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This workshop addresses the unique challenges and opportunities of implementing active learning strategies in large, introductory lecture settings, which are crucial as they often mark the beginning of a student's journey into a specific discipline or way of thinking. Research in educational pedagogy supports the effectiveness of active learning, particularly in large classes. However, students in large classes might feel skeptical about engaging in active learning, given that large classes often rely primarily on lecture. In this workshop, we will explore strategies for incorporating active learning into large classes, as well as some techniques for engaging large groups of students in your rationale for using active learning.

This three-part interactive workshop series is a follow-up to D-Lab’s Python Fundamentals. It covers loops and conditionals, creating your own functions, analysis and visualization in Pandas, and the workflow of a data science project. Learning Objectives After completing Python Intermediate, you will be able to: Understand and write for-loops. Understand and write if-statements. Write your own functions. Do basic operations in Pandas, including simple visualizations. Understand the basic workflow for a data science project. This workshop does not cover the following: Navigating Jupyter Notebooks, assigning variables, data types, and error messages. These are covered in Python Fundamentals. Advanced DataFrame manipulation. This is covered in Python Data Wrangling. Advanced data visualization. This is covered in Python Data Visualization. Workshop Structure Python Intermediate has 3 parts. Each of the parts takes 2 hours, and is delivered in a lecture-style coding walkthrough interrupted by challenge problems and a break. Instructors and TAs are dedicated to engaging you in the classroom and answering questions in plain language. Part 1: Control Flow and Functions Part 2: Data Analysis and Visualization Part 3: Project

“Preparing for the First Year as Faculty” can help you land on your feet when you get to your new job. The panel will share insights into how to build a solid foundation for success, and what rewards and challenges you might encounter while in the first year(s) of a tenure-track job. You do not need to have an offer, or be on the job market, to benefit from this panel – all trainees interested in faculty careers are encouraged to hear speaker advice! Speakers are: Molly Kozminsky, PhD, Assistant Professor of Chemical and Biological Engineering, Iowa State University (former Berkeley postdoc) Jasmine Nirody, PhD, Assistant Professor of Organismal Biology and Anatomy, The University of Chicago (Berkeley PhD alum) Bronwyn Lucas, PhD, Assistant Professor of Biochemistry, Biophysics and Structural Biology, UC Berkeley Register to attend

Calling all digital humanities enthusiasts! If you are interested in presenting for this session please complete this google form. We encourage everyone to participate, regardless of your experience level. The DH Working Group is a welcoming and supportive community for all things digital humanities. About the Digital Humanities Working Group The UC Berkeley Digital Humanities Working Group is a research community founded to facilitate interdisciplinary conversations in the digital humanities and cultural analytics. Our gatherings are participant driven and provide a place for sharing research ideas (including brainstorming new ideas and receiving feedback from others), learning about the intersection of computational methods and humanistic inquiry, and connecting with others working in this space at Berkeley. We welcome grad students, faculty, and staff from all disciplinary backgrounds regardless of whether you are a beginner or an expert in empirical and data-driven methods. Working group meetings may include participants sharing work on current or future research, open discussions about theoretical, methodological or other challenges (e.g., data collection), invited speakers, and social mixers. Research at any stage of development (including nascent) is welcome for discussion. The Digital Humanities Working Group is led by Tim Tangherlini (Department of Scandinavian) and David Bamman (School of Information), and sponsored by D-Lab: Claudia von Vacano, Aaron Culich, Finley Golightly, and the UTech staff.

Geospatial data are an important component of data visualization and analysis in the social sciences, humanities, and elsewhere. The Python programming language is a great platform for exploring these data and integrating them into your research. Python Geospatial Fundamentals: Part 1 This workshop is the first one of the two-part series on using Python for fundamental geospatial analysis and visualization. After this workshop, you will be able to: Recognize different forms of geospatial data and coordinate reference system (CRS), Use GeoPandas and matplotlib libraries to map and analyze spatial data. Python Geospatial Fundamentals: Part 2 This workshop is the second one of the two-part series on using Python for fundamental geospatial analysis and visualization. After this workshop, you will be able to: Apply more advanced Python libraries for interactive visualization. Choose domain-specific spatial datasets to create your own maps.