final reflection

FINAL REFLECTION!

As I left the Data Science Fair this afternoon, I it hit me how much this course has exposed me to and taught me. 
I'll start with this past week. All of our groups had gotten switched up again. We were working with people we had never worked with before and the groups kept merging and separating as the class's vision changed. If this had happened at the beginning of the year, or even the middle of the semester, there would have been complete chaos. But this time there wasn't. People only panicked a little, we knew how to organize ourselves effectively using github and google calendar. Standing with other classmates today and looking at our completed poster, I knew we had at least accomplished that portion of the course - we knew how to collaborate. Even though the lack of instruction on how to use github (I know there was some, it was just all in class on a day where I had no computer to follow along) was stressful, github was absolutely vital and I am so glad that I worked through that frustration so that I know how to use these tools now. Collaboration was what I learned the most about, and after this class, entering graduate school where I will have to work with other students, professors and professors at otehr schools, I feel completely prepared. After managing and finding times to meet with my busy schedule with band and group members who went home every weekend, lived a couple of miles away or were just busy and did not always respond and then bringing back our "small group" to collaborate with the whole class on the whole project. everything else seems managable. 
I felt like I learned a lot about the theory of things being reproducible. I was sick during the class exercise concerning it, but I felt like I learned enough about the necessity of it in the scientific community by the lectures given by Aaron that I know I will not forget it when I do my own research. This was reinforced by my groups attempts to reproduce Luen's code to create Error Diagrams. We struggled over the variable names, the lack of comments, not knowing what many functions and variables were! This showed me that even when data and information is open (like we received in this class), it is not always easy to reproduce. These struggles cemented the idea that reproducibility is an absolute necessity in scientific research - and this is a lesson I will not forget.
The only thing I feel like I did not learn about was the Data Science. I tried to learn python, doing tutorials after my computer was fixed. I tried to understand the math that Professor Stark was presenting, but without more advanced statistics courses I couldn't. I wish I could have learned more of these tools as well in the class because I often felt like I could not help with those parts of the class work. I did what I could out of class, but I was busy so I had to miss many office hours opportunities to learn about the math - and my group was busy so that all of the coding was done at sporadic times, never together.
Overall, this class has altered the way I view research. I will take these lessons with me in my next journey. 
Thanks for teaching this class!