Docendo discimus ( "by teaching, we learn") is an old latin proverb that expresses an important philosophy about the learning process. The Dlab is one of the few communities that I have found at UC Berkeley that  fully embodies and champions this way of thinking. 


About  a year and a half ago, I walked testily into the D-Lab to participate in the fourth part of their Intro to Python workshop series. I had worked with Python loosely in the past but I did not understand a lot of the fundamentals and I could not find any community where I could learn these things without admitting my embarrassment. As I had advanced in my academic career, it became harder for me to vocalize my ignorance. During a conversation with an older graduate student about the latter problem, I was recommended to this session at D-Lab.  I was extremely skeptical in the beginning. I remember asking myself “Why should this place be any different?" However, as soon as the session began, I was amazed at how friendly and open the learning environment was. The instructor ensured that everyone was on the same page and would explain even the seemingly trivial concepts and questions. For the first time in a long while, I felt a lot more comfortable fully voicing my questions and my doubts.  I was even more impressed when at the end of such a good learning session, we were still asked to give feedback on the session and the instructor.  


I began taking more trainings in the D-Lab and fast forward to the present, I recently became even more integrated into the D-Lab community as a Data Science Fellow. The D-Lab Data Science Fellow role is relatively new and as such it allows a lot of flexibility. One of my primary interests is teaching and thus I signed up to teach one of the Python data science courses - Intro to Pandas. I have been using Pandas  for data analysis for some time now, but I only really began questioning some notation and concepts when I was preparing to teach the course at the D-Lab. I became obsessed with understanding every single detail about the module because I wanted to be able to answer all the questions accurately. As a student, I was only responsible for my own understanding but as an instructor, I started feeling the weight of each of my future students learning progress.


In the early days of preparation,  I believed that perfection was expected of me as a teacher which made the experience both challenging and frustrating.  I began doubting my knowledge and ability as the date for the first session is approaching. It was about that time  that I reached out to my mentor within the D-Lab to talk through the situation and help me prepare. I was amazed at his feedback and encouragement. He helped me realise that it was okay to be imperfect even when one is an instructor. I got out of the first meeting feeling energized and ready to take on teaching. My first teaching session is in a few days and I can now see how  my learning process has come full circle: learning , teaching and the learning some more. This iterative model has altered my approach to academic and professional development and will stay with me as I continue my career.


A few key things I am learning from the D-Lab community:

  • D-Lab embodies its famous motto, “It's Okay Not to Know” (IOKN2K) which in turn embodies the cycle of learning, teaching and then learning. It is okay even as an instructor to not have all the answers because learning is an iterative process.

  • It is never too late to start learning about data science. At the D-Lab, there are many courses and trainings that range from the introductory to more advanced levels. In addition, members of the community provide personal consultations in their areas of expertise. Finally, there are various working groups that provide interesting and targeted ways to learn and get involved.

  • Giving back is important to sustain our community and connecting to other students.


Pelagie Elimbi Moudio

Pelagie is a PhD student in the Industrial Engineering and Operations Research department. Her research interest lies in modeling decision support systems to aid natural resource management focusing on forests management. She teaches Python at the D-Lab.