Data Science Hacks - Google Causal Impact

Data Science Hacks - Google Causal Impact

Welcome to our Google Causal Impact Course.

This course I'll teach you how to use the google's package Causal Impact in your on job or personal projects.

The Causal Impact model developed by Google works by fitting a bayesian structural time series model to observed data which is later used for predicting what the results would be had no intervention happened in a given time period. The idea is to used the predictions of the fitted model (depicted in blue) as a reference to what probably would had been observed with no intervention taking place.

After this course you will have a powerful tool, to measure (with statistical significance):

* The extra number of sales / app downloads / clicks / web site visits  caused by a marketing campaign

* The ROI of a Marketing Campaign

* The effect of a promotion over demand

* Any change of behavior in a series, caused by a known event

Inferring Causal Impact with Google's Causal Impact Package

Url: View Details

What you will learn

• Inferring the Causal Impact of a event (Promotion, Marketing Campaign, etc) over sales, website visits, download apps or any other variable you want to analyse
• How to use the pythons implementation of Google Causal Impact package
• How to calculate the ROI of a marketing campaign or a sales promotion

Rating: 3.55

Level: Intermediate Level

Duration: 1.5 hours

Instructor: Rodrigo Teixeira