Information Visualization in Grad School, Part 1

IUSOICRecently I’ve finished my final exam in my first graduate school course at Indiana – Information Visualization – and am beginning the final group project. I wanted to talk a little bit about my experience as a non-traditional student returning to graduate school part-time while working full-time.

According to Yi Deng, “We are facing a huge deficit in people — (with) the knowledge and skills to generate value from data — dealing with the non-stop tsunami. How do you aggregate and filter data, how do you present the data, how do you analyze them to gain insights, how do you use the insights to aid decision-making, and then how do you integrate this from an industry point of view into your business process? The whole thing is hugely important for the future.”

I chose to pursue an advanced degree from Indiana over earning self-taught credentials because I thought the transition back into the world with homework and assignments would be smoother for me with a more structured learning environment. So far that has been the case as I balanced the responsibilities of viewing the lectures, taking the quizzes and completed the homework after working all day. It has not been easy by any stretch of the imagination but rewarding so far. I think it helps that the topic of information visualization is very interesting to me, I can see real world applications and that I don’t have any spare time to NOT do my absolute best.

I chose data science because I have always loved learning and see the value that can be derived from information and am excited to see how this field continues to grow and evolve. I believe that information can change the world. After talking with some professionals in the field, I know I will eventually need to become an expert in one of the specializations – visualization, databases, machine learning, etc.

The first half of this course I spent learning theory and practicing it with hands-on assignments using the Sci2 data visualization open source tool. Studying the temporal, geospatial, topical and networks of scholarly datasets at the micro, meso and macro level has provided a great foundation for explaining data. Although there are a few quirks I didn’t like about the user experience in the Sci2 tool, it’s a commonly accepted and used tool by researchers and others at the NSF, USDA and NIH. You can find some of my data visualizations on my website and join me as I begin this grad school journey.

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