Python Tutorial: Introduction to pandas for marketing
Want to learn more? Take the full course at https://learn.datacamp.com/courses/analyzing-marketing-campaigns-with-pandas at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work.
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Welcome to the course! My name is Jill Rosok, and in this course, you will learn about how Data Science techniques are used to understand the impact of marketing campaigns.
My hope is that this course will not only help to reinforce your Python and pandas abilities but also help understand what kinds of problems data scientists on marketing teams might encounter.
While the possibilities are endless, there are a few types of projects that will almost certainly come up in a marketing team.
You will likely be asked how a marketing campaign performed. Marketing campaigns mean anything that required the marketing team to put in work to promote your product. It could be a new creative direction, a discounted product, targeting a specific demographic or a multitude of other options.
Another common question is how different marketing channels are performing. For example, when you send out an email how many new users subscribe? Given current conversion rates and revenue, should you continue investing in this channel and how much should you spend?
Another common practice in marketing is running experiments, or A/B tests, to try to understand the impact of a particular change.
All of these types of questions can intersect. You could analyze a marketing campaign by channel based on A/B test results, or you could tackle any one of these types of questions individually.
First, let me give you a quick refresher on pandas.
Hopefully, you've completed DataCamp's foundational pandas courses, but as a reminder, pandas makes data analysis and transformation in Python much easier by formatting the data into a table-like structure similar to an Excel spreadsheet.
Pandas makes it easy to import and export common data formats. Once your data is impor
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