Data Science Hacks – Google Causal Impact

Data Science Hacks – Google Causal Impact, available at $39.99, has an average rating of 4.15, with 12 lectures, based on 33 reviews, and has 3104 subscribers.

You will learn about 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 This course is ideal for individuals who are Data Analysts and Data Science Students or Anyone interested in causal inference techniques and data analysis with python It is particularly useful for Data Analysts and Data Science Students or Anyone interested in causal inference techniques and data analysis with python.

Enroll now: Data Science Hacks – Google Causal Impact

Summary

Title: Data Science Hacks – Google Causal Impact

Price: $39.99

Average Rating: 4.15

Number of Lectures: 12

Number of Published Lectures: 12

Number of Curriculum Items: 12

Number of Published Curriculum Objects: 12

Original Price: $29.99

Quality Status: approved

Status: Live

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

Who Should Attend

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

Target Audiences

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

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

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: Course Overview

Lecture 2: Important Disclaimer!

Lecture 3: Introduction – Theoretical Background

Lecture 4: Introduction – How to install Google's Causal Impact Library

Chapter 2: Model Inputs

Lecture 1: The Response Variable

Lecture 2: The Control / Predictor Variable

Lecture 3: The Time Index & Pre / Post Period Inputs

Chapter 3: Building the Model

Lecture 1: Example 1: Simple Linear Data

Lecture 2: Example 2: More Complex Data

Lecture 3: Example 3: Real Data

Chapter 4: Interpreting the Results

Lecture 1: Interpreting the Results

Lecture 2: Exporting the results

Instructors

Rodrigo Teixeira
Marketing Science & Martech Specialist

Rating Distribution

1 stars: 1 votes
2 stars: 3 votes
3 stars: 9 votes
4 stars: 9 votes
5 stars: 11 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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