Python for Marketing (2019)
1h 45mIntermediate2019-11-12
Authors

Nick Duddy
Founder of Miratrix, a search and app marketing agency

Madecraft
Full-Service Learning Content Company
Course details
Take your marketing analytics to the next level with Python. The features that make Python so useful for data scientists are the same ones that marketers can use to better understand their customers, product performance, competition, and marketplace. In this course from Madecraft, you can learn how to use Python to improve marketing at your business. Discover how to import and clean data from sources like Google Analytics and Facebook, merge data sets, create detailed visualizations, analyze time series data, and build custom metrics and alerts for your marketing activities. Instructor Nick Duddy shows how to combine these techniques—and helpful Python libraries like Pandas and Seaborn—to conduct market analysis, predict consumer behavior, assess the competition, monitor market trends, and more.
Learning objectives
Benefits of Python for marketing
Importing data
Visualizing data
Cleaning data
Replacing missing data
Merging data sets
Creating charts and scatter plots with Python
Evaluating time series data
Calculating metrics
Filtering data
Creating alerts
Learning objectives
Benefits of Python for marketing
Importing data
Visualizing data
Cleaning data
Replacing missing data
Merging data sets
Creating charts and scatter plots with Python
Evaluating time series data
Calculating metrics
Filtering data
Creating alerts
Skills covered
PythonEssential TrainingProgramming LanguagesOpen SourceSoftware Development
Concepts
0. Introduction
- 01 - Accelerate your marketing with Python
1. The Role of Python in Marketing
- 02 - Prerequisites
- 03 - Why Python is great for marketers
- 04 - Why Python is valuable for marketers
2. Loading and Exploring Your Data
- 05 - Introduction to pandas
- 06 - Installing Jupyter
- 07 - Importing Google Analytics data
- 08 - Importing Google Search Console data
- 09 - Importing Facebook and AdWords data
- 10 - Accessing the Google Trends API
- 11 - Visualizing Google data
- 12 - Plotting Facebook and Google Ads data
- 13 - Visualizing Google Trends data
3. Cleaning, Wrangling, and Joining Your Data
- 14 - Introduction to data wrangling
- 15 - Fixing Google Analytics page data
- 16 - Preparing data to be grouped
- 17 - Creating new datasets with Groupby
- 18 - Rebuilding Google Analytics data
- 19 - Dropping columns
- 20 - Replacing missing Facebook Ad data
- 21 - Merging Google Analytics and Search Console
- 22 - Saving your data to a CSV
4. Visualizing Marketing Data in Python
- 23 - Custom visualizations in Python
- 24 - Import, explore, and plot a basic chart
- 25 - Creating Matplotlib subplots
- 26 - Plotting a secondary y-axis
- 27 - Adding x and y labels to a plot
- 28 - Rotating xticks labels on plot
- 29 - Adding a legend to a plot
- 30 - Adding a title to your plot
- 31 - Adding annotations to plots
- 32 - Switching between Matplotlib styles
- 33 - Using a scatter plot in Seaborn
- 34 - Customizing a scatter plot in Seaborn
- 35 - Creating a Facebook Ads heatmap in Seaborn
5. Working with Timeseries
- 36 - Time series notebook
- 37 - Fixing missing values
- 38 - Resampling time series data
- 39 - Rolling average plots
- 40 - Plotting weekly PPC and CPC data
- 41 - Adding dynamic annotations to a plot
6. Calculating, Filtering, and Creating New Metrics
- 42 - Introduction to calculating and filtering
- 43 - Calculating metrics
- 44 - Filtering data
7. Creating Helpful Alerts
- 45 - Intro to alert calculations
- 46 - Creating simple alerts
- 47 - Calculating two date ranges
- 48 - Creating alerts with actions
Conclusion
- 49 - Next steps
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