Python for Marketing

1. Exploring Marketing Data with Python

• Identify the role of Python in marketing
• Load marketing data in Python
  • load_marketing_data_starter_code.ipynb
  • analytics_data.csv
  • users.csv
• Interpret marketing data in Python
  • interpret_marketing_data_starter_code.ipynb
  • analytics_data.csv

2. Cleaning Marketing Data with Python

• Clean marketing data in Python
  • clean_marketing_data.ipynb
  • analytics_data.csv
• Handle missing values in marketing data
  • handle_missing_data.ipynb
• Prepare for outlier handling in Python
• Handle outliers in marketing data
  • handle_outliers.ipynb
  • marketing_data.csv
  • marketing_data_dictionary.csv
• Reformat marketing data in Python
  • reformat_marketing_data.ipynb
  • analytics_data_updated

3. Manipulating Marketing Data with Python

• Manipulate marketing data in Python
  • manipulate_marketing_data.ipynb
  • analytics_data_updated
• Group marketing data by categories
  • group_marketing_data.ipynb
  • channels.CSV
• Merge marketing datasets in Python
  • merge_marketing_datasets.ipynb
  • search_data.csv
  • analytics_data_updated
• Filter marketing datasets in Python
  • filter_marketing_datasets.ipynb
  • analytics_search_data.csv
• Export marketing data as CSV
  • export_marketing_data.ipynb
  • analytics_search_data.csv

4. Visualizing Marketing Data with Python

• Visualize marketing data in Python
• Create a bar plot in Python
  • create_bar_plot.ipynb
  • campaigns_cleaned.CSV
• Label the axes in data visualization
  • label_the_axes.ipynb
  • campaigns_cleaned.CSV
• Add a title to your data visualization
  • add_a_title.ipynb
  • campaigns_cleaned.CSV
• Use subplots for multiple visualizations
  • use_subplots.ipynb
  • analytics_search_data.csv
• Add a secondary y-axis to your data visualization
  • add_a_secondary_y_axis.ipynb
  • analytics_search_data.csv
• Add a legend to your data visualization
  • add_a_legend.ipynb
  • analytics_search_data.csv
• Annotate your data visualization
  • annotate_your_data_vis.ipynb
  • analytics_search_data.csv
• Customize a scatter plot in Python
  • customize_a_scatter_plot.ipynb
  • analytics_search_data.csv
• Create a heatmap in Python
  • create_a_heatmap.ipynb
  • social_media_campaign_data.csv

5. Working with Time Series Data in Python

• Find value of time series data in marketing
• Prepare the times series data for analysis
  • prepare_the_time_series.ipynb
  • page_traffic.csv
• Resample the time series data
  • resample_the_time_series.ipynb
  • page_traffic_updated.csv
• Create a rolling average plot for time-series marketing data
  • create_a_rolling_average_plot.ipynb
  • page_traffic_updated.csv
• Plot cost-per-click marketing data
  • plot_cpc_data.ipynb
  • cpc_data.csv
• Add dynamic annotations
  • add_dynamic_annotations.ipynb
  • cpc_data.csv

6. 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

7. Automating Marketing Tasks with Python

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

References

● Documentation for Pandas
● Documentation for Matplotlib
● Documentation for Seaborn
● Documentation for Numpy
● Documentation for Scipy
● Documentation for Smtplib