This course will teach you how to build robust linear models and do logistic regression in Excel, R and Python.

Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.


Robust linear models : 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.

Excel, R and Python :  Put what you’ve learnt into practice. Leverage these powerful analytical tools to build models for stock returns.

What’s covered?

Simple Regression : 

  • Method of least squares, Explaining variance, Forecasting an outcome
  • Residuals, assumptions about residuals
  • Implement simple regression in Excel, R and Python
  • Interpret regression results and avoid common pitfalls

Multiple Regression : 

  • Implement Multiple regression in Excel, R and Python
  • Introduce a categorical variable

Logistic Regression : 

  • Applications of Logistic Regression, the link to Linear Regression and Machine Learning
  • Solving logistic regression using Maximum Likelihood Estimation and Linear Regression
  • Extending Binomial Logistic Regression to Multinomial Logistic Regression
  • Implement Logistic regression to build a model stock price movements in Excel, R and Python
Connect the Dots: Linear and Logistic Regression
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Connect the Dots: Linear and Logistic Regression
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