Reinvesting in Myself during the AI Gold Rush
A little more than a year ago — amidst the ChatGPT craze — I made the decision to reinvest in myself in the fast-evolving world of AI. I took a career break to join the University of San Francisco’s Master of Science in Data Science (MSDS) program, located in San Francisco, right at the heart of the latest Gold Rush in AI. They say the real winners of a Gold Rush are those who sell picks and shovels, but I decided to take goldsmithing classes instead.
I couldn’t have made a better decision to sharpen my technical chops. Although it had been at least a handful of years since I last opened Visual Studio Code, the summer bootcamp ramped me up to speed — revisiting foundations in programming and statistics, such as list comprehension, hash map, and the Central Limit Theorem.
The program was well designed to gradually build up our knowledge, with each subsequent course piggybacking off of our new skills learned in the preceding courses. After establishing a solid foundation in programming and statistics, we learned to extract appropriate data from publicly available websites via web crawling (and sometimes getting blacklisted) and started dipping our toes in machine learning basics — decision trees, naive bayes, and gradient descent. We came a long way, finishing up the course with the latest developments in AI, including LLM with RAG, GenAI.
My classmates and I brought all our learnings together with our projects in the Data Science Entrepreneurship course where we built MVPs to solve real-life problems, serving and deploying our models in a scalable manner on GCP or AWS, thanks to our Data Engineering and DevOps courses.
I was fortunate enough to intern with How We Feel, a non-profit organization with a mobile app that helps people improve their emotional intelligence. The team included amazing talent, such as founding and early members of Pinterest, ex-Meta, and ex-McKinsey. Some notable projects I worked on at How We Feel included building a data lake of user logs, designing and analyzing multivariate tests for new feature launches, and building a recommendation algorithm for content discovery. With projects spanning from data engineering, data science, and machine learning, I feel fully equipped in understanding the nuances faced by my engineering counterparts.
I also had the privilege of serving as the first Class President of the MSDS program. I’m proud to have organized events and programs to bring the class together, such as our first annual pickleball tournament, in collaboration with faculty and staff while also representing the students’ voice in designing the program.
It’s bittersweet that this is my last week at USF’s MSDS program. While I’m sad to no longer be able to learn full-time, I’m eager to bring my new skills to the industry.
But this is not the end of my journey as a student. I may no longer have the title of a full-time student, but I look forward to learning about the latest cutting-edge technologies in the industry.
