Day 2: Success on Deploying the Book and Start of Text Analysis

        My second day of this internship/research experience. From one side, today's experience seemed easier than the one on Friday, my first day, as I had an idea of what this process was going to look like. On the other hand, for people with standard 8-5 jobs, Mondays are generally harder than Fridays, and I truly felt it while getting out of bed at 7 am. While seemingly mundane, this learning curve is very important for me to mention as this is the first time I truly got to recognize the dread that the word Monday had been bringing all these years to the office workers in my life. All of my previous labor experiences were on a flex-schedule. With this knowledge, I plan to be better prepared for the next Mondays to come, of which I only have 7 left. Time flies.

        We started off the day with some group members rearrangement, during which some teams lost while others gained members. The Data Analytics group, my group, welcomed my fellow student and colleague Sandesh Lamichhane. During my first day on Friday, the team successfully launched the web application for the book (available at https://github.com/pearcej/httlads) after a few unfruitful attempts during the previous days. For future reference I wrote a step-by-step guide for deploying the web application, which Imma (Immanuela Belaineh) successfully tested, deploying the web-app locally on her Windows computer. The package download, however, kept throwing warnings and errors, about which I asked Brad Miller. After a conversation with Brad, I stayed after work to learn more about the importance of using virtual environments and added them to the guide. Today we tested the updated guide on Sandesh's computer with no errors or warnings throughout the process. When writing a guide, I followed some of the lessons from the Human-Computer Interaction course, such as reducing the cognitive load of a user by building on familiar patterns (language, bullet point order) and effectively using visuals for more effective pattern recognition (italicizing, bolding and properly spacing text from code.) I believe that this level of detail is what makes up an effective team.

        During the latter part of the day, the team read the material, marking what each member deemed as a change worthy. While some team members chose to skim the book, I thought it was important for me to read it slowlier with greater attention to detail. I made sure to watch the videos as well as read outside articles. I believe that as a future Teaching Assistant for this course I need to be well versed in all aspects of this textbook. This resonates with my strategy for the Intro to Data Science course preparation, during which I also edited the textbook and created assignments. I strongly believe that it was this approach that made a large positive impact on my ability to engage students at different levels during the course and my lab hours. While I chose this approach for tackling the challenge, I strongly support a diversity of ways in problem-solving. Those who chose to skim the book and move a bit faster are now able to provide a different perspective on the same challenge.

        We are the only group in this Open Source project with such high level, instructional work. While other teams mainly focus on the GUI of their web-apps, our team also tackles major material analysis work. This raises my only concern for the work we have been doing: I think we might need more instructions on the vision for the final textbook and overarching goals. I hope to wait and see for the vision to unfold as the work with the instructors continues, and raise my concern if the situation does not change.