Data Science for the Common Good

This week, we are proud to spotlight McCarthy Fellow in Sacramento Liana Mendoza ’27 who  has dedicated her 12-week summer as an intern for the California Health and Human Services. Liana shares a key take-away — where data science meets policy-making and public service. Read more about Liana’s journey as an intern and how it has influenced her future career aspirations. 

In a fast-track world, technology continues to grow into a double-edged tool, particularly in the world of policy-making and community service. As a McCarthy Fellow, I was given the opportunity to explore how data science and analysis can be utilized in creating programs for the common good. At first, I was hesitant to apply because I was unsure how I could be of value to a space that was rooted in policy-making and human-centric work. However, I was given tremendous space to explore through my internship with the California Health and Human Services Agency, particularly in the Office of Patient Advocate (OPA). 

One of my primary goals in this experience is to see the intersection between community service and data science. Through this, I hoped to gain more clarity about the career I want to pursue. What I am most sure of is my pursuit of data science that is grounded in social justice and politics.

This experience taught me how data science must be politically engaged.

One of the main initiatives of the department is the patient report cards. These report cards aim to create platforms to provide accessible information regarding medical groups and health plan ratings. Through this project, the office aims to aid residents in making well-informed decisions about their healthcare choices.

Guided by my supervisors in OPA, I was allowed to explore other ways of determining patient satisfaction ratings for different medical groups in California. I was determined to utilize the opinions of residents on social media platforms to generate honest ratings. My supervisors advised me to gather data from different platforms such as Yelp. 

Fortunately, I have taken classes at the University of San Francisco, which has given me a good foundation of the technical tools I need for the internship. I was able to create a program that takes data from online platforms, particularly Yelp, and translate it into numerical ratings. Through a mechanism called sentiment analysis, I was hoping to make a flexible tool that can analyze data from different platforms. Then, I constructed different cutpoints to translate this data into a 5-star rating system, similar to what is currently published on the OPA platforms. 

“Data science” and “machine learning” can be daunting words especially when involving policymaking and public service. This experience taught me how data science must be politically engaged. More particularly, it is important that data scientists partake in an intentional and deliberate approach to their craft. 

Aside from taking into consideration the technical aspects, such as coding and statistical analysis, it is also important to choose what problems to work on and how to pivot them towards serving an advocacy. This involves rigorous checkpoints at each stage of the process— there is an urgent need to partake in data science not to develop or strengthen new technology, but to aid in creating solutions to sociopolitical issues.