Data-Driven Financial Analysis

Perform advanced financial analysis with algorithms and statistical techniques.

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At a Glance

Type
Professional Education
Enrollment
Open Enrollment
Duration
Eight weeks Open Enrollment
Format
Remote
Cost

Use data-driven analysis to identify relevant financial trends.

The University of Chicago’s eight-week Data-Driven Financial Analysis 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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Curriculum

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

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Offered by The University of Chicago's Professional Education

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Business and Management

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