Student Data Analysis

Analysis of high school students from different states in US who have internet connection at home.

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Inspiration

I really like analysis of things as it provides me with the meaning of the data. When I look at graphs such as histograms or plot-graphs it really intrigues me to go check out what it means, finding the limits to which the data can exceed and where it can fail. Its my hobby to collect various data-sets of students, brands, people who are part of some organisation and I like to draw the interpretation of what the particular set of information tells me about the certain group. So, this time I have collected a student data-set from UNICEF website which basically tells us the number of students in United States who have internet connection at their home.

What it does

It contains various insights and interpretations that can be drawn from the data given such as

  1. BoxPlot depicting the percentage of students having internet connection according to the income groups they fall in.
  2. Median Percentage with Internet Connection for Rural as well as Urban areas.
  3. Top 10 Countries with Highest Percentage of students having Internet connection at home for both Rural as well as Urban area.

How we built it

Language used is Python and for plotting graphs we have used python library MatPlotLib and for reading the data file we have used Pandas library.

Process:

  1. The data was provided in a raw csv file. Then it was cleaned.
  2. Python library matplotlib was used to draw plot-graph (Box Plot).
  3. For mathematical calculations of median we have used Pandas library.

Challenges we ran into

  1. Data cleaning - Data was not formatted, well structured and had duplicate values.
  2. We were not familiar with Python language and its libraries. So, the first day was spent learning the basics of python and its libraries.
  3. The final challenge was how can we present data in the structured format. (For example: What to show exactly when a certain query gets hit).

Accomplishments that we're proud of

  1. We were able to come up with the basic idea implementation on the first day itself.
  2. Plotting the exact data points on the graph was the turning point for the project as it gave us the hope that the project will be completed by deadline.

What we learned

  1. The basics are king. If you are well versed with the basics learning other stuff is a piece of cake.
  2. Always be prepared to face any challenge. You should atleast think about what might go wrong and be prepared with a plan to avoid it. ## What's next for Student Data Analysis We are thinking of using machine learning models to train them with data which will be very versatile and we will be using tkinter library for making the GUI.

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