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Scenario 1:
Scenario 1:
You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations
R Script
R Script
practice_case_study_01_cyclistic.pdfScenario 2:
Scenario 2:
The market research company has asked you to lead the web scraping project, using your expertise in data extraction and analysis. They have provided you with a detailed scope of work, which includes the following requirements:
Scrape data from all active Shopify stores in the USA, including their URLs, store names, and product categories.
Extract additional data from each store's website, including product names, descriptions, prices, and images.
Organize the data in a structured format, such as a spreadsheet or database, that can be easily analyzed and manipulated.
Verify the accuracy of the data and remove any duplicates or irrelevant information.
Provide regular updates on the progress of the scraping project and any issues that arise.
To complete this project, you will need to use web scraping tools and techniques to extract data from a large number of Shopify stores. You may need to develop custom scripts or use existing software tools to automate the scraping process, and you may also need to work with other data analysts or IT professionals to ensure the data is properly organized and analyzed.
Once the data is collected and organized, you will need to use data analysis tools and techniques to identify trends and patterns in the data. You may need to create visualizations or dashboards to help the retailer better understand the competitive landscape and make strategic decisions.
R Script:
R Script:
Exported as CSV File:
Exported as CSV File:
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