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. The primary method of conveying your findings will be through transforming your exploratory visualizations from the first part into polished, explanatory visualizations.
This project uses Python 3 and is designed to be completed through the Jupyter Notebooks IDE. It is highly recommended that you use the Anaconda distribution to install Python, since the distribution includes all necessary Python libraries as well as Jupyter Notebooks. The following libraries are expected to be used in this project:
- NumPy
- pandas
- Matplotlib
- Seaborn
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.
After completing this project, you will be able to:
- Supplement statistics with visualizations to build understanding of data.
- Choose appropriate plots, limits, transformations, and aesthetics to explore a dataset, allowing you to understand distributions of variables and relationships between features.
- Use design principles to create effective visualizations for communicating findings to an audience.
First, you will choose a dataset
It’s time to get to the interesting bits. Explore your data and document your findings in a report. The report should briefly introduce the dataset, then systematically walk through the points of exploration that you conducted. You should have headers and text that organize your thoughts and findings. Visualizations in this part of the project need not be completely polished: this is just your own exploration at this point. However, you should still make sure that you adhere to principles of using appropriate plot types and encodings so that accurate conclusions can be drawn, and that you have enough comments and labeling so that when you return to your work, you can quickly grasp your analysis steps.
At the end of your exploration, you probably have a bunch of things that you’ve discovered. Now it’s time to organize your findings and select a story that you will convey to others. In your readme document, you should summarize your main findings and reflect on the steps you took in your data exploration. You should also lay out the key insights that you want to convey in your explanatory report as well as any changes to visualizations, or note new visualizations that will be created to bridge between your insights.
Follow the plans you laid out in the previous step and create a slide deck with explanatory data visualizations to tell a story about the data you explored. You can start with code that you used in your exploration, but you should make sure that the code is revised so that your plots are polished. Make sure that you also pay attention to aspects of design integrity in your revisions.
Though not required, it is highly recommended that you try to get feedback from at least one person before you submit your project. By sharing your work with others, you can get input from a different perspective that catches things that you may have originally missed. Share your slide deck with someone in person and have them provide live feedback on what they get from your slide deck. Alternatively, you can also share your work with your fellow students. Post a message in a student community channel for this project with a link to your project and ask for feedback. Be sure to keep an eye out for others who are also seeking feedback and return the favor!
You might need to ask specific questions to prompt your reader. The following questions might be good starters; be sure to follow up or come up with your own questions:
- What do you notice about each visualization?
- What questions do you have about the data?
- What relationships do you notice?
- What do you think is the main takeaway from the slide deck?
- Is there anything that you don’t understand from the plots?
This is a datset of Proser Loans , Prosper makes personal loans easy. Whether you're consolidating debt or remodeling your home, we have a solution for you. This data set contains 113,937 loans with 81 variables on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, and many others. (Last updated 03/11/2014)
This data set contains 113,937 loans with 81 variables . Rows = 113,937 and Columns = 81
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