Cody and Robert speak with Shan Wang, the Program Director for the Master of Science in Data Science program at University of San Francisco, about data science education and where it may be going during these quickly changing times.
In the final episode of Cohort 12's student series, we talk with Ian Duke and Tatshini Ganesan about their year in the MSDS program. Ian discusses his practicum at the ACLU training ML models on massive amounts of police body cam footage to flag videos for specific topics like searches or arrests. Tatshini shares about her experience working with Vibrant Data Labs to disseminate climate change-related financial data to companies and researchers who need it. They also give their insights on working in hybrid environments, navigating the work/life balance, and forming community and friendships in MSDS.
In this episode we talk to two more students from our most recent cohort, Bassim and Rishi, about their experiences in the MSDS program. Bassim shares some insights into how he has optimized his job search process with some noticeable success, and Rishi walks us through his data engineering practicum and decision to enroll in the data engineering concentration.
In this episode we talk to two of our graduating MSDS students, Amy and Rashmi, about their experiences in the MSDS program, their strategies for selecting their practicum companies, and the things that most surprised them about getting their MS degrees in Data Science at USF.
The top three winners of our Advanced ML course Kaggle competition join us to share their strategies and lessons learned from training models on real estate listing price data from houses in Spain. Hear how they navigated the decisions involved in model building, including feature engineering, the curse of dimensionality, and the challenge of handling text descriptions in a foreign language.
In this episode, we chat with Kekona Sorenson, a versatile data science leader at Microsoft. Kekona shares insights on his team’s role in improving Microsoft products through data analysis and experimentation. We also discuss the importance of cultural shifts in data-driven decision making and the need for effective communication in Data Science. According to Kekona, "the thing that separates an average Data Scientist from a great Data Scientist is your soft skills". He also highlights the importance of building trust with stakeholders, and he attempts to sell the role of Product Data Science to an audience enraptured by all things Gen AI.
In this episode, we speak to Nico Thiébaut, a seasoned machine learning engineer with a PhD in Physics. Nico shares his experiences working on the popular gaming platform Roblox, highlighting the platform's growth and the challenges of creating engaging, user-generated content. We also discuss the role of machine learning in game development and the ethical complexities of content moderation on large platforms. Our hosts, Cody Carroll and Robert Clements add their insights on human moderation and the importance of soft skills in consulting.
In this episode, we speak to Sundar Dorai-Raj, a seasoned data scientist at Google and MSDS faculty at USF. He shares about his experience working on Bard/Gemini and the constraints and freedoms of working at a major tech company. We also have a discussion about the place of LLMs and Generative AI in academia — for both students and professors.
In this episode, MSDS faculty Mustafa Hajij gives a beginner-friendly introduction to topological deep learning. We discuss the foundations and some applications of his research, including graphs, social media networks, and chemical interactions of drugs. He also shares his take on some new developments in Generative AI and discusses how he incorporates emerging topics in deep learning into the classes he teaches at USF.
In this episode we chat with Hadley Dixon, a student in the Bachelor of Science in Data Science program at the University of San Francisco. She tells us about how she made the decision to major in Data Science after taking a data ethics class and we discuss dealing with imposter syndrome as both students and experts of data science. We also give her a pop quiz about SQL, EDA, and Deep Learning.
In this episode, we sit down with Matt Wheeler, who works as a Data Engineer at PG&E and is an alumnus of the MS in Data Science program at the University of San Francisco. Matt talks about his move from the UK to the US and the process of integrating into the American Data Science industry. He shares his insights on the dynamic field of Data Engineering and gives us a peek into USF's Data Engineering program. Reflecting on the importance of practicums, Matt highlights how these applied experiences are crucial in shaping a successful career in Data Science and Data Engineering. This episode offers a blend of personal stories and professional insights, providing a unique window into the evolving world of Data Science.
In this episode, we chat with Yannet Interian, an Associate Professor of Data Science at University of San Francisco. Yannet reflects on her academic and professional odyssey from Mathematics to Data Science, a journey which takes her from Cuba through Europe and eventually to the Bay Area. We also discuss the value of mentoring and teaching young data scientists and how to best guide students to success in their careers.
In this episode, we engage in a conversation with Scott Coffin, who shares his extensive experience in Data Science at the California State Water Resources Control Board. During our discussion, we discuss data science in the context of microplastics and their far-reaching impacts on aquatic organisms and human health. Scott also shares strategies and opportunities for aspiring Data Scientists looking to build a rewarding career in the government sector.
In this episode we talk to two of our graduating MSDS students, Patricia and Ben, about tracking fitness workouts and researching neural network training strategies as part of their MSDS Practicum projects, and dealing with imposter syndrome and other challenges when moving to SF and completing a one-year intensive data science program.
Hosts: Cody Carroll and Robert Clements
Guests: Patricia Ornelas Jauregui and Ben Chen
Editing: Cody Carroll
Mixing: Joshua Lehrer
In this episode we talk to two of our graduating MSDS students, Jessica and Xin, about identifying tweets about California Black Bears and monitoring "AirBnBs" for birds in California that are used for bird migration as part of their MSDS Practicum projects, and getting through an intensive one-year MS program while parenting kids.
Special thanks to Joshua Lehrer for editing this episode.
In this episode we talk to two of our graduating MSDS students, Rushil and Guru, about their experiences in the MSDS program, living in the SF Bay Area, exploring the city in autonomous cars, and the best Indian food in SF.
In this episode we talk to two of our graduating MSDS students, Chenxi and Kyle, about their experiences in the MSDS program, their Practicums in Agriculture and Risk Intelligence, moving to SF, and building an app for summarizing restaurant opinions in the Entrepreneurship course.
Cody Carroll is an Assistant Professor in the MSDS program at USF, and talks about his background, his teaching, and his time-warping research. He's also a DJ and the songs you hear in this episode were chosen by him, and they are (in this order): Sunseeker by Zuma Dionys; Epic Resurrection by Space Motion; and This Time Baby by Jackie Moore.
Paul Kim talks about his journey studying Film, TV, Media Studies, and Data Science, and how these topics will forever be interrelated.
Learn more about the MSDS program at the Data Institute here:
MSDS: https://www.usfca.edu/arts-sciences/programs/graduate/data-science
MSDS: https://twitter.com/usfca_msds
Data Institute: https://twitter.com/DataInstituteSF
Data Institute: https://www.linkedin.com/company/the-data-institute-university-of-san-francisco/
Recent MSDS graduate Kooha Kwon talks about his favorite part of the MSDS program at the University of San Francisco as well as his job search process, successes, and failures since graduating.