Total 11 projects.
In each folder, the proj.py is the code of project. Also, there is a description of each project inside the folder.

The goal is that when students are presented with a problem they will say “I can write a program to solve that!”
The theme is to take real data and analyze it.
The course uses Python, and assumes no prior programming.
Below is a sampling of the weekly programming projects from Fall 2017 (incorporating the list, set, and dictionary Python data structures).

Using U.S. Geological Survey data (usgs.gov) students analyzed water usage of five types: public, domestic, industrial, irrigation, and livestock. Generating data such as this, as well as making a pie chart.

Daily we collected IP addresses from attacks on the CSE computer systems (using the most-significant numbers, a.k.a. Class C for privacy). We then asked the question: “What countries did the most attacks come from?” To answer that students had to merge information from three files: the attack IP addresses, a mapping of IP addresses to country codes, and a mapping of country codes to full country names, e.g. UK for United Kingdom). They then sorted by attack counts and plotted the top ten.

Using data from the National Hurricane Center (nhc.noaa.gov) students extracted trajectories and maximum wind speed by hurricane and plotted them.

Using Twitter data (twitter.com) of tweets using a selection of MSU related hashtags from MSU users, students wrote a program to address the following questions related to this data set: • “What are the most common hashtags used by users collectively?” • “What are the most common hashtags used by users as individuals?” • “How does hashtag similarity between two users vary over time?”