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Data Science Hacks - Google Causal Impact
Rating: 3.7 out of 5(41 ratings)
3,127 students

Data Science Hacks - Google Causal Impact

Inferring Causal Impact with Google's Causal Impact Package
Created byRodrigo Teixeira
Last updated 8/2024
English
English

What you'll 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

Course content

4 sections • 12 lectures • 1h 33m total length

  • Course Overview3:09

    Learn to infer and measure causal impact on time series using Google's causal impact package, with Python and Pandas basics, pre and post intervention periods, and model inputs.

  • Important Disclaimer!2:03

    Discover practical statistical analysis tools for beginners in data science hacks using Python and pandas with Google Causal Impact, without deep statistical theory or hypothesis testing.

  • Introduction - Theoretical Background7:30
  • Introduction - How to install Google's Causal Impact Library1:43

Requirements

  • Basic knowledge of python programming
  • Basic knowledge of python library pandas

Description

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



Who this course is for:

  • Data Analysts and Data Science Students
  • Anyone interested in causal inference techniques and data analysis with python

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