Jersey City CitiBike Tableau - (Tableau Challenge)

Expected to generate regular reports for city officials looking to publicize and improve the city program.

Classmate Contributors on study group:

Thank you for helping on Challenge Study Group:

• Vinny Shankar -- Vinny Shankar
• Jed Miller -- Jed Miller
• Jesús Jiménez -- Jesús Jiménez

Tableau Story URL:

Tableau Story URL: https://public.tableau.com/app/profile/hany.dief/viz/Challenge18-CitiBike/JCCitiBikeStory?publish=yes

Purpose:

• Cleaning Data on Pandas Jupyter notebook.

• Import cleaned data into Tableau.

• Create and style worksheets, dashboards, and stories in Tableau.

• Use Tableau worksheets to display data in a professional way.

• Portray data accurately using Tableau dashboards.

Overview of the statistical analysis:

Since 2013, the Citi Bike program has implemented a robust infrastructure for collecting data on the program's utilization. Each month, bike data is collected, organized, and made public on the Citi Bike Data

Links to an external site. webpage.

However, while the data has been regularly updated, the team has yet to implement a dashboard or sophisticated reporting process. City officials have questions about the program, so your first task on the job is to build a set of data reports to provide the answers.

Instructions

Your task in this assignment is to aggregate the data found in the Citi Bike Trip History Logs and find two unexpected phenomena.

1. Design 2–5 visualizations for each discovered phenomenon (4–10 total). You may work with a timespan of your choosing. Optionally, you can also merge multiple datasets from different periods.

2. Use your visualizations (not necessarily all of them) to design a dashboard for each phenomenon. The dashboards should be accompanied by an analysis explaining why the phenomenon may be occurring.

3. Create one of the following visualizations for city officials:

   • 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.

   • 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.

   • The map you choose should also be accompanied by a write-up describing any trends that were noticed during your analysis.

4. Create your final presentation:

   • Create a Tableau story that brings together the visualizations, requested maps, and dashboards.

   • Ensure your presentation is professional, logical, and visually appealing.

Tableau Story

Tableau Story Summary:

1- Winter is lowest bike usage while Summer is highest.

2- Members highest usage is during commute hours during weekday.

3- Casual highest usage 8:00 am - 8:00 pm weekends.

4- Bike type demand inorder:

1) Classic

2) Electric

3) Docked

"maybe they cost diffrence & that's is a main factor".

5- Most bike pickup starts in NJC but ends usually in NJC as well & sometimes way far to NY.

6- Wish if NJC CitiBike statistics have Gender & Age informations will be interesting to study & analyize them.

DATA SOURCES & REFERENCE:

Source Of Data: https://citibikenyc.com/system-data