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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 imported, you can adapt your dataset to work for your analysis, including aggregations, merging multiple datasets, and selecting subsets of data that fit specific criteria.
To use pandas, first import pandas using the alias pd.
To import a CSV file, you can use the read_csv() function and pass the name of the file you want to import.
Once you've imported your data, it is a good practice to examine its contents using the head() method. This will return the first five rows of the DataFrame.
Use the describe() method to print the statistics of all columns in your dataset. You can inspect the output to find some obvious errors. For example, if you see negative values in a date column, this might indicate an error. In addition, pay careful attention to the minimum and maximum values. If the maximum is much larger than the median, it might be an outlier and merit further investigation.
Finally, you can identify the data types and the number of non-missing values in your DataFrame using the info() method. The result includes all columns and their data types.
Now that you have a high-level understanding of pandas and data science in marketing let's practice combining these two skills!
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