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Case study 1: Cyclistic bike-share analysis

Date:	·         Course/topic: Case study: Cyclistic bike-share analysis
Prompt:	·         How do annual members and casual riders use Cyclistic bikes differently? ·         Why would casual riders buy Cyclistic annual memberships? ·         How can Cyclistic use digital media to influence casual riders to become members? Moreno has assigned you the first question to answer: How do annual members and casual use Cyclistic bikes differently?
Journal Entry:	Today I began working on the Cyclistic case study assigned in the Google Data Analytics Capstone. My focus is on the first key question from Moreno: How do annual members and casual riders use Cyclistic bikes differently? So far, I’ve identified that understanding user behavior between these two groups is crucial. I plan to analyze ride data (duration, day of the week, time of day, start/end stations, etc.) to spot trends. My initial assumption is that annual members may use the bikes more consistently and for commuting, while casual riders might favor weekends, holidays, and recreational use. I’m preparing to clean and explore the datasets for patterns, focusing on the following: Ride length and frequency Popular start and end stations Preferred ride times and days Type of bikes used (classic, docked, or electric) I expect visualizations (like bar charts or heatmaps) to be very helpful in illustrating usage patterns. This will also help later in addressing the other two questions about converting casual users and digital marketing strategies.
Other thoughts or questions:	§  Will weather or special events significantly impact ride patterns between the two groups? §  Should I include geographic mapping to better understand location-specific trends? §  What behavioral triggers can be drawn from the data to recommend specific marketing actions?