Introduction
Accelerate your marketing with Python
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1. The Role of Python in Marketing
Prerequisites
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Why Python is great for marketers
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Why Python is valuable for marketers
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2. Loading and Exploring Your Data
Introduction to pandas
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Installing Jupyter
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Importing Google Analytics data
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Importing Google Search Console data
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Importing Facebook and AdWords data
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Accessing the Google Trends API
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Visualizing Google data
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Plotting Facebook and Google Ads data
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Visualizing Google Trends data
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3. Cleaning, Wrangling, and Joining Your Data
Introduction to data wrangling
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Fixing Google Analytics page data
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Preparing data to be grouped
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Creating new datasets with Groupby
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Rebuilding Google Analytics data
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Dropping columns
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Replacing missing Facebook Ad data
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Merging Google Analytics and Search Console
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Saving your data to a CSV
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4. Visualizing Marketing Data in Python
Custom visualizations in Python
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Import, explore, and plot a basic chart
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Creating Matplotlib subplots
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Plotting a secondary y-axis
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Adding x and y labels to a plot
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Rotating xticks labels on plot
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Adding a legend to a plot
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Adding a title to your plot
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Adding annotations to plots
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Switching between Matplotlib styles
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Using a scatter plot in Seaborn
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Customizing a scatter plot in Seaborn
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Creating a Facebook Ads heatmap in Seaborn
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5. Working with Timeseries
Time series notebook
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Fixing missing values
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Resampling time series data
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Rolling average plots
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Plotting weekly PPC and CPC data
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Adding dynamic annotations to a plot
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6. Calculating, Filtering, and Creating New Metrics
Introduction to calculating and filtering
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Calculating metrics
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Filtering data
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7. Creating Helpful Alerts
Intro to alert calculations
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Creating simple alerts
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Calculating two date ranges
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Creating alerts with actions
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Ex_Files_Python_Marketing.zip
(1.8 MB)