Python for Marketing

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, learn how to use Python to improve marketing outcomes for your business.

Discover how to import and clean data from various sources, merge data sets, create detailed visualizations, analyze time series data, build custom metrics, and automate key tasks to streamline marketing activities. Along the way, get tips on combining these techniques to conduct market analysis, predict consumer behavior, assess the competition, monitor market trends, and more.

Concepts

Introduction

Accelerate your marketing with Python
Check out the course prerequisites
Set up your coding environment and tools

Exploring Marketing Data with Python

Identify the role of Python in marketing
Load marketing data in Python
Interpret marketing data in Python

Cleaning Marketing Data with Python

Clean marketing data in Python
Handle missing values in marketing data
Prepare for outlier handling in Python
Handle outliers in marketing data
Reformat marketing data in Python

Manipulating Marketing Data with Python

Manipulate marketing data in Python
Group marketing data by categories
Merge marketing datasets in Python
Filter marketing datasets in Python
Export marketing data as CSV

Visualizing Marketing Data with Python

Visualize marketing data in Python
Create a bar plot in Python
Label the axes in data visualization
Add a title to your data visualization
Use subplots for multiple visualizations
Add a secondary y-axis to your data visualization
Add a legend to your data visualization
Annotate your data visualization
Customize a scatter plot in Python
Create a heatmap in Python

Working with Time Series Data in Python

Find value of time series data in marketing
Prepare the times series data for analysis
Resample the time series data
Create a rolling average plot for time-series marketing data
Plot cost-per-click marketing data
Add dynamic annotations

Calculating Metrics in Python

Calculate click-through rate in Python
Calculate bounce rate in Python
Calculate key performance indicators
Create new metrics for marketing reports

Automating Marketing Tasks with Python

Send personalized emails using Python
Set up helpful alerts with Python
Web scraping for marketing insights

Conclusion

Unlock resources and next steps