When I started my thesis, I barely remembered how Python worked. I knew what a dataset was and how to print a line or write a simple loop, but that was about it. The idea of building an entire data-science workflow seemed far beyond what I could do on my own. Yet, a few months later, I had written a full pipeline to analyze hybrid work patterns using behavioral logs, location data, and daily surveys. What made that possible was Generative AI.
ChatGPT quickly became my silent collaborator. Whenever I got stuck, I simply described what I needed: filtering AWT data by time, merging JSON files by date, or running a Mann-Whitney U-test. Within seconds, it generated structured and readable code that actually worked. It helped me clean and merge datasets, calculate metrics like active work time and task switches, and even combine GPS data with behavioral data to label each day as home or office. Suddenly, something that felt completely out of reach became manageable.
Of course, the process was not perfect. I often had to debug the AI’s mistakes, rewrite lines of code, and verify that the logic fit my data. Sometimes ChatGPT used outdated Pandas functions or made assumptions that didn’t make sense. But those moments taught me more than any tutorial could. I started to understand not just what the code was doing but why it worked that way.
Looking back, Generative AI didn’t write my thesis for me; it expanded what I was capable of. It turned Python from something intimidating into a tool I could actually use. For me, that is the real power of AI. It doesn’t make you less of a coder; it makes you more confident to learn, experiment, and create things you once thought were impossible.
