Events

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.

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 two-part workshop provides an introduction to machine learning algorithms using the tidymodels package. It covers what machine learning is, which problems it is most and least equipped to address, and explores the tidymodels framework to fit supervised machine learning models in R. Addressing machine learning problems requires a deep conceptual understanding of the material. While the workshop will cover coding in R, it will also dedicate a significant portion of the time to motivating machine learning techniques. By the end of the workshop, learners should feel prepared to explore machine learning approaches for their own data problems. This workshop does not cover unsupervised machine learning techniques. Prerequisites: Familiarity with R programming and data wrangling is assumed. If you are not familiar with the materials in Data Wrangling and Manipulation in R, we recommend attending that workshop first. In addition, this workshop focuses on how to implement machine-learning approaches. Learners will likely benefit from previous exposure to statistics.

The aim of this workshop is to make the conventions of interviewing more transparent and provide basic strategies for your success in presenting yourself to employers. You do not need to be currently on the job market to benefit from participating in this event; you will gain insights into interviewing dynamics and tips for preparation that can serve you in the future. PLUS…. learn about helpful campus resources such as Handshake, Vault and BigInterview.

This three-hour workshop will cover charts in more detail, review pivot tables, and the widely-used VLOOKUP function. We recommend first taking the introductory workshop Excel Data Analysis: Introduction.

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 two-part workshop provides an introduction to machine learning algorithms using the tidymodels package. It covers what machine learning is, which problems it is most and least equipped to address, and explores the tidymodels framework to fit supervised machine learning models in R. Addressing machine learning problems requires a deep conceptual understanding of the material. While the workshop will cover coding in R, it will also dedicate a significant portion of the time to motivating machine learning techniques. By the end of the workshop, learners should feel prepared to explore machine learning approaches for their own data problems. This workshop does not cover unsupervised machine learning techniques. Prerequisites: Familiarity with R programming and data wrangling is assumed. If you are not familiar with the materials in Data Wrangling and Manipulation in R, we recommend attending that workshop first. In addition, this workshop focuses on how to implement machine-learning approaches. Learners will likely benefit from previous exposure to statistics.

Improve your teaching and prepare for the academic job search. Come find out what is typically addressed in a teaching philosophy statement and participate in activities that will help you get started.

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

The Global Women in Data Science (WiDS) Conference is an annual technical conference based at Stanford University, which brings together data scientists and professionals in adjacent fields from around the globe to discuss the latest research and applications of data science in a broad set of domains. Participants learn how leading-edge companies are leveraging data science for success and connect with potential mentors, collaborators, and others in the field. WiDS Berkeley is an independent event organized by the Berkeley School of Information and other UC Berkeley partners as part of the annual WiDS Worldwide conference, the WiDS Datathon, and an estimated 200 WiDS Regional Events worldwide. Everyone is invited to attend all WiDS conference and WiDS Datathon Workshop events which feature outstanding women doing outstanding work. All genders are invited to participate in the conference, which features exclusively female speakers.

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

Network with representatives and learn more about full time and internship opportunities. Find out about recruiting timelines and application procedures. Registration IS NOT required for in-person fairs. All majors and degree levels are welcome to attend; employers will specifically be seeking to fill Nonprofit, Environment, and Public Service related roles. This fair is 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. Note: To view participating employers, select 'All Employers' and choose your filters of interest. This directory is updated in real-time, so check back often as the fair approaches! PLEASE NOTE THE FOLLOWING: Students cannot drop-off or leave any personal belongings at the fair or with BCE 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

By attending this event, you will learn more about our 2025 Technology Full-Time Programs & Internships. During this interactive overview and associate panel, you'll learn more about our Technology Development Program (TDP) & Internship, Capital One Developer Academy (CODA), Cyber Security Development Program (CSDP) & Internship, Machine Learning Development Program (MLDP) and our Tech Early Internship Program. This is a great opportunity to learn more about our offerings and ask Capital One recruiters & associates questions during the Q&A. Agenda (Feel free to opt in where you want and when it works for you!): 4 - 4:30 PM ET: Technology Programs & Internships Overview 4:30 - 5 PM ET: Associate & Recruiter Q&A To view a list of other upcoming events, check out our landing page. If you’d like to learn more about us, feel free to browse our blog homepage and get a snapshot of #LifeAtCapitalOne

As part of our series of workshops on DataHub, the second part will concentrate on introducing the Berkeley Data Stack–a suite of tools that support Data Science instruction at Berkeley–and exploring how instructors across the campus are integrating such tools as part of their course work. The tools include nbgitpuller, Otter Grader, Gradescope, and Github.  We'll explore the more advanced functionalities of these tools, which are used by diverse instructors. Consider also enrolling in the first part of this workshop series: Teaching with Jupyter Notebooks in Python and R - Intro to Datahub For the past six years, UC Berkeley has been utilizing DataHub, a campus-wide JupyterHub infrastructure, to enhance learning across various disciplines like Engineering, Data Science, Natural Science, and Social Sciences. DataHub is used in over 100 courses across 30+ departments, offering interactive computing environments via JupyterHub's open-source tools, accessible from any web browser. This workshop is ideal for those interested in: integrating computational notebooks to improve learning outcomes, utilizing cloud-based DataHub for interactive computing in classrooms and homework integrating auto graders to grade student assignments and store the final scores in Gradescope This workshop will provide a concise introduction to Berkeley Data Stack, along with case studies from diverse departments and a brief demo to showcase practical applications. This workshop is the second session in a three-part series aimed at introducing both basic and advanced tools that support Data Science pedagogy. By the end of the workshop, participants will be able to understand how they can integrate Berkeley Data Stack as part of their coursework and learn from examples of colleagues across the campus interested in using Datahub as part of their pedagogy. This hybrid event will be held in person in 117 Dwinelle Hall (Academic Innovation Studio) and also on Zoom. Please register to get the Zoom invite.   ➡️Register for this event here!⬅️ ***Registration for this session will close one hour before the session***

Advance your career as an effective, inclusive leader in STEM through this four-part series brought to you by the New York Academy of Sciences with special guest speakers from Pfizer senior leadership. Effective leadership is a key skill for all STEM professionals, but it is often difficult to define and practice. It requires developing and honing a range of personalized skills beyond the technical knowledge acquired from pursuing a degree or working in a lab or school. Aligning these skills with an individual’s values and strengths empowers them to be a positive change agent in the world, uplifting people within and outside a single organization. This four-part series of bite-sized learning webinars are designed to build key skills as a change maker. Participants will have the opportunity to attend the webinars live or on demand. Registration required.

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

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. 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 Python Data Wrangling and Manipulation with Pandas

This workshop is designed for graduate students who are interested in improving their statements ​of teaching philosophy through feedback​ ​from peers. Workshop participants will exchange statements of teaching philosophy and provide​ ​and receive input. Participants must bring two copies of a draft of their statement of teaching philosophy to participate in the workshop.

In this 3 part workshop series, we will provide an introduction to spatial analyses in R. We discuss the benefits of the additional ‘location’ component that defines spatial data and how spatial dataframes organize this information. Using the sf (simple features) and terra packages, we'll navigate fundamental operations for reading, writing, manipulating, and visualizing spatial data. Some of the key topics we'll cover include: Understanding different types of spatial data (points, lines, polygons, raster) Using appropriate coordinate reference systems Implementing classification schemes (equal intervals, quantiles and natural breaks) for improved data visualization Conducting spatial measurement queries (distance from, intersections, buffers, and finding nearest features) Employing visualization techniques (overlay plotting, interactive maps) Performing combined vector-raster analysis (zonal statistics) This workshop aims to equip participants with the fundamentals needed to conduct spatial analyses for their various endeavors.

The event is primarily aimed at UC Berkeley students and scholars, and we warmly welcome students and scholars from other Bay Area universities such as UCSF, Stanford, and the University of San Francisco. The participants will include undergraduate, graduate, and doctoral students. There will be no charges for any attendees, and participants can RSVP in advance through the pre-registration form below. By attending this event, students will be able to network with HRs from over 30 companies with bases all over the world, most of which will sponsor CPT/OPT/H1B for international students. This event will provide a different perspective for students, focusing on an international view and providing students more options for their career paths

Berkeley’s Active Learning classrooms (ALC) are teaching and learning spaces that allow the instructor to customize the physical learning environment to meet pedagogical needs. In an ALC, the room design, flexible furniture, writing surfaces, and technology allow for multiple pedagogical activities. Research also suggests that students in ALCs outperform their peers in traditional classrooms (Cotner et al., 2013). ALCs have been intentionally designed to encourage and foster the use of active learning strategies - except, they don’t come with a manual about how to make that happen! In this workshop,  you’ll learn how to get the most out of your classes in ALCs. By the end of the workshop, participants will be able to: Describe what an Active Learning Classroom is Outline the advantages of an active learning classroom Identify learning strategies that offer students multiple ways to engage with the course content, their peers, and the instructor This session will run for 45 minutes, with an additional 15 minutes reserved for questions. This event will take place in-person at Social Sciences 110. Please RSVP if you're planning to join.  ➡️ Register for this event here!⬅️ ***Registration for this session will close one hour before the session***

The goal of this workshop is to build intuition for deep learning by building, training, and testing models in Python. Rather than a theory-centered approach, we will evaluate deep learning models through empirical results. We start with a review of what deep learning is and then unpack what neural networks are and how they work. We then jump straight into Python, using the Keras library to build neural networks. We will explore how different architectures affect the performance of predicting handwritten digit images. Lastly, we explore a specific flavor of neural networks, the convolutional neural network. We review how it’s different from a standard vanilla neural network, and build different architectures to test how well they perform on the classification of animal and vehicle image classification.

In this 3 part workshop series, we will provide an introduction to spatial analyses in R. We discuss the benefits of the additional ‘location’ component that defines spatial data and how spatial dataframes organize this information. Using the sf (simple features) and terra packages, we'll navigate fundamental operations for reading, writing, manipulating, and visualizing spatial data. Some of the key topics we'll cover include: Understanding different types of spatial data (points, lines, polygons, raster) Using appropriate coordinate reference systems Implementing classification schemes (equal intervals, quantiles and natural breaks) for improved data visualization Conducting spatial measurement queries (distance from, intersections, buffers, and finding nearest features) Employing visualization techniques (overlay plotting, interactive maps) Performing combined vector-raster analysis (zonal statistics) This workshop aims to equip participants with the fundamentals needed to conduct spatial analyses for their various endeavors.

Grades have become ubiquitous in the college teaching and learning environment, but how did we get here? Join us for an informal journal club to learn more about the history of the traditional grading system (e.g. A-F course grade scale) and review a synthesis of the literature on how grades have been shown to affect student learning. We will dive into the Schinske and Tanner 2017 article “Teaching More by Grading Less (or Differently)” and discuss their proposed strategies for enhancing teaching and learning, particularly for more equitable outcomes. In so doing, we invite participants to envision what the future of grading could look like at UC Berkeley. At the end of this session, you will: Investigate the history of grading in higher education. Discuss how grades affect student learning experiences. Examine grading as a mechanism for exacerbating inequities and reinforcing biases. Identify strategies to mitigate the shortcomings of traditional grading practices. Everyone in the UC Berkeley instructional community is welcome to join the chat! This event is hosted by Research, Teaching, and Learning (RTL) and the College of Engineering EMPOWER Program and is one of a three-part series focused on Equitable Grading Strategies. Please consider also enrolling in the other two events: Perspectives on Equitable Grading-Faculty Coffee Chat and Putting Equitable Grading into Practice - Assignment Clinic. This hybrid event will be held in person in 117 Dwinelle Hall (Academic Innovation Studio) and also on Zoom. Please RSVP to get the Zoom invite.   Lunch will be available for in-person participants. We kindly request that you register no later than 3 days in advance if you're attending in person, to help us arrange catering. In-person participants please arrive promptly at 11:30 AM for lunch. This journal club will then run for 60 minutes, with an additional 15 minutes reserved for informal networking. ➡️ Register for this event here!⬅️ ***Registration for this session will close two hours before the session for remote participants***

Berkeley Career Engagement (BCE) is pleased to invite you to the 2024 Diversity & Inclusion Career & Internship Fair Network with representatives and learn more about full time and internship opportunities. Find out about recruiting timelines and application procedures. Registration IS NOT required for in-person fairs. All majors and degree levels are welcome to attend. This fair is 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.

Advance your career as an effective, inclusive leader in STEM through this four-part series brought to you by the New York Academy of Sciences with special guest speakers from Pfizer senior leadership. Effective leadership is a key skill for all STEM professionals, but it is often difficult to define and practice. It requires developing and honing a range of personalized skills beyond the technical knowledge acquired from pursuing a degree or working in a lab or school. Aligning these skills with an individual’s values and strengths empowers them to be a positive change agent in the world, uplifting people within and outside a single organization. This four-part series of bite-sized learning webinars are designed to build key skills as a change maker. Participants will have the opportunity to attend the webinars live or on demand. Registration required.

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 is part of a loosely-coupled 4-part text analysis workshop series that will prepare participants to move forward with research that uses text analysis, with a special focus on social science applications. We explore fundamental approaches to applying computational methods to text in Python. We cover some of the major packages used in natural language processing, including scikit-learn, NLTK, spaCy, and Gensim. This two-part workshop series will prepare participants to move forward with research that uses text analysis, with a special focus on humanities and social science applications.

In this 3 part workshop series, we will provide an introduction to spatial analyses in R. We discuss the benefits of the additional ‘location’ component that defines spatial data and how spatial dataframes organize this information. Using the sf (simple features) and terra packages, we'll navigate fundamental operations for reading, writing, manipulating, and visualizing spatial data. Some of the key topics we'll cover include: Understanding different types of spatial data (points, lines, polygons, raster) Using appropriate coordinate reference systems Implementing classification schemes (equal intervals, quantiles and natural breaks) for improved data visualization Conducting spatial measurement queries (distance from, intersections, buffers, and finding nearest features) Employing visualization techniques (overlay plotting, interactive maps) Performing combined vector-raster analysis (zonal statistics) This workshop aims to equip participants with the fundamentals needed to conduct spatial analyses for their various endeavors.

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.

It is said that 80% of data analysis is spent on the process of cleaning and preparing the data for exploration, visualization, and analysis. This R workshop will introduce the dplyr and tidyr packages to make data wrangling and manipulation easier. Participants will learn how to use these packages to subset and reshape data sets, do calculations across groups of data, clean data, and other useful tasks.

This workshop is dedicated to enhancing academic mentoring by emphasizing the unique needs and expectations of students, particularly those from diverse backgrounds. It aims to bridge the gap between faculty mentors and their students, helping faculty understand what students of diverse backgrounds want them to know about effective mentoring. In this workshop, we'll explore the perspectives of students shedding light on their needs and preferences when it comes to mentorship. We’ll also discuss the challenges and constraints faculty members often face that hinder their ability to provide effective mentoring to these students. Faculty members will leave with practical strategies to foster inclusive, equitable, and effective mentoring relationships, ensuring that all students, regardless of their backgrounds, can thrive academically.  

The Forest Service is hosting webinars to help you through the application process. Join us and get answers to your questions. Note: You may be prompted to download the free Microsoft Teams application. All webinars will be available to watch on-demand after the date and time listed. Navigating USAJOBS – Tuesday, March 19, 3-4 p.m. MT Visit the Forest Service Jobs webpage, fs.usda.gov/fsjobs, to learn more about career opportunities, benefits, hiring events, and resources to help with the application process.

This workshop will provide a topic overview with advice/tips. You will gain insights into the dynamics of negotiating job offers that can serve you in the future. The primary focus of this session will be negotiating non-faculty job offers. You do not need to be currently on the job market to benefit from this event. Register here

Racism is a multifaceted, interconnected, and fundamental cause of racialized health inequities. As such, racism impacts who gets sick, who dies, and who is able to live healthy. In this talk Dr. Pirtle will overview interventions of her empirical research, informed by critical race theory, that utilizes multidimensional measures of race and skin color, and structural measures of racism, to explore health outcomes for Black, Latinx, and other populations of color. The talk demonstrates that using theoretically informed measures of race and racism help us refine our understanding of racialized health associations and clarifies mechanisms of structural racism that shape contemporary racialized health inequities. Collectively, the discussion helps think through equitable interventions that can facilitate better health for more of us. Dr. Whitney Pirtle is an Associate Professor of Sociology and MacArthur Foundation Chair in International Justice and Human Rights at the University of California, Merced, where she also directs the Sociology of Health and Equity (SHE) Lab. Dr. Pirtle is a sociologist with interdisciplinary and subject area expertise in race and racism; health disparities and health equity; social problems and social justice; Black feminist sociology and praxis; and mixed methodologies. Recent research includes writing on Covid-19 pandemic inequities, institutional anti-Blackness, and in 2021 she co-edited Black Feminist Sociology: Perspectives and Praxis with Zakiya Luna.

Join JLL for our Future of Work Series! This session will provide you tips, tricks, and tools to help develop your personal branding with online platforms! We will also have opportunities for you to ask questions about the topic or our programs!

It is said that 80% of data analysis is spent on the process of cleaning and preparing the data for exploration, visualization, and analysis. This R workshop will introduce the dplyr and tidyr packages to make data wrangling and manipulation easier. Participants will learn how to use these packages to subset and reshape data sets, do calculations across groups of data, clean data, and other useful tasks.

This workshop is part of a loosely-coupled 4-part text analysis workshop series that will prepare participants to move forward with research that uses text analysis, with a special focus on social science applications. We explore fundamental approaches to applying computational methods to text in Python. We cover some of the major packages used in natural language processing, including scikit-learn, NLTK, spaCy, and Gensim. This two-part workshop series will prepare participants to move forward with research that uses text analysis, with a special focus on humanities and social science applications.

Fisher Investments is excited to continue our Career Convo series focused on topics important to your job search and career! Join our Gender Equality Matters Affinity Group virtually on Friday, March 22 from 11:00am-12:00pm PST / 2:00pm-3:00pm EST as they discuss Imposter Syndrome and how to overcome it in your career. Our diverse leaders are excited to engage in a conversation about our global organization and the career opportunities currently available. During this virtual event, you will: 1. Hear from Fisher Investments’ Affinity Group leaders and members about their personal career paths and experiences 2. Learn what a career in financial services could look like for you and why it’s a great fit for individuals of all different backgrounds 3. Find out why more than 90% of our employees agree that when you join Fisher Investments, you’re made to feel welcomed 4. Receive guidance on what you can do today to start on the path to your career in finance. At Fisher Investments, we believe that championing a diverse and inclusive workforce is critical to our mission to help more clients globally. For over 40 years, we have worked in our client’s best interests by thinking and acting independently. Our personalized approach, commitment to serving our clients, and our investing experience make us unique in our industry. We are looking for individuals such as yourself to join our team! PROGRAM DETAILS Date: Friday, March 22, 2024 Time: 11:00AM PST/ 2:00PM EST Location: Event will be held via Zoom. The meeting link will be emailed upon registration.

Are you interested in applying to the DreamSF Fellowship? Learn more about the DreamSF Fellowship program, how to apply, and more from the current DreamSF fellows at this virtual information session. Register here and we'll send you the information to join the presentation. The DreamSF Fellowship is a paid leadership and civic engagement program for immigrant youth. No experience necessary. Open to all immigrants, regardless of status. Learn more at: dreamsffellows.org.

How do we build movements to defeat mass incarceration and the climate crisis? At the community level, how do we make sense of both of these crises and advance solutions that don’t compromise one for the other? What are narratives that can inspire transformation and build alliances across different organizations and issues? We have been focused on these questions over the last few years and are ready to share some insights and tools. Join us virtually on Wednesday, March 27 at 10am PT as we share a beautiful, animated video, a curriculum for facilitating workshops and planning sessions related to transformative narratives and collective visioning, and a report related to just world creation. These tools were made for community education, organizational change, and coalition building - especially for organizers and advocates focused on racial justice, climate justice, abolition, mass liberation, and building bridges across movements for transformative change.  

Join City Teaching Alliance, Thursday March 28th from 7-8pm est, as we proudly present "A Teacher Like Me: The Impact of AAPI Leaders in Urban Education." This program is an illuminating virtual celebration of the invaluable contributions of AAPI educators in shaping our classrooms and communities. In a world where diversity and inclusion are paramount, the need for AAPI representation in urban education has never been more pressing. Join us as we delve into the transformative power of representation and explore the profound impact that AAPI educators have on students' lives, classroom dynamics, and the broader educational landscape. Through insightful discussions and firsthand accounts, we will highlight the unique perspectives and experiences of AAPI educators, shedding light on their role in fostering inclusive learning environments and promoting cultural understanding. Whether you're an educator, student, community advocate, or simply curious about the powerful impact of AAPI representation, this event promises to be an eye-opening exploration of diversity, equity, and inclusion in education. Don't miss this opportunity to gain valuable insights and inspiration from AAPI leaders in urban education. Register now and be a part of the conversation as we collectively strive to build a more equitable and inclusive future for all.

Our speakers will explore the importance of allyship in the workplace and share stories of their experiences with allyship at McKinsey. This is an informal, non-evaluative event for you to hear from members of Equal—McKinsey's LGBTQ+ network . Feel free to wear casual attire (we will!) and bring your curiosity and questions. We recognize that each attendee’s circumstances may be different, and we want to ensure your privacy is considered. We encourage you to do what feels most comfortable during these sessions, including joining without your camera turned on and/or changing your name as it appears on the Zoom screen upon joining. Our Proud Speakers series is aimed at individuals who self-identify as LGBTQ+ or LGBTQ+ allies and are interested in learning more about being LGBTQ+ at McKinsey and our research. This virtual series is just one of our many worldwide programs aimed at helping candidates get to know McKinsey better. Stay tuned to our Real Life at McKinsey Facebook page, as well as our events page, for more information regarding upcoming events and initiatives.

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

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

Want a chance to hear from experts studying the root of what’s driving us apart and how we come back together? Eager to build the skills to listen empathetically, elicit narratives of connection, and interview professionally? Looking for a chance to engage with groundbreaking documentary films that push us to expand our perspective? Come join The Othering and Belonging Institute’s Campus Bridging Project and The Social Cohesion Lab, along with our collaborators and co-sponsors (The Greater Good Science Center, Berkeley Haas DEIJB and Berkeley Haas HBSA) for a Depolarization Day on April 6th, 2024 from 10am-4pm. The interest form can be found here.