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

0. Introduction

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

1. Exploring Marketing Data with Python

04 - Identify the role of Python in marketing
05 - Load marketing data in Python
06 - Interpret marketing data in Python

2. Cleaning Marketing Data with Python

07 - Clean marketing data in Python
08 - Handle missing values in marketing data
09 - Prepare for outlier handling in Python
10 - Handle outliers in marketing data
11 - Reformat marketing data in Python

3. Manipulating Marketing Data with Python

12 - Manipulate marketing data in Python
13 - Group marketing data by categories
14 - Merge marketing datasets in Python
15 - Filter marketing datasets in Python
16 - Export marketing data as CSV

4. Visualizing Marketing Data with Python

17 - Visualize marketing data in Python
18 - Create a bar plot in Python
19 - Label the axes in data visualization
20 - Add a title to your data visualization
21 - Use subplots for multiple visualizations
22 - Add a secondary y-axis to your data visualization
23 - Add a legend to your data visualization
24 - Annotate your data visualization
25 - Customize a scatter plot in Python
26 - Create a heatmap in Python

5. Working with Time Series Data in Python

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

6. Calculating Metrics in Python

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

7. Automating Marketing Tasks with Python

37 - Send personalized emails using Python
38 - Set up helpful alerts with Python
39 - Web scraping for marketing insights

Conclusion

40 - Unlock resources and next steps