English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 44m | 312 MB
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.
Topics include:
- 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
Table of Contents
Introduction
1 Accelerate your marketing with Python
The Role of Python in Marketing
2 Prerequisites
3 Why Python is great for marketers
4 Why Python is valuable for marketers
Loading and Exploring Your Data
5 Introduction to pandas
6 Installing Jupyter
7 Importing Google Analytics data
8 Importing Google Search Console data
9 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
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
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
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
Calculating, Filtering, and Creating New Metrics
42 Introduction to calculating and filtering
43 Calculating metrics
44 Filtering data
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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