Professional Development Events

This interactive workshop series is your complete introduction to programming in R for people with little or no previous programming experience. It covers the basics of using RStudio, creating variables, working with data frames, and starting to analyse your data using summary statistics and data visualization. After completing this workshop series you will be able to: Navigate R Studio Open data in R and work with it in data frames using tidyverse Distinguish between different variable types Visualize data using ggplot Inspect documentation to deal with error messages R Fundamentals has 4 parts. Each of the parts takes 2 hours, and is delivered in a lecture-style coding walk through interrupted by challenge problems and a break. Instructors and TAs are dedicated to engaging you in the classroom and answering questions in plain language. The workshop series is structured as follows: Part 1: Introduction to R and RStudio Part 2: Data frames and variable types Part 3: Manipulating data frames Part 4: Data visualizations and custom functions

This three-part interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application. The complete Python Fundamentals series has 6 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. Parts 1-3 are intended for the complete beginner in Python. We will go over the basics of Python in Jupyter, variables and data types, and a gentle introduction to data analysis in Pandas: Part 1: Introduction to Jupyter and Python, Variables Part 2: Data Types and Structures Part 3: Introduction to Pandas After completing parts 1-3, you will be able to do basic operations in Python. You will know how to navigate Jupyter Notebooks, how to work with common data types and structures, methods, and basic operations in Pandas. You will have the minimum requirements to continue to other D-Lab workshops such as Python Data Wrangling or Python Data Visualization.

This interactive workshop series is your complete introduction to programming in R for people with little or no previous programming experience. It covers the basics of using RStudio, creating variables, working with data frames, and starting to analyse your data using summary statistics and data visualization. After completing this workshop series you will be able to: Navigate R Studio Open data in R and work with it in data frames using tidyverse Distinguish between different variable types Visualize data using ggplot Inspect documentation to deal with error messages R Fundamentals has 4 parts. Each of the parts takes 2 hours, and is delivered in a lecture-style coding walk through interrupted by challenge problems and a break. Instructors and TAs are dedicated to engaging you in the classroom and answering questions in plain language. The workshop series is structured as follows: Part 1: Introduction to R and RStudio Part 2: Data frames and variable types Part 3: Manipulating data frames Part 4: Data visualizations and custom functions

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.

Calling all digital humanities enthusiasts! End-of-the-year celebration! Lightning talk for our fifth DHWG meeting: “Tracking Microchanges: On the Evolution of the Novelistic Scene” - Nicholas Paige, Professor of French, presents an analytical examination of "scenes" in the novel from the early 1800s. 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

This three-part interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application. The complete Python Fundamentals series has 6 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. Parts 1-3 are intended for the complete beginner in Python. We will go over the basics of Python in Jupyter, variables and data types, and a gentle introduction to data analysis in Pandas: Part 1: Introduction to Jupyter and Python, Variables Part 2: Data Types and Structures Part 3: Introduction to Pandas After completing parts 1-3, you will be able to do basic operations in Python. You will know how to navigate Jupyter Notebooks, how to work with common data types and structures, methods, and basic operations in Pandas. You will have the minimum requirements to continue to other D-Lab workshops such as Python Data Wrangling or Python Data Visualization.

This interactive workshop series is your complete introduction to programming in R for people with little or no previous programming experience. It covers the basics of using RStudio, creating variables, working with data frames, and starting to analyse your data using summary statistics and data visualization. After completing this workshop series you will be able to: Navigate R Studio Open data in R and work with it in data frames using tidyverse Distinguish between different variable types Visualize data using ggplot Inspect documentation to deal with error messages R Fundamentals has 4 parts. Each of the parts takes 2 hours, and is delivered in a lecture-style coding walk through interrupted by challenge problems and a break. Instructors and TAs are dedicated to engaging you in the classroom and answering questions in plain language. The workshop series is structured as follows: Part 1: Introduction to R and RStudio Part 2: Data frames and variable types Part 3: Manipulating data frames Part 4: Data visualizations and custom functions

What is the relevance of the academy to achieving social justice? What does it mean to be a social change scholar? How can the academy be (re-)made to reflect the diversity and complexity of society, where students and communities have active voices and roles in shaping the pedagogy, research approaches, and policy production of the research university? For more than four decades, ISSI's Graduate Fellows Program has provided mentorship, training and support to doctoral students engaged in social change scholarship. This one-day symposium features the current first-year Graduate Fellows sharing their work in progress. Each panel includes one faculty respondent.

Many academics dream of sharing their research and expertise beyond the ivory tower, on media outlets such as the New York Times and NPR. However, the transition from academic to public writing can be intimidating for a variety of reasons--unfamiliarity with journalistic conventions, fear of being dismissed for making their work "too accessible" (as if that's a bad thing!), and above all, a lack of self-belief in one's expertise. In this webinar, I will talk about the mindset and mechanics behind transitioning from writing for fellow scholarly experts to writing for the public--from the art of pitching articles to landing a "big 5" publishing deal.

This three-part interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application. The complete Python Fundamentals series has 6 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. Parts 1-3 are intended for the complete beginner in Python. We will go over the basics of Python in Jupyter, variables and data types, and a gentle introduction to data analysis in Pandas: Part 1: Introduction to Jupyter and Python, Variables Part 2: Data Types and Structures Part 3: Introduction to Pandas After completing parts 1-3, you will be able to do basic operations in Python. You will know how to navigate Jupyter Notebooks, how to work with common data types and structures, methods, and basic operations in Pandas. You will have the minimum requirements to continue to other D-Lab workshops such as Python Data Wrangling or Python Data Visualization.

This interactive workshop series is your complete introduction to programming in R for people with little or no previous programming experience. It covers the basics of using RStudio, creating variables, working with data frames, and starting to analyse your data using summary statistics and data visualization. After completing this workshop series you will be able to: Navigate R Studio Open data in R and work with it in data frames using tidyverse Distinguish between different variable types Visualize data using ggplot Inspect documentation to deal with error messages R Fundamentals has 4 parts. Each of the parts takes 2 hours, and is delivered in a lecture-style coding walk through interrupted by challenge problems and a break. Instructors and TAs are dedicated to engaging you in the classroom and answering questions in plain language. The workshop series is structured as follows: Part 1: Introduction to R and RStudio Part 2: Data frames and variable types Part 3: Manipulating data frames Part 4: Data visualizations and custom functions

This workshop will start by introducing you to navigating your computer’s file system and basic Bash commands to remove the fear of working with the command line and to give you the confidence to use it to increase your productivity. And then working with Git, a powerful tool for keeping track of changes you make to the files in a project. You will learn to use Bash and Git together to synchronize your work across computers, collaborate with others, and even deploy applications to the cloud. In this workshop, you will learn the basics to understand and use Git, including working with the popular "social coding" website, GitHub where you can keep a private backup copy of your code or choose to publish it to the world.

This workshop will focus on the importance of having a strong professional support system and creating meaningful professional relationships. Students will learn how to effectively network and build a strong board of directors. After the session, students will have a better understanding of what social capital entails, how to network and utilize connections to leverage career success, and how to find and manage a professional and personal board of directors. Hosted by L.E.K. Consulting.

Mark your calendars for the 2nd Annual Teaching and Learning Conference to be held May 3.  This year’s theme is "Inclusive Teaching is Effective Teaching: Sharing Inclusive Teaching Strategies to Advance Equity". Registration and proposal submission information will be available in late January/early February. All members of the teaching and learning community - senate and non-senate faculty, GSIs, instructional staff, and undergraduate student instructors - are welcome to attend and submit a proposal.

The LLM Working Group is a community founded to facilitate conversations about Large Language Models (LLMs) and Generative AI within academia. This 4-part series will provide fundamental knowledge of LLMs, and generate conversation about the promises and challenges of LLMs in different facets of academic work. In the second session, Teaching with LLMs, Kimberly Vinall, Emily Hellmich, Genevieve Smith, and Ben Spanbock will lead a dialogue on the potential of LLMs in reshaping educational landscapes. It discusses educational challenges such as AI literacy, academic integrity, biases, hallucinations, and privacy issues, as well as opportunities such as accessibility and democratization. Questions we will be addressing include: How can we cultivate openness in class about students using LLMs and GenAI? Will LLMs fundamentally alter the importance of remembering knowledge and learning? Are LLMs fundamentally different from other information technologies like Wikipedia? How to cultivate openness in class about students using LLMs and GenAI? LLM working Group sessions will be interactive, encouraging participants to share their experiences, pose questions, and collaboratively explore the challenges and potential of these technologies in their respective fields. Please send in your questions ahead of time for priority consideration – you can use this Google Form to let us know what’s on your mind. We review all submitted questions but may need to shorten, consolidate, or clarify them for discussion. We encourage everyone to participate, regardless of their experience level with LLMs and GenAI. The LLM Working Group is a welcoming and supportive community for all. This is a hybrid event. In-person seating is limited to 35.