Econometrics and Statistics for Business in R & Python

Learn Causal Inference & Statistical Modeling to solve finance and marketing business problems in Python and R

Created by: Diogo Alves de Resende
Analytics and Data Science expert

What Will I Learn?

• Understand the application of econometric techniques in business settings
• Apply Google's Causal Impact to measure the effect of an intervention on a time series.
• Code econometric techniques in R and Python from scratch.
• Solve real business or economic problems using econometric techniques.
• Use propensity score matching to compare outcomes between groups while controlling for confounding variables.
• Develop an intuitive understanding of Difference-in-differences, Google's Causal Impact, Granger Causality, Propensity Score Matching, and CHAID
• Perform Granger causality to test for causality between two time series.
• Develop intuition for econometric techniques through business case studies.
• Practice coding and applying econometric techniques through challenging and interesting problems.
• Understand and apply basic statistical concepts and techniques in real-life business cases

Requirements

• Basic high school math
• Basic statistics: mean, median, mode

Target audience

• Students or recent graduates interested in Econometrics and Data Science
• Data Scientists that would like to learn econometrics
• Business Analysts wanting to make a difference in their current job
• People curious about Econometrics and Data Science
• Professionals who would like to know more about analytics