My deep dive into data and ai began during the summer of the COVID-19 pandemic (2020). By this time, I had encountered and enjoyed the Zen of Python, but I hadn’t really used the language for building anything more than glorified “hello worlds!” and toy projects. I also had a strong curiosity about data and artificial intelligence.

And so, being stuck at home, I did what I love to do: learn. I enrolled in and completed IBM’s Python for Data Science, AI & Development course that summer. I was a philosophy major and English Language Arts educator venturing into the seemingly foreign world of data science and artificial intelligence. I enjoyed the course but felt that my background (strong in social science and humanities but lacking in mathematics or engineering) prevented me from continuing to pursue data science in any formal fashion.

So, I found a way to get into the master’s chambers, even if it was just for a peek. I had long considered librarians to be the alchemists of information of our time, and now, why not become one? So, instead of data science, I started my graduate program in information science in the Fall of 2020. However, after taking two ‘data’-related courses in the second semester of the program (Data Analysis and Knowledge Discovery and Data Visualization and Communication) and really enjoying them, I decided to transfer to the Advanced Data Analytics program. I considered it to be a path that was a type of ‘applied’ data science (or, at least, for me, a way in the data science door without the strong background in mathematics or engineering.).

Five years later and a Master’s degree completed in Advanced Data Analytics, the journey continues. It started with IBM’s Python for DS & AI course in the summer of 2020 and now extends to IBM’s Generative AI Engineering Professional path! Along with this program, I am also completing the Associate AI Engineering for Developers specialization.