Analyzing Marketing Campaigns with pandas
Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!
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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.
- 1
Pandas
FreeIn this chapter, you will review pandas basics including importing datasets, exploratory analysis, and basic plotting.
Introduction to pandas for marketing50 xpImporting the dataset100 xpExamining the data100 xpData types and data merging50 xpUpdating the data type of a column100 xpAdding new columns100 xpDate columns100 xpInitial exploratory analysis50 xpDaily marketing reach by channel100 xpVisualizing daily marketing reach100 xp - 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 metrics50 xpCalculating conversion rate100 xpCalculating retention rate100 xpCustomer segmentation50 xpComparing language conversion rate (I)100 xpComparing language conversion rate (II)100 xpAggregating by date100 xpPlotting campaign results (I)50 xpVisualize conversion rate by language100 xpCreating daily conversion rate DataFrame100 xpSetting up our data to visualize daily conversion100 xpVisualize daily conversion rate100 xpPlotting campaign results (II)50 xpMarketing channels across age groups100 xpGrouping and counting by multiple columns100 xpAnalyzing retention rates for the campaign100 xp - 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 analysis50 xpBuilding a conversion function100 xpTest and visualize conversion function100 xpPlotting function100 xpPutting it all together100 xpIdentifying inconsistencies50 xpHouse ads conversion rate100 xpAnalyzing House ads conversion rate100 xpHouse ads conversion by language100 xpCreating a DataFrame for house ads100 xpConfirming house ads error100 xpResolving inconsistencies50 xpSetting up conversion indexes100 xpAnalyzing user preferences100 xpCreating a DataFrame based on indexes100 xpAssessing bug impact100 xp - 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 marketing50 xpDetermining key metrics50 xpTest allocation100 xpComparing conversion rates100 xpCalculating lift & significance testing50 xpCreating a lift function100 xpEvaluating statistical significance50 xpA/B testing & segmentation50 xpBuilding an A/B test segmenting function100 xpUsing your segmentation function100 xpWrap-up50 xp
In the following tracks
Marketing Analytics
Sumedh Panchadhar
Mona KhalilPrerequisites
Data Manipulation with pandas
Jill Rosok
Data Scientist at Spotify
Jill is a Data Scientist at Spotify.
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Lloyds Banking Group
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Harvard Business School
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Decision Science Analytics, USAA
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