Lecture Series

The Lecture Series will bring Data Science speakers to UCSC and CSUMB. These lectures will be open to the public and aim to increase the awareness of Data Science programs, research, pedagogy, and implementations among our community.

24-25 Series

Valerie Carr - SJSU Carr Lab Investigating Memory and the Brain

Valerie Carr earned a BS in Biological Psychology from the College of William and Mary and a PhD in Neuroscience from UCLA. As a PhD student and later as a postdoctoral fellow at Stanford University, Valerie used structural and functional MRI to investigate the neural basis of episodic memory in both younger and older adults. As a Professor at SJSU, Valerie's goal is to leverage her cognitive neuroscience background to elucidate strategies for improving brain health, memory, and executive function across the lifespan. She teaches courses relating to neuroscience, learning and memory, and computer programming. Regarding the latter, she is actively involved in pedagogical research examining optimal strategies for teaching interdisciplinary computing.

Public Lecture, April 7th, 2025

Applied Programming Experiences (APEX): Embedding interdisciplinary computing modules into introductory biology and statistics courses

Interdisciplinary professionals with both domain and computing skills are in high demand in our increasingly digital workplace. Universities have begun offering interdisciplinary computing degrees to meet this demand, but many community college students are not provided learning experiences that foster their self-efficacy in pursuing them. The Applied Programming Experiences (APEX) program aims to address this issue by embedding computing modules into introductory biology and statistics courses at community colleges. In this talk, I will describe how we developed these modules, train faculty to use them, provide continuing professional development for faculty adopting the modules, and how we assess students. Ultimately, we aim to encourage nationwide adoption of embedding computing into popular introductory community college and lower division courses.

Jacob Bien - USC Marshall School of Business

Jacob Bien is Professor of Data Sciences and Operations (Statistics group) at the Marshall School of Business, University of Southern California.

Public Lecture, February 10th, 2025

Generative AI for Data Science 101: Coding Without Learning To Codeinterdisciplinary classes across campus.

Should one teach coding in a required introductory statistics and data science class for non-major students? Many professors advise against it, considering it a distraction from the important and challenging statistical topics that need to be covered. By contrast, other professors argue that the ability to interact flexibly with data will inspire students with a lasting love of the subject and a continued commitment to the material beyond the introductory course. With the release of large language models that write code, we saw an opportunity for a middle ground, which we tried in Fall 2023 in a required introductory data science course in our school's full-time MBA program. We taught students how to write English prompts to the artificial intelligence tool Github Copilot that could be turned into R code and executed. In this talk, we report on our experience using this new approach.

23-24 Series

Eric Van Dusen - UC Berkeley

Eric Van Dusen is Outreach and Tech Lead and Lecturer at the College of Computing, Data Science, and Society at UC Berkeley.

Public Lecture, April 29th, 2024

Data Science Modules, how UC Berkeley has built interactive computing into interdisciplinary classes across campus.

This talk will illustrate the world of Data Science Modules and how UC Berkeley has seamlessly integrated interactive computing into classes across the entire campus. The core of this initiative revolves around leveraging the power of data science to enhance educational experiences, making it more hands-on and applicable to real-world scenarios. UC Berkeley's approach involves embedding short, flexible modules into existing courses across various disciplines. This method allows students from diverse academic backgrounds to gain practical skills in data analysis, visualization, and computational thinking without the need for a full data science course. Ubiquitous access to interactive computing, through Jupyter notebooks, enables students to execute code, analyze data, and visualize results all in one place. This interactive platform not only makes learning more engaging but also directly applicable to solving complex problems in fields ranging from social sciences to natural sciences and beyond. By incorporating these modules , UC Berkeley is not just teaching students how to use data science tools; it's empowering them to apply these tools to their areas of study. This interdisciplinary approach demystifies data science, making it accessible and valuable to all students, regardless of their major.

Teaching Seminar, April 30th, 2024

Teaching with the Data Science Stack

The talk explores the transformative potential of Jupyter notebook-based instruction, focusing on the integration of a set of open source tools used to teach at scale in dozens of classes at UC Berkeley. At the infrastructure level, the campus JupyterHub facilitates universal access to both Notebook and Lab environments, and an RStudio environment. This allows for a more dynamic and interactive teaching and learning experience, making it easier for students and educators to easily engage with interactive computing in any class . Additionally, the talk highlights the utility of nbgitpuller in simplifying the distribution of materials by seamlessly pulling content from Git repositories into Jupyter environments. Teaching at scale also involves the use of automated grading systems, with a focus on tools like Otter Grader and Gradescope. . This talk aims to showcase the myriad ways in which notebook-based tools and complementary technologies can revolutionize the educational landscape, particularly in STEM fields.

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