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.
In this chapter, you will review pandas basics including importing datasets, exploratory analysis, and basic plotting.
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.
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.
In this chapter, you will analyze an A/B test and learn about the importance of segmentation when interpreting the results of a test.
In the following tracksMarketing Analytics
PrerequisitesData Manipulation with pandas
Data Scientist at Spotify
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Devon Edwards Joseph
Lloyds Banking Group
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Harvard Business School
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Decision Science Analytics, USAA