Introduction to Data Science
Topic
The project should satisfy an engineering/research/consumer/expository need. What need does the project satisfy? Besides yourself, who is the target audience?
You may work solo, or in teams of two (no more, please).
Types of Topics
1. Data Analysis
a. Come up with a question you want to answer. For example, what are the trends of gas prices at your local gas station? Are prices higher during certain days of the week? Certain months?
b. Find the data. There are many data repositories on the web, government and otherwise. Decide which data you will use and how to get it (is it downloadable? Will you scrape it with a python script?)
c. Clean the data. Deal with missing values in an appropriate way (mean, median, 0, whatever makes sense for your project).
d. Analyze the data. This could be via groupby, pivot tables, or your own statistical analysis.
e. Output the data. This could be by charts (e.g., matplotlib or Seaborne)
2. Explore NumPy functionality we did not cover in class. Give a demo of this functionality and explain what you are doing.
3. Look at a Python data analysis tool we did not cover and give a demo
4. Walk us through a Machine Learning algorithm using scikit-learn or another Python Machine Learning library.
5. Roll your own project. If you do this, please let me know.