课程

Analyzing Marketing Campaigns with pandas

基础技能水平

更新时间 2026年4月

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

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PythonExploratory Data Analysis4 小时14 视频53 练习4,500 经验值33,356成就声明

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课程描述

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.

先决条件

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

50 经验值

Importing the dataset

100 经验值

Examining the data

100 经验值

Data types and data merging

50 经验值

Updating the data type of a column

100 经验值

Adding new columns

100 经验值

Date columns

100 经验值

Initial exploratory analysis

50 经验值

Daily marketing reach by channel

100 经验值

Visualizing daily marketing reach

100 经验值

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

50 经验值

Calculating conversion rate

100 经验值

Calculating retention rate

100 经验值

Customer segmentation

50 经验值

Comparing language conversion rate (I)

100 经验值

Comparing language conversion rate (II)

100 经验值

Aggregating by date

100 经验值

Plotting campaign results (I)

50 经验值

Visualize conversion rate by language

100 经验值

Creating daily conversion rate DataFrame

100 经验值

Setting up our data to visualize daily conversion

100 经验值

Visualize daily conversion rate

100 经验值

Plotting campaign results (II)

50 经验值

Marketing channels across age groups

100 经验值

Grouping and counting by multiple columns

100 经验值

Analyzing retention rates for the campaign

100 经验值

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

50 经验值

Building a conversion function

100 经验值

Test and visualize conversion function

100 经验值

Plotting function

100 经验值

Putting it all together

100 经验值

Identifying inconsistencies

50 经验值

House ads conversion rate

100 经验值

Analyzing House ads conversion rate

100 经验值

House ads conversion by language

100 经验值

Creating a DataFrame for house ads

100 经验值

Confirming house ads error

100 经验值

Resolving inconsistencies

50 经验值

Setting up conversion indexes

100 经验值

Analyzing user preferences

100 经验值

Creating a DataFrame based on indexes

100 经验值

Assessing bug impact

100 经验值

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

50 经验值

Determining key metrics

50 经验值

Test allocation

100 经验值

Comparing conversion rates

100 经验值

Calculating lift & significance testing

50 经验值

Creating a lift function

100 经验值

Evaluating statistical significance

50 经验值

A/B testing & segmentation

50 经验值

Building an A/B test segmenting function

100 经验值

Using your segmentation function

100 经验值

50 经验值

Analyzing Marketing Campaigns with pandas

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