We already know the ranks and the tuition fees per university (given). An interesting question to investigate would be to find what's the correlation of uni ranks with tuition and fees?
To carry out this, we first cat and cut out the Tuition and fees(col 4) and and Ranks (col 6) from the data into the new dataset called udata.csv:
Note that the redirection symbol (>), helped us to save the output. This data can simply be plotted using a scatterplot tool called scatter (install sudo pip install bashplotlib).Note that tail -n +2 exludes the first row i.e., column titles prior to passing the output all the way to end to scatter. However, this tool's output doesn't make much sense, as it doesn't show any x,y- axes legends. Therefore, we uploaded the data (udata.csv) to an online tool called plot.ly, which produced the following beautiful scatter plot:
It's a no brainer to understand from the plot above that highly ranked universities have higher tuition fees! However, the scatterplot also depicts one university (Brigham Young University--Provo) that had a higher rank (rank=68) with an extremely low tuition fees ($5300 USD p/a). Is this an anomaly (outlier) in the dataset? We leave the question for you to further investigate!
In this project we have learned to used some important bash commands like cat,head, tail,sort, uniq, cut, grep, etc. in the context mining a csv formatted toy dataset consisting of rankings of the US academic Institutes. We will re-use these commands in a more complicated format in the upcoming chapters.