Day 15: Research is Not Finding Results Until You Finally Do...

        I believe it is important to start this blog post by talking about my parents for the reader potentially worried about them. I called them in the morning and my mom reassured me that she and my dad are not experiencing any fever. This is really good news, however, it is still too early to celebrate as it has only been about 6 days since the symptoms started, and they are still experiencing fatigue and sinus congestion. After the call with my parents I started working on the challenge that was introduced to me yesterday: finding a dataset for Chapter 8 of the How To Think Like a Data Scientist Book. 
        At first glance, it seemed that this chapter was going to be the easiest one to find as it dealt with textual analysis. However, after closely examining the exercises in the book, I realized that the chapter very closely focuses on the existing dataset, which means that there is practically no room for dataset swapping without major changes needed to be made. With the previous chapters we were able to simply swap the datasets: clean and adjust the new, business-related dataset to resemble the old one, then write down the new objectives, and re-write the exercises to reflect those objectives. Swapping datasets in Chapter 8 was not possible, as we would need to re-write the core material, and add two other datasets as required by the content. Nevertheless, I searched the web for about 2 hours until I decided to talk to one of our instructors about the potential issue. During a conversation with Dr. Jan, I confirmed that the effort was not worthwhile and that it was easier for us to change the context of the material rather than change the dataset in this case. Tomorrow I will have a conversation with Dr. Boggs, MIS professor at Berea, about adjusting the context of the chapter to be more business-related.
        While searching for a dataset, I also spent some time with Sama and Roberto, our new team members, on getting them familiarized with Jupyter Notebook interface and Altair library. It was important for me to share some of the insights I had on the tools, reflecting on the confusions I had when starting with JN. I believe that sharing a personal story of working with a tool, rather than raw instructions, creates affirmation in people who are just starting. It is a big part of my teaching philosophy - to humanize myself as a learner before I can teach.
        At the end of the day, I had a 30-minute long conversation with Dr. Jan on my experiences with online teaching format as a Teaching Assistant and content contributor to the first Berea College CS course, Intro to Data Science, earlier in May. This conversation was important as it helped me understand what exactly we are trying to achieve with the books we are working on during the summer institute. I also got a better understanding of the tools, such as cloud environments, that we will need to implement for all of our classes in the Fall. Somehow everything I have done since the pandemic started ties together in this project, and I cannot help but think of it as a good omen for my career in Data Science.

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