open Enrollment
- Length:
- 8 weeks
Upcoming Dates
December Start
February Start
Students may register up to 7 days after the course start.
Master finance-applied machine learning.
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.
Designed For
Designed for finance-oriented professionals across industries who want to improve their analytical capabilities, make better-informed decisions, and gain a data-driven competitive edge in their respective fields.
Learning Objectives to Become an Expert in Machine Learning for Finance
Today’s organizations seek to streamline processes, cut costs, and drive profitability. Data has become a key driver in producing better financial analytics, providing leaders with the insights they need to make strategic decisions.
After completing the course, you will be able to:
- Apply basic concepts of probability and statistics 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.
- Earn a credential certifying completion from the University of Chicago and become part of the UChicago network.
Ready to Take Your Career to the Next Level?
Register today and unite your professional practice with our distinctive blend of academic rigor and real-world application.
Enroll NowFinancial Analytics Curriculum
You will learn to:
- Work with linear regression and apply linear regression metrics to a model.
- Make models more rigorous by adding train/test split and cross-validation.
- Backtest a model and understand why backtesting is important.
- Use simulation to solve a portfolio allocation problem.
- Converse at a high level about several advanced topics in financial machine learning.
Methodologies and Techniques:
Pandas
Python
Machine Learning for Finance Online Format Features
- Self-paced interactive learning modules with a variety of engaging learning activities, assignments, and resources.
- Live sessions that bring you, your peers, and your instructor together to learn collaboratively about the current state of the field, engage with real-world problems, and explore authentic solutions.
- Continuous support from your instructional assistant, who will accompany you on your journey through the content, answer your questions, and provide feedback on your work.
Weekly Course Schedule
Gain an introduction to Python, which covers variables, functions, control structures, loops, and Pandas, and learn about probability and statistics, including statistics for finance.
Learn about exploratory data analysis, including the univariate and bivariate models, scatterplots, histograms, and boxplots, as well as regression and regression metrics.
Learn how to conduct train-test splits, cross-validation, overfitting, and regularization. Explore feature engineering and selection, more specifically in terms of transforming independent and dependent variables, before delving into its application to finance, including returns and interest rates.
Define ARIMA modeling, explore stationarity for time series models, metrics, and tests, and use the statsmodels package in Python to build an ARIMA model.
Discover the different types of testing regimes, such as backtesting a time series-ARIMA model, simple rolling pseudo-out-of-sample backtesting, cross-validation backtesting, and backtesting for linear regression. Learn to monitor and troubleshoot models in production.
Understand logistic regression and metrics, encompassing accuracy, precision, and recall, and the confusion matrix. Learn about ensemble methods such as bootstrap aggregation, random forests, boosting, and clustering.
Define risk in finance in terms of no variance in returns, as well as risky and risk-free assets, including expected returns and variance. Learn about resampling and efficient portfolios, such as utility functions, and use Monte Carlo simulation for out-of-sample testing.
Become familiar with cloud computing and industry leaders like Amazon Web Services, Google, and Microsoft. Understand deep learning and neural networks, including back propagation and keras. Learn about bayesian inference with a focus on beyond frequentist statistics and PyMC3.
Earn a Credential in Machine Learning for Finance
After successful completion of this course, participants will receive credentials certified by the University of Chicago including a digital badge to recognize their achievement.
Meet Your Instructor
Our highly trained instructors are courageous thinkers and passionate leaders who leverage years of industry expertise and up-to-date knowledge of terminology, tools, and trends to deliver an unparalleled learning experience. Through their rigorous discourse, cross-disciplinary collaboration, and field-shaping contributions, they create practical solutions and pioneering innovations that enrich our world.
This instructor teaches this course regularly. Please speak to your enrollment advisor if you wish to know who the current teacher is.
Lara Kattan, MPP
Data Science Educator and Curriculum Writer
Lara Kattan is a data scientist, risk professional, and curriculum developer. She is an adjunct assistant professor at the University of Chicago Booth School of Business and develops data science curriculums for several learning platforms. Previously, she was a consultant at McKinsey and EY.
Kattan...
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.
The average annual base pay for a financial analyst in the United States.
The anticipated value of the financial analytics market by 2030.
The projected CAGR of the financial analytics industry from 2022 to 2030.
Potential Job Titles for Professionals with Skills in Machine Learning for Finance
- Accountant
- Asset/Wealth Manager
- Business Owner
- CFO
- Commercial Banker
- Economist
- Finance Manager
- Financial Advisor
- Financial Analyst
- Investment Banker
How Do I Get Started?
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Complete the form on the registration page.
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Pay the tuition fee through our secure gateway.
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Receive a welcome email with your login information for the virtual campus.
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Gain access to the course content prior to the start date.
Offered by The University of Chicago's Professional Education
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