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Analyzing Marketing Campaigns with pandas

BasicSkill Level

4.7+

Updated 04/2026

Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!

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PythonExploratory Data Analysis

4 hr

14 videos

53 Exercises

4,500 XP

33,706

Statement of Accomplishment

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Course Description

One of the biggest challenges when studying data science technical skills is understanding how those skills and concepts translate into real jobs. Whether you're looking to level up in your marketing job by incorporating Python and pandas or you're trying to get a handle on what kinds of work a data scientist in a marketing organization might do, this course is a great match for you. We'll practice translating common business questions into measurable outcomes, including "How did this campaign perform?", "Which channel is referring the most subscribers?", "Why is a particular channel underperforming?" and more using a fake marketing dataset based on the data of an online subscription business. This course will build on Python and pandas fundamentals, such as merging/slicing datasets, groupby(), correcting data types and visualizing results using matplotlib.

Prerequisites

Intermediate Python

1

Pandas

In this chapter, you will review pandas basics including importing datasets, exploratory analysis, and basic plotting.

Introduction to pandas for marketing

Importing the dataset

Examining the data

Data types and data merging

Updating the data type of a column

Adding new columns

Date columns

Initial exploratory analysis

Daily marketing reach by channel

Visualizing daily marketing reach

2

Exploratory Analysis & Summary Statistics

In this chapter, you will learn about common marketing metrics and how to calculate them using pandas. You will also visualize your results and practice user segmentation.

Introduction to common marketing metrics

Calculating conversion rate

Calculating retention rate

Customer segmentation

Comparing language conversion rate (I)

Comparing language conversion rate (II)

Aggregating by date

Plotting campaign results (I)

Visualize conversion rate by language

Creating daily conversion rate DataFrame

Setting up our data to visualize daily conversion

Visualize daily conversion rate

Plotting campaign results (II)

Marketing channels across age groups

Grouping and counting by multiple columns

Analyzing retention rates for the campaign

3

Conversion Attribution

In this chapter, you will build functions to automate common marketing analysis and determine why certain marketing channels saw lower than usual conversion rates during late January.

Building functions to automate analysis

Building a conversion function

Test and visualize conversion function

Plotting function

Putting it all together

Identifying inconsistencies

House ads conversion rate

Analyzing House ads conversion rate

House ads conversion by language

Creating a DataFrame for house ads

Confirming house ads error

Resolving inconsistencies

Setting up conversion indexes

Analyzing user preferences

Creating a DataFrame based on indexes

Assessing bug impact

4

Personalization A/B Test

In this chapter, you will analyze an A/B test and learn about the importance of segmentation when interpreting the results of a test.

A/B testing for marketing

Determining key metrics

Test allocation

Comparing conversion rates

Calculating lift & significance testing

Creating a lift function

Evaluating statistical significance

A/B testing & segmentation

Building an A/B test segmenting function

Using your segmentation function

Analyzing Marketing Campaigns with pandas

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FAQs

What marketing metrics will I learn to calculate with pandas?

You will calculate conversion rates, subscriber acquisition by channel, and campaign performance metrics, then visualize results and practice user segmentation techniques.

What kind of dataset does this course use?

You will work with a fake marketing dataset based on an online subscription business, designed to mimic real scenarios like channel attribution and A/B testing.

Does this course include A/B test analysis?

Yes. The final chapter walks you through analyzing an A/B test on personalization and teaches why segmentation is critical when interpreting test results.

What pandas skills should I already have?

You need Introduction to Python and Intermediate Python. Comfort with importing data, slicing DataFrames, groupby operations, and basic matplotlib plotting is expected.

Will I build reusable functions for marketing analysis?

Yes. Chapter 3 teaches you to build functions that automate common marketing calculations, such as diagnosing why specific channels had lower conversion rates during a given period.

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