Goal: as a part of Udacity's Data Analyst Nanodegree Program, you need to convey findings from the dataset through transforming exploratory visualizations into polished, explanatory visualizations.
This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, you will use Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, you will produce a short presentation that illustrates interesting properties, trends, and relationships that you discovered in your selected dataset.
Bay Wheels, previously known as Ford GoBike (https://www.lyft.com/bikes/bay-wheels) is a regional public bike sharing system in San Francisco Bay Area, California. Bay Wheels is the first regional and large-scale bicycle sharing system deployed in California and on the West Coast of the United States with nearly 500,000 rides since the launch in 2017 and had about 10,000 annual subscribers as of January 2018. The dataset used for this exploratory analysis consists of Bay Wheels's trip data for public use for February 2020, which you can find here. https://www.lyft.com/bikes/bay-wheels/system-data
Getting started
You need an installation of Python, plus the following libraries:
numpy pandas matplotlib.pyplot seaborn Motivation
After exploratory data analysis using visual tools in Python, and its libraries.
In this project I was curious to answer following question:
- When are most trips taken in terms of time of day, day of the week, or month of the year?
- How long does the average trip take?
- Does the above depend on if a user is a subscriber or customer?