After the project, you will be able to:
- Store data in a DataFrame, and export it in some shape or form (text file, database, json text files)
- Visualize data through means of Matplotlib or ggplot (the matplotlib wrapper)
- Provide insight about the data in front of your peers
Contents Include
- Exploratory Data Analysis of your data set including visuals, subsets, acquistion notes, etc.
- Preliminary questions for your project
- Well organized ipython notebook with code and output.
- 5 minute presentation: A descriptive and visual tour of your dataset
Due Date and Grade Scale
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Projects will be due October 1st, 2014.
- You'll be presenting your projects in small groups on that class day, so come prepared
- Expect to see all projects turned in before the beginning of class
- If you get it to us earlier, Dave and I will try to provide early feedback
You will Submit
- An ipython notebook, well organized and formatted, including markdown, showcasing all of your code and outputs
Going the Extra Mile
- You can (and should!) rework your project into a post on your own blog. It's encouraged to share your work within your social networks.
- Engineers should consider exploring frameworks for deploying visuals on the web: d3.js, shiny (you'd also have to learn R, which is encouraged), iPython Notebook
Lesson material in order to complete Project
- Fundamental Statistics Review
- Python Basics and Using Libraries/Modules
- Data collection, storage, and manipulation
- Data visualization in Python
Next Steps
- Consider how the data collected could be used in predictive analytics application