College Information Visualization

For this visualization report, I used a colleges.csv dataset and my goal was to use this information to analyze different colleges and look at their general education results, costs, colleges’ admissions student qualities, and the demographic of students and their backgrounds. I chose this particular dataset because I thought it would be relatable being that I’m currently a student, so I have exposure to some factors of this data. I used this to analyze trends and draw observations and used tools like JavaScript, CSS, HTML, and Tableau to create an interactive visualization from the data.

Role: Designer, Programmer Timeline: ~ 1 week

Team: Individual Project Tools: JavaScript, CSS, HTML, Tableau

WALKTHROUGH

Along the x-axis when you see the visualization there are many important components of the data such as SAT results, ACT medium, data pertaining to students’ academic backgrounds as well as information about the school like the cost, student population, faculty salary, cost, and average family income. There is a filtering tool that allows the user to select which aspect of the data they want to look into.

In the next image you can see the change of the x axis from SAT Average to Admission rate. And as a result, the graph changes and different information is shown.

Among the data points there is also a distinction between public and private schools. The filled in solid circles are public schools and the empty circles with only the outline are private schools. This is important because there are many different factors such as cost and family income that can be affected based on this differentiation.

In the control bar, as shown below, the user has the option to filter to either all public schools or private schools, depending on what they are looking for.

Here the user chooses public schools which filters to that desired outcome.

Another possible analytic task that this visualization supports is differentiating colleges by region.

Down below demonstrates the capability of narrowing by a specific region. In this case, only the Southeast schools are shown, because that was the setting selected in Region.

Another function here is the tooltip. As shown, when the user hovers over a data point it shows the school. In this case, when hovering over the point, it shows California Maritime Academy. Another feature added was that the tooltip will show the user the information on the X value and Y value selected. For example, since Admission Rate was chosen for the X-Axis and Mean Earnings 8 Years After Entry was chosen for the Y-Axis, the data shown on the tooltip are respectively those: the admission rate and mean earnings 8 years after entry for that university.

Previously was when the user hovers on the point. However, when you actually click the data point, the school’s in-depth information is presented. In this case, Georgia Tech was the university focused on. It shows a more detailed view of the school’s information from region/control to the median debt and a Pi Chart of the racial demographic of the school.

Here, California Maritime Academy is selected instead showing the in-depth descriptions of that specific school. It is also an example of the user changing the X-Axis with ACT Median instead. As a result, the tooltip shows the ACT Median.

Here is another example of changing the axes with Y-Axis into Median Debt and the tooltip reflecting the x and y-axis chosen.

In general, for this visualization, we are showing the main visual which can display the trends and overall position of universities in comparison to other universities. The tooltips show statistics from both a general view to a detailed view. From the trends perspective, we can observe quickly which schools have less/more income, higher/lower SAT scores, etc. For example, under the SAT Average setting, we can observe that some schools have SAT values of 0, which is an exception and important information to consider. We can conclude that these schools didn’t consider SAT for their admissions.

Another trend we can observe is that public schools generally cost less than private schools. There are many trends we can obverse and when the user wants to get more specific about a certain school they can do so.

REFLECTION & LEARNINGS

When creating this visualization, I was able to get more experience using front-end languages like JavaScript, HTML, and CSS to create an effective visualization that portrayed important aspects of the college data. It was also interesting to view the results and filter out certain aspects that you don’t initially notice. It gave you a better understanding of the data and I enjoyed the interactive perspective.