Excel is a programming language in and of itself. In this power-packed course, we use Excel and VBA (Visual Basic for Apps) for some serious financial number-crunching.
1. Risk Modeling
2. Factor Analysis
3. Numerical Optimization
4. Linear and Logistic Regression
A financial portfolio is almost always modeled as the sum of correlated random variables. Measuring the risk of this portfolio accurately is important for all kinds of applications: the financial crisis of 2007, the failure of the famous hedge fund LTCM and many other mishaps are attributable to poor risk modeling.
In this course, we cover the theory and practice of robust risk modeling:
Factor analysis helps to cut through the clutter when you have a lot of correlated variables to explain a single effect.
This course will help you understand Factor analysis and it’s link to linear regression. See how Principal Components Analysis is a cookie cutter technique to solve factor extraction and how it relates to Machine learning .
Principal Components Analysis
Implementing PCA in Excel and VBA
Optimization techniques are used everywhere, but until recently they were not that important in software. With the rising importance of machine learning that is changing, because training ML models requires optimization in parameter training.
This course focuses on the theory and implementation of optimization in Excel using Solver.
Linear and Logistic Regression:
This course will teach you how to build robust linear models and do logistic regression in Excel and VBA
Linear Regression is a powerful method for quantifying the cause and effect relationships that affect different phenomena in the world around us. This course will teach you how to build robust linear models that will stand up to scrutiny when you apply them to real world situations.
Logistic regression: Logistic regression has many cool applications : analyzing consequences of past events, allocating resources, solving binary classification problems using machine learning and so on. This course will help you understand the intuition behind logistic regression and how to solve it using cookie-cutter techniques.
Simple Regression :
Multiple Regression :
Logistic Regression :
Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.