Upcoming Events

This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets. No theory instruction will be provided. Prerequisites: D-Lab’s Python Fundamentals introductory series or equivalent knowledge. Workshop materials: https://github.com/dlab-berkeley/Python-Machine-Learning-Fundamentals(link is external) Software Requirements:Installation Instructions(link is external) for Python Anaconda Date & Time: This online workshop is a 2-part series running from 2pm-5pm each day: • Monday, October 2 • Wednesday, October 4  

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. This is a two part online workshop. Geospatial Data and Mapping in Python, Part 1: Getting started with spatial dataframes (Oct 3rd) Part one of this two-part workshop series will introduce basic methods for working with geospatial data in Python using the GeoPandas library(link is external). Participants will learn how to import and export spatial data and store them as GeoPandas GeoDataFrames (or spatial dataframes). We will explore and compare several methods for mapping the data including the GeoPandas plot function and the matplotlib library. We will review coordinate reference systems and methods for reading, defining and transforming these. Note, this workshop focuses on vector spatial data. Geospatial Data and Mapping in Python, Part 2: Geoprocessing and analysis (Oct 5th)  Part two of this two-part workshop series will dive deeper into data driven mapping in Python, using color palettes and data classification to communicate information with maps. We will also introduce basic methods for processing spatial data, which are the building blocks of common spatial analysis workflows. Note, this workshop focuses on vector spatial data. Knowledge Requirements You'll probably get the most out of this workshop if you have a basic foundation in Python and Pandas, similar to what you would have from taking the D-Lab Python Fundamentals workshop series. Here are a couple of suggestions for materials to check out prior to the workshop. D-Lab Workshops: Python Fundamentals Pandas Workshop Materials: https://github.com/dlab-berkeley/Python-Geospatial-Fundamentals Software Requirements:Installation Instructions for Python Anaconda  

Participants in this workshop will learn about some of the issues surrounding the collection of health statistics, and will also learn about authoritative sources of health statistics and data. We will look at tools that let you create custom tables of vital statistics (birth, death, etc.), disease statistics, health behavior statistics, and more. The focus will be on U.S. statistics, but sources of non-U.S. statistics will be covered as well. Whether you need a quick fact or a data set to analyze, this workshop will lead you to relevant data sources. No prior knowledge is required for this workshop. Please attend if you have any interest in health statistics and data sources. Have a laptop with you to follow along. Prerequisites: None. Workshop Materials: Workshop Step by Step PDF (or DOCX) and slides (PPTX).

As colleges near the demographic cliff, they’re looking for ways to attract more students while fulfilling their missions. For many colleges, this will very likely include teaching incarcerated and formerly incarcerated students. What do faculty members need to know to best serve this population’s needs? “The Faculty Experience With Incarcerated Students” will feature a panel of experts ready to offer advice about what to expect and share tips for professors and administrators alike. How does educating incarcerated students differ from teaching traditional students? What unique needs do these students have regarding special support services? What else do faculty members need to know? Join us for a Virtual Forum on Wednesday, October 4, at 11am PT, to find out. Register now.  

High-achieving students from low-income and working-class backgrounds are less likely than their middle- and upper-middle class peers to submit applications to the nation’s top colleges and universities. In Unequal Choices, Professor Yang Lor examines the college application choices of these students and situates their decisions within the larger context of the family, school, and community. Lor identifies several mechanisms behind the reproduction of social inequality, showing how institutions and families of the middle- and upper-middle class work to procure advantages by cultivating dispositions among their children for specific types of higher education opportunities. As a result, middle-class youth apply to leading universities and colleges across the country because they are told from multiple sources that such colleges are appropriate and because they understand college as an opportunity to accumulate new experiences. In contrast, low-income and working-class students tend to limit their choices to colleges and universities close to home, even when they have knowledge about top colleges, because they understand college as a continuation of family interdependence that requires them to factor in their family needs. Social class differences in where students submit college applications are shaped not only by access to information but the context under which such information is received and the life experiences students draw upon to make sense of higher education. Institutional contexts like high schools and college preparation programs shaped the type of colleges that students deemed appropriate for them, while family upbringing and experiences influenced how far from home students imagined they could apply to college.

The Berkeley Career Engagement Office  is pleased to invite you to the in-person 2023 Master's & PhD Career Fair. Presented since 2002, this fair is exclusively for graduate students. Recruiters will be seeking Masters, PhD, and postdocs for a wide variety of roles and industries. (MBA and JD students are not recruited at this event, please contact your respective career services office for other recruitment opportunities.) Registration is not required. This fair is only open to currently enrolled UC Berkeley students and eligible alumni with a current Handshake account. Be sure to bring your UC Berkeley Student ID and copies of your updated resume to the fair. Business casual attire is recommended. All majors and degree levels are welcome to attend. IMPORTANT HEALTH GUIDELINES This event is subject to current Public Health Guidelines at the time of the fair, which may include, but is not limited to: wearing an approved mask or other safety measures. For up to date information, please refer to this page: https://uhs.berkeley.edu/coronavirus-covid-19-information PLEASE NOTE THE FOLLOWING: Students cannot drop-off or leave any personal belongings at the fair or with the Berkeley Career Engagement Staff, including backpacks, helmets, or any form of transportation. Printing services will not be available (click here to view places to print on campus) Beverages or food will not be available for students

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. This is a two part online workshop. Geospatial Data and Mapping in Python, Part 1: Getting started with spatial dataframes (Oct 3rd) Part one of this two-part workshop series will introduce basic methods for working with geospatial data in Python using the GeoPandas library(link is external). Participants will learn how to import and export spatial data and store them as GeoPandas GeoDataFrames (or spatial dataframes). We will explore and compare several methods for mapping the data including the GeoPandas plot function and the matplotlib library. We will review coordinate reference systems and methods for reading, defining and transforming these. Note, this workshop focuses on vector spatial data. Geospatial Data and Mapping in Python, Part 2: Geoprocessing and analysis (Oct 5th)  Part two of this two-part workshop series will dive deeper into data driven mapping in Python, using color palettes and data classification to communicate information with maps. We will also introduce basic methods for processing spatial data, which are the building blocks of common spatial analysis workflows. Note, this workshop focuses on vector spatial data. Knowledge Requirements You'll probably get the most out of this workshop if you have a basic foundation in Python and Pandas, similar to what you would have from taking the D-Lab Python Fundamentals workshop series. Here are a couple of suggestions for materials to check out prior to the workshop. D-Lab Workshops: Python Fundamentals Pandas Workshop Materials: https://github.com/dlab-berkeley/Python-Geospatial-Fundamentals Software Requirements:Installation Instructions for Python Anaconda  

The complexity of a college or university requires that its leadership have constant availability; thus, leaders are always representing the institution. How do leaders—women in particular—craft and maintain a healthy work-life balance? With many responsibilities, presidents of higher education institutions often have workdays of 12 hours or more, plus weekend commitments. Campus safety, enrollment, strategic financing, crisis management, accreditation, student life, athletics, alumni affairs, learning outcomes, academic freedom, and community engagement are just some of the day-to-day obstacles leaders face during their tenure. While challenging, the job is also rewarding thanks to the accomplishments of graduates, the philanthropic support of donors, and the national and international recognition of faculty and staff. Join us for the newest event in the 2023 Women’s Leadership Speaker Series as our panel discusses women in leadership and how to best support their well-being.

As colleges and universities across the country face declines in undergraduate student enrollments, institutions are shifting more resources towards student success: How do we keep our current students on the path towards degrees? But student needs and expectations are constantly shifting. Many more students today face mental-health conditions like depression and anxiety, prompting an increase in mental-health resources from colleges. And, several colleges are turning to new tools and strategies to improve connections with students — with the hope of boosting student engagement. On Thursday, October 5th at 11am PT, join The Chronicle of Higher Education for “Supporting Student Success: Tools and Strategies”, the first part of a Virtual Forum series exploring the strategies and systems that colleges are using to support student needs, including AI-powered chatbots and data analytics, and how you can implement them to support students in the 2023-2024 academic year.

Without leaps of imagination, or dreaming, we lose the excitement of possibilities. Dreaming, after all, is a form of planning. ~Gloria Steinem If you haven’t decided yet on a career direction or begun to set goals, this session can help you get started. If you already know or have an idea of what you want to do, it can help you refine your vision and goals. When planning your future, relying on rational, logical thinking sometimes isn't enough; letting your intuition, imagination, and emotions guide you can help you forge your path. In this session, you will have the opportunity to envision a possible future by looking inward, reflecting on past experience, tuning into what you want, and projecting forward. You will use the insights you gain to formulate goals and identify tangible steps to reach them. All majors and class levels are welcome.

Are you starting a research project at UC Berkeley that involves human subjects? If so, one of the first steps you will need to take is getting IRB approval. Numerous questions can come to mind when first negotiating getting a project approved. When should you apply? Does your project require review, and if so, what kind? This course will walk you through the process of getting IRB approval for your project. After this course, you will approach getting your project approved with confidence that will save you time and avoid potential delays. Specifically, we will: Describe the origins of the institutional review board and its purpose. Introduce you to IRB at UC Berkeley. Walk through the different types of protocols to determine the best one for your project. Create a protocol and explain in-depth the different portions that make up your eprotocol. Discuss the review and amendment process. Prerequisites: None Workshop Materials:  IRB slides and https://github.com/dlab-berkeley/IRB-Fundamentals

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 Prerequisites: D-Lab's Python Fundamentals (6 hours) series or equivalent introductory Python knowledge. Please take the D-Lab Skill Check to see if you are ready for Python Intermediate! Workshop Materials: https://github.com/dlab-berkeley/Python-Intermediate

Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python. In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis. We will cover: Pandas data structures Loading data Subsetting and filtering Calculating summary statistics Dealing with missing values Merging data sets Creating new variables Basic plotting Exporting data Prerequisites: D-Lab’s Python Fundamentals introductory series or equivalent knowledge. GitHub Repository: https://github.com/dlab-berkeley/Python-Data-Wrangling(link is external) Software Requirements:Installation Instructions(link is external) for Python Anaconda

This workshop is a three-part introductory series that will teach you Stata from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the Stata software, understand data and basic manipulations, import and subset data, explore and visualize data, and understand the basics of automation in the form of loops and functions. After completion of this workshop you will have a foundational understanding to create, organize, and utilize workflows for your personal research. Each of the parts is divided into a lecture-style coding walkthrough interrupted by challenge problems, discussions of the solutions, and breaks. Instructors and TAs are dedicated to engaging you in the classroom and answering questions in plain language. Part 1:  Introduction Loading datasets into Stata (no previous knowledge expected) Examining a dataset and finding variables of interest Summarizing and tabulating variables Stata specific tools and resources (do files, logs, help files, etc.) Coding and cleaning data (making new variables from old variables; labeling variables and values, etc.) Using logical operators in Stata Cross-tabulations Part 2: Data Analysis in Stata Correlation T-tests Ordinary Least Squares (OLS) and logistic regression (basic syntax, using interaction terms, interpreting output) Visualization (histograms, bar graphs, scatter plots) Regression postestimation (getting predicted values, basic graphs) Merging and appending datasets Part 3: Stata Programming Local and global variables (macros) Looping (foreach, forvalues) Reshaping data between wide and long formats Recalling and using command output Generating nicely formatted journal-style tables Workshop Materials: https://github.com/dlab-berkeley/stata-fundamentals Software Requirements: Installation Instructions (note: UC Berkeley students will receive an email with an instructional license for the workshop)

Come explore the variety of career and internship possibilities found among Non Technical roles within the Tech industry with alumni & professionals working in various roles. Students of all majors are welcome! Light refreshments will provided. Features speakers are listed below: Joseph Ham-Silvestre- Culture & People Strategy Senior Program Manager at Marqeta Nile Taylor- Senior Program Manager at Google Sara Oh- Technical Content Strategist at Medidata Solutions Isra Ahmad-Quant Research at Twitch Todd Tran- Co-Founder at Lumebox Tina Cheung- Program Manager/Talent Manager at Visa Heyun Jeong- Influencer Marketing Lead at Juniper Networks Kevin Ji- Corporate Sustainability Analyst at Western Digital Cyril Anyaoha- Realtor at Compass

Explore the crucial steps and insights required to bridge the gap between student learning assessment and impactful research within the field of Virtual Exchange/Collaborative Online International Learning (VE/COIL). In this webinar, the panelists will share their experiences and strategies for utilizing assessment data to create robust research findings that enhance student learning and global education. 8:00 a.m. EDT/5:00 a.m. PDT.

Anyone pursuing a faculty career should understand the conventions of the academic job market. In this panel, UC alumni and current faculty in the biosciences will share their experiences as candidates and on hiring committees for teaching focused roles. We will discuss what trainees should know to better navigate the hiring process. Speakers include: Natalia Caporale, PhD, Associate Professor of Teaching, Department of Neurobiology, Physiology and Behavior, UC Davis Katie Hart, PhD, Assistant Professor of Chemistry at Williams College Cary Lai, PhD, Professor of Biology and Program Director, Biotechnology PSM, University of San Francisco Register here

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 Prerequisites: D-Lab's Python Fundamentals (6 hours) series or equivalent introductory Python knowledge. Please take the D-Lab Skill Check to see if you are ready for Python Intermediate! Workshop Materials: https://github.com/dlab-berkeley/Python-Intermediate

Do you often feel drained by departmental drama? Do feel unclear how to handle conflicts that arise in your department? Are you unsure when, where, and how to manage conflict with people who will be voting on your tenure? Academics are notoriously conflict avoidant and the inability to manage conflict can result in negative physical, emotional, and relational consequences for tenure-track faculty. So why not learn early in your career to master the SKILL of healthy conflict so that you can effectively manage conflicts as they arise and avoid carrying around all of the negative energy, anger, and resentment in your mind and body. In this webinar, you will learn: How conflict-management is an essential part of thriving in the Academy. How to decide when to push back and when to pull back in the face of conflict. The difference between healthy and unhealthy conflict. How to get clear about the role that power plays in resolving departmental conflicts. Ten tips for engaging in healthy conflict

This workshop is a three-part introductory series that will teach you Stata from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the Stata software, understand data and basic manipulations, import and subset data, explore and visualize data, and understand the basics of automation in the form of loops and functions. After completion of this workshop you will have a foundational understanding to create, organize, and utilize workflows for your personal research. Each of the parts is divided into a lecture-style coding walkthrough interrupted by challenge problems, discussions of the solutions, and breaks. Instructors and TAs are dedicated to engaging you in the classroom and answering questions in plain language. Part 1:  Introduction Loading datasets into Stata (no previous knowledge expected) Examining a dataset and finding variables of interest Summarizing and tabulating variables Stata specific tools and resources (do files, logs, help files, etc.) Coding and cleaning data (making new variables from old variables; labeling variables and values, etc.) Using logical operators in Stata Cross-tabulations Part 2: Data Analysis in Stata Correlation T-tests Ordinary Least Squares (OLS) and logistic regression (basic syntax, using interaction terms, interpreting output) Visualization (histograms, bar graphs, scatter plots) Regression postestimation (getting predicted values, basic graphs) Merging and appending datasets Part 3: Stata Programming Local and global variables (macros) Looping (foreach, forvalues) Reshaping data between wide and long formats Recalling and using command output Generating nicely formatted journal-style tables Workshop Materials: https://github.com/dlab-berkeley/stata-fundamentals Software Requirements: Installation Instructions (note: UC Berkeley students will receive an email with an instructional license for the workshop)

Everyone delivers their science in their unique ways. As a graduate student or a post-doc, have you ever walked out of an awesome scientific talk and reflected to yourself, “That speaker was so good! How did they do that?” or perhaps the complete opposite? Have you ever wondered how to approach and practice for an upcoming talk? Join us in this seminar to learn what Dr. Glaunsinger has to say about delivering engaging scientific talks!  

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 Prerequisites: D-Lab's Python Fundamentals (6 hours) series or equivalent introductory Python knowledge. Please take the D-Lab Skill Check to see if you are ready for Python Intermediate! Workshop Materials: https://github.com/dlab-berkeley/Python-Intermediate

This is a three-hour introductory workshop that will provide an overview of Excel, with no prior experience assumed. Attendees will learn how to use functions for handling data and making calculations, how to build charts and pivot tables, and more. The workshop includes a lecture-style walkthrough of each concept, combined with challenge problems to apply each concept to a real-world data analysis application. Instructors and TAs will provide support for students using Excel on either Windows or Mac, as well as for students using Google Sheets. Prerequisites: None. Workshop Materials: https://github.com/dlab-berkeley/Excel-Fundamentals Software Requirements: Installation Instructions Is Excel Not working on your laptop? Attend the workshop anyway, we can provide you with a cloud-based solution using Google Sheets.

This is a three-hour introductory workshop that will provide an overview of Excel, with no prior experience assumed. Attendees will learn how to use functions for handling data and making calculations, how to build charts and pivot tables, and more. The workshop includes a lecture-style walkthrough of each concept, combined with challenge problems to apply each concept to a real-world data analysis application. Instructors and TAs will provide support for students using Excel on either Windows or Mac, as well as for students using Google Sheets. Prerequisites: None. Workshop Materials: https://github.com/dlab-berkeley/Excel-Fundamentals Software Requirements: Installation Instructions

Are you wondering what processes, platforms, and funding are available at UC Berkeley to publish your research open access (OA)? This workshop will provide practical guidance and walk you through all of the OA publishing options and funding sources you have on campus. We’ll explain: the difference between (and mechanisms for) self-depositing your research in the UC’s institutional repository vs. choosing publisher-provided OA; what funding is available to put toward your article or book charges if you choose a publisher-provided option; and the difference between funding coverage under the UC’s “transformative agreements” vs. the Library’s funding program (Berkeley Research Impact Initiative). We’ll also give you practical tips and tricks to maximize your retention of rights and readership in the publishing process.

WORKSHOP REGISTRATION FORM Graduate Writing Center and the GSI Teaching & Resource Center Graduate Division University of California, Berkeley GETTING THE MENTORING YOU NEED IN GRADUATE SCHOOL TUESDAY, OCTOBER 17, 2023 2:00 to 3:30 P.M. THIS WORKSHOP WILL BE OFFERED REMOTELY VIA ZOOM. The purpose of this workshop is to assist graduate students in establishing and working within the context of mentoring relationships with faculty. It will also provide graduate students with strategies and skills that they will need in mentoring undergraduates here at Berkeley and preparing for the mentoring they will do in future careers. With Linda von Hoene (Assistant Dean for Professional Development and Director, GSI Teaching and Resource Center) and Sabrina Soracco (Director, Graduate Writing Center). Open to UCB graduate students and postdocs in all disciplines.

This workshop is a three-part introductory series that will teach you Stata from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the Stata software, understand data and basic manipulations, import and subset data, explore and visualize data, and understand the basics of automation in the form of loops and functions. After completion of this workshop you will have a foundational understanding to create, organize, and utilize workflows for your personal research. Each of the parts is divided into a lecture-style coding walkthrough interrupted by challenge problems, discussions of the solutions, and breaks. Instructors and TAs are dedicated to engaging you in the classroom and answering questions in plain language. Part 1:  Introduction Loading datasets into Stata (no previous knowledge expected) Examining a dataset and finding variables of interest Summarizing and tabulating variables Stata specific tools and resources (do files, logs, help files, etc.) Coding and cleaning data (making new variables from old variables; labeling variables and values, etc.) Using logical operators in Stata Cross-tabulations Part 2: Data Analysis in Stata Correlation T-tests Ordinary Least Squares (OLS) and logistic regression (basic syntax, using interaction terms, interpreting output) Visualization (histograms, bar graphs, scatter plots) Regression postestimation (getting predicted values, basic graphs) Merging and appending datasets Part 3: Stata Programming Local and global variables (macros) Looping (foreach, forvalues) Reshaping data between wide and long formats Recalling and using command output Generating nicely formatted journal-style tables Workshop Materials: https://github.com/dlab-berkeley/stata-fundamentals Software Requirements: Installation Instructions (note: UC Berkeley students will receive an email with an instructional license for the workshop)

Attend the Cal State Monterey Virtual Grad Fair and learn more about UC Berkeley and our graduate programs!

Lura Dolas (Senior Continuing Lecturer Emerita and former head of the UC Berkeley Acting Program) conducts a two-session in-person workshop to help graduate students deliver job talks and professional presentations clearly, confidently, and persuasively. The workshops offer techniques for neutralizing stage fright, identifying naturally expressive behavior, achieving personal connection in virtual presentations, and replacing flat jargon with engaging language. Exercises to expand vocal range and improve eye contact and use of gesture will enhance both live and virtual audience rapport. These workshops provide a safe, constructive environment to promote individual participation. In the first meeting, participants are given a series of exercises to enhance presentation of self and material. In the second, participants may present a short sample of a job talk or professional presentation for individual coaching. Workshop dates: Monday, October 30, 2023 | 9:30 am - 12:30 pm Monday, November 6, 2023 | 9:30 am - 1:00 pm Eligibility: UC Berkeley PhD students who have advanced to candidacy and are entering the job market, broadly conceived. Admission is limited. Priority will be given to students in the arts and humanities and related social sciences. Make sure to complete the Graduate Student Public Speaking Workshop Application by October 6th.

Registration deadline: Friday, October 27, 11:59 p.m. Please join us for BA's annual Professional Profile Clinic. This event is composed of three parts: The first event (November 2nd, 4-5pm PT, Zoom) will be a workshop on how to perfect your LinkedIn profile. The second event (November 2nd, 5-7pm PT, Zoom) is a chance for you to have your resume reviewed by recruiters in industry and career center professionals at UC Berkeley. This part of the event is capped at a certain number of participants, based on how soon you sign up. We will let you know if you have been selected for the resume review by October 27th. The third event (November 3rd, 3:30-5pm PT, In-Person) will be free headshots at Ishi Court (Location may change depending on the weather). We'd love to see you there!