Communicate Data Findings

Use Python visualization libraries, including matplotlib and seaborn, to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. Then, produce a short presentation that illustrates interesting properties, trends, and relationships that you discovered in your selected dataset.

Evaluation

Use the instructions in the project lesson to complete the steps in the project. Once you're done, use the Project Rubric to review your project. If you see room for improvement in any category in which you do not meet specifications, keep working! If you are happy with your project, then you are ready to submit it!

Remember, your project will be evaluated by a Udacity reviewer according to the same Project Rubric . Your project must "Meet Specifications" in each category in order for your submission to pass.

Submission

If you're ready to submit your project, make sure that you collect the following files in a .zip file:

  1. A report with your exploratory data analysis, in PDF or HTML format. If you used a Jupyter Notebook to conduct your analysis, you should also include the original .ipynb file in your submission. If you did not, you should include the code you used in your exploration as .py scripts.
  2. A slide deck presentation with your explanatory analysis, in PDF or HTML format. If you used a Jupyter Notebook, include the original .ipynb file with your submission and any template file used to render the slide deck.
  3. A readme document, in plain text, Markdown, or PDF format, including the following information:
  • Which dataset you chose. If not part of Udacity's dataset options, document the source of your data.
  • Main findings from the exploratory data analysis, and how you chose the results to put in your explanatory analysis.
  • If you obtained feedback from others for your explanatory designs, document them here.
  • List of resources used during the creation of the project. This includes web sites, books, forums, blog posts, and GitHub repositories.
  1. If you chose a dataset that is not in the Dataset Options document, include the dataset used to perform the analyses. If the dataset is too large, then make sure the readme documents where the data can be found so that the reviewer can check your work as needed.

Ready to submit your project?

Click on the "Submit Project" button below and follow the instructions to submit!

It can take us up to a week to grade the project, but in most cases it is much faster. You will get an email when your submission has been reviewed. If you are having any problems submitting your project or wish to check on the status of your submission, please email us at review-support@udacity.com.