- This was saved and published to my Tableau Public account: https://public.tableau.com/app/profile/elliotteinstein/viz/Citi_Bike_Analysis_16517287699750/CitiBikeAnalysis.
As the new lead analyst for the New York Citi Bike Program, I am now responsible for overseeing the largest bike sharing program in the United States. In this new role, I will be expected to generate regular reports for city officials looking to publicize and improve the city program.
Since 2013, the Citi Bike Program has implemented a robust infrastructure for collecting data on the program's utilization. Through the team's efforts, each month bike data is collected, organized, and made public on the Citi Bike Data webpage.
However, while the data has been regularly updated, the team has yet to implement a dashboard or sophisticated reporting process. City officials have a number of questions on the program, so my first task on the job is to build a set of data reports to provide the answers to key business needs to drive decision making.
**Designed visualizations for discovered phenomena (4-10 total). I worked with the timespan from Jan-March 2022 and merged those multiple data sets accordingly.
**The following are some questions I chose to tackle.
- By what percentage has total ridership grown?
- How has the proportion of short-term customers (casual) and annual subscribers (members) changed?
- What are the peak hours in which bikes are used during winter months?
- Today, what are the top 10 stations in the city for starting a journey?
- Today, what are the top 10 stations in the city for ending a journey? (Based on data, why?)
- Today, what are the bottom 10 stations in the city for starting a journey?
- Today, what are the bottom 10 stations in the city for ending a journey (Based on data, why?)
Next, as a chronic over-achiever:
- I used my visualizations to design a dashboard for each phenomena.
- The dashboards were accompanied with a brief analysis explaining why the phenomena may be occuring.
City officials would also like to see one of the following visualizations:
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Basic: A static map that plots all bike stations with a visual indication of the most popular locations to start and end a journey with zip code data overlaid on top.*************
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Advanced: A dynamic map that shows how each station's popularity changes over time (by month and year). Again, with zip code data overlaid on the map.**************
Finally, I created my final presentation
- Created a Tableau story that brings together the visualizations, requested maps, and dashboards
My audience will be city officials, public administrators, and heads of New York City departments. My data and analysis needs to be presented in a way that is focused, concise, easy-to-understand, and visually compelling. My visualizations are colorful enough to be included in press releases, and my analysis should be thoughtful enough for dictating programmatic changes.
- Data was not consistent and clean throughout the analysis.
- **Solved By: **
- Checking for null values, dropping unclean and null values, and cleaning dataframes using python in jupyter notebook. Used concat function to combine all three excel files into a single dataframe.
- I needed be creative in how you combine each of the CSV files because each file contained 1M+ records.
- **Solved By: **
- I did an ETL process move data file from the jupter notebook in PostGreSQL database((https://www.citibikenyc.com/system-data). I created a connection to PostgreSQL database to read in the large 4M+ record file. I checked for a successful connection to the database and confirmed that the tables had been created, and I also confirmed by querying the database.