Overview
-
Take your marketing analytics to the next level with Python. Learn how to create detailed visualizations and build custom metrics and alerts for your marketing activities.
Syllabus
-
Introduction
- Accelerate your marketing with Python
- Prerequisites
- Why Python is great for marketers
- Why Python is valuable for marketers
- Introduction to pandas
- Installing Jupyter
- Importing Google Analytics data
- Importing Google Search Console data
- Importing Facebook and AdWords data
- Accessing the Google Trends API
- Visualizing Google data
- Plotting Facebook and Google Ads data
- Visualizing Google Trends data
- Introduction to data wrangling
- Fixing Google Analytics page data
- Preparing data to be grouped
- Creating new datasets with Groupby
- Rebuilding Google Analytics data
- Dropping columns
- Replacing missing Facebook Ad data
- Merging Google Analytics and Search Console
- Saving your data to a CSV
- Custom visualizations in Python
- Import, explore, and plot a basic chart
- Creating Matplotlib subplots
- Plotting a secondary y-axis
- Adding x and y labels to a plot
- Rotating xticks labels on plot
- Adding a legend to a plot
- Adding a title to your plot
- Adding annotations to plots
- Switching between Matplotlib styles
- Using a scatter plot in Seaborn
- Customizing a scatter plot in Seaborn
- Creating a Facebook Ads heatmap in Seaborn
- Time series notebook
- Fixing missing values
- Resampling time series data
- Rolling average plots
- Plotting weekly PPC and CPC data
- Adding dynamic annotations to a plot
- Introduction to calculating and filtering
- Calculating metrics
- Filtering data
- Intro to alert calculations
- Creating simple alerts
- Calculating two date ranges
- Creating alerts with actions
- Next steps
