Theory and Practice in Python, R and Excel


Risk Modeling, Optimization, Factor Analysis, & Regression in Python, R & Excel

Mode Of Learning: Online

Access Duration: 365 days



This course lies at the intersection of four areas: math, finance, computer science, and business. Over this enormous course, you’ll cover risk modeling, factor analysis, numerical optimization, and linear and logistic regression by looking at real financial models and examples.


What’s Inside

  • Access 130 lectures & 14.5 hours of content 24/7
  • Model risk using covariance matrices & historical returns
  • Calculate Value-at-Risk & understand the implications, strengths, & weaknesses of this approach
  • Understand principal components, Eigen values, Eigen vectors, & Eigenvalue decomposition
  • Apply PCA to explain the returns of a tech stock like Apple
  • Understand the classic linear programming problem setup & the primal & dual problems
  • Explore the method of least squares
  • Implement multiple regression in Excel, R, & Python
  • Discover applications of logistic regression, as well as the link to linear regression & machine learning



Experience level required: all levels