Machine Learning for Finance

Perform advanced financial analysis with algorithms and statistical techniques.

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At a Glance

Professional Education
Open Enrollment
Eight weeks Open Enrollment

Use data-driven analysis to identify relevant financial trends.

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The University of Chicago’s eight-week Machine Learning for Finance course will teach you to collect, organize, and use data to perform advanced financial analysis with algorithms and statistical techniques and tools.

Designed For

Designed for financial professionals who want to develop a career in the present-day financial industry or in an organization’s finance department.

Learning Objectives

Organizations are constantly trying 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 statistics to finance, including the random walk model
  • Understand what Exploratory Data Analysis is and how to perform it with Python and Pandas
  • Engineer new functions using existing data
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What insights are necessary for financial reporting?

Learn how organizations incorporate data-driven analysis to identify financial trends.

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Understand how to use data to perform advanced financial analysis with algorithms and statistical techniques and tools in order to make strategic financial decisions in your organization.

You will learn to:

  • Review statistics and probability and apply basic concepts of statistics to finance 
  • Understand what linear regression is, when to use it, and how to 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

Remote Learning Course Structure

  • Eight weeks in length
  • Weekly, self-paced interactive learning modules and assignments are time-sensitive and should be completed by the set deadlines
  • Synchronous sessions and live question and answer sessions 
  • Mentors will provide continuous support and encourage a dynamic and positive learning environment

Meet Your Instructor

Career Outlook

Traditional financial reporting like profit and loss statements, balance sheets, cash flows, and variance analysis are no longer enough. Today’s businesses need data-based financial analysis to gain deeper insights that will allow them to connect business operations to long-term value, model scenarios in real-time, and allocate resources efficiently. The increasing demand for advanced finance functions such as connecting operational KPIs to financial metrics, along with technological advancements in cloud-based services, has led to the financial analytics market’s current valuation of 6.32 billion. Experts anticipate it will nearly double in size by 2026, with a projected value of 11.02 billion.

$ 72 k

The average annual base pay for a financial analyst in the US

$ 11 b

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

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Offered by The University of Chicago's Professional Education

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