Communicate-Data-Findings

Udacity's Data Analyst Nanodegree - Project 5

The "Why"

Data visualization is an important skill that is used in many parts of the data analysis process.

Exploratory data visualization generally occurs during and after the data wrangling process, and is the main method that you will use to understand the patterns and relationships present in your data. This understanding will help you approach any statistical analyses and will help you build conclusions and findings. This process might also illuminate additional data cleaning tasks to be performed.

Explanatory data visualization techniques are used after generating your findings, and are used to help communicate your results to others. Understanding design considerations will make sure that your message is clear and effective. In addition to being a good producer of visualizations, going through this project will also help you be a good consumer of visualizations that are presented to you by others.

Project Details

1. Dataset: Ford GoBike
2. Explore the data: Feel free to explore the jupyter notebook where the dataset is visually, and programatically explored.
3. Document the story: organized findings convey a story to present to an audience.
4. Communicate the findings - a slide deck with my findings is prepared for a curious audience.

Project Findings

This project is a win - win situation where a large number of people can benefit from this program:

• Environmentally friendly, budget friendly, and lifetsyle friendly.
• Subscribers (i.e. daily commuters) benefit from a health commuting choice.
• Customers (i.e. tourists, students, etc.) have a sustainable, yet flexible option for touring the city.
• Affordable and convenient transportation for the people of all socioeconomic classes.
• Renting a bike from theFord GoBike System is a fantastic (healthy and environmentally friendly) way of moving around in the city, both for enjoyment and work.

There are two types of clients using the system: Subscribers and Customers. Subscribers are primarily daily commuters, having short trips to and from work, who rent a bike on weekdays at 8-9am and 5-6pm. Customers are usually tourists or occassional riders who use the system mainly on weekends to explore the Bay Area. Age is also a factor within user type. Subscribers who fall in the age group between 26-35 years old are the most common age group to use the bike sharing system. The 26-35 years old also lead the spike which occurs across all age groups in October. Subscribers who fall in the 36-45 year old age group are the next most common age group to use the bike sharing system, and follow a similar trend at the 26-35 year olds.

Files

• readme.md - This Markdown file contains sections that you should fill out as you select your dataset, complete your exploration, and plan your explanatory analysis.

• exploration_template.ipynb - This Jupyter Notebook contains section templates to help you organize your exploration, starting from loading in the data, working through univariate visualizations, and ending with bivariate and multivariate exploration.

• slide_deck_template.ipynb - This Jupyter Notebook contains starter cells to help you organize your slide deck deliverable. These cells provide an example of how the slide deck should be organized, including pre-set slideshow settings.