Upcoming Events

This two-hour introductory workshop will teach you MaxQDA from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the MaxQDA software, upload multiple forms of data then how to use manual and autocode features. We will review some of the additional analytic features including visual, memo and the Questions, Themes and Theories (QTT) tools. We will briefly touch on the MaxQDA Team cloud-based version. Instructors will share recommended resources. Prerequisites: None Workshop Materials: https://github.com/dlab-berkeley/MAXQDA-Fundamentals Software Requirements: Installation Instructions for MaxQDA

Description: At NSA, we’re bound together by a common mission to protect the nation, but we’re excited about our differences, too. We depend on diverse perspectives to solve problems of broad magnitude because every voice adds value to the discussion. Sign up to hear from NSA employees about their experiences as members of employee resource groups and the Agency’s commitment to diversity, equity, inclusion, and accessibility.

What is it like to join a global consulting firm if you have a background in academia? Come hear from McKinsey consultants with advanced professional degrees (APD) about their journeys! McKinsey values consultants with PhDs and other graduate-level degrees - they have fantastic research skills, specific domain knowledge, and ways of thinking that are important to our firm and our clients.

While graduate students do not need to become experts in how students learn, a basic understanding of the research on learning can enable GSIs to make informed teaching decisions. Drawing on research findings from neuroscience, anthropology, and cognitive and social psychology, this workshop will enable GSIs to consider ways to apply research-based principles to the learning environments they create for their students.

In this workshop, we cover how to extract data from the web with APIs using Python. APIs are often official services offered by companies and other entities, which allow you to directly query their servers in order to retrieve their data. Platforms like The New York Times, Twitter and Reddit offer APIs to retrieve data. When APIs are not available, one can turn to web scraping. If you want to learn how to do web scraping in Python, attend the D-Lab Python Web Scraping Workshop. Requirements: We will assume a basic knowledge of Python. If you've taken the D-Lab's Python Intensive, that should be sufficient. Prerequisites: D-Lab’s Python Fundamentals introductory series or equivalent knowledge. GitHub Repository: https://github.com/dlab-berkeley/Python-Web-APIs Software Requirements:Installation Instructions for Python Anaconda

Active learning is an approach that emphasizes student engagement with course materials during class rather than solely listening to lectures. While active learning techniques have been shown to significantly enrich students’ educational experience, active learning does not have to be a replacement for lectures. In fact, using a mixed methods approach that incorporates both lecture and active learning can be a powerful tool for increasing student engagement. In this workshop, we explore how blending lectures with active learning strategies can create a more dynamic and effective learning experience. Drawing upon the science of learning, we will explore various methods and strategies to help you find a balance between lecture and active learning that feels right for your teaching context. By the end of this workshop you will: Learn what active learning is and why it's beneficial in teaching. Gain insights into the benefits of combining lectures with active learning Discover ways to blend lectures with active learning methods. This session will run for 30 minutes, with an additional 15 minutes reserved for questions. This session will be held via Zoom. Please register to get the Zoom link. ➡️ Register for this event here!⬅️ ***Registration for this session will close one hour before the session***

Covidence, a web-based tool licensed by the UC Berkeley Library, helps with your systematic and other literature reviews, which are popular processes to summarize and synthesize literature in your topic of interest. Covidence helps you organize and track progress on your review, from search results to extraction. In Covidence, you can: Work individually or with a team of reviewers; Import journal article citations; Screen titles and abstracts; Upload full article PDFs; Screen full text; Resolve reviewer conflicts and calculate inter-rater reliability scores; Create forms for critical appraisal; Perform risk of bias tables; Complete data extraction, and; Export a PRISMA flowchart. This interactive workshop will take you through these steps, starting with creating your UC Berkeley Covidence account. How to add reviewers or make changes mid-review, how to develop exclusion criteria, and how to get help will be covered. There will be plenty of time for Q & A during this session; you are welcome to raise questions about your specific review or review process. *A @berkeley or @lbl email is needed to use the UC Berkeley Covidence license. However, non-UCB folks are welcome to attend this workshop. Prerequisites: None. Workshop Materials: https://drive.google.com/drive/folders/1T1CnJP_f6e8Uv7VyT6zhS1gp60ulfD6b Software: A @berkeley or @lbl email is needed to use the UC Berkeley Covidence license. However, non-UCB folks are welcome to attend this workshop.

What does it take to create a values-based supply chain? Who are the workers and other stakeholders that make this supply chain operate? What does providing technical assistance to farmers and ranchers and other food businesses look like? Berkeley Food Institute will address these questions and more at our annual career fair titled Cultivating Values-Based Relationships: Career Conversations with Food System Professionals. This year will center careers in values-based supply chains and technical assistance for farmers and food businesses. We invite students at any point in their academic journey to learn about career pathways that center their values and forge meaningful relationships with the people who help feed our communities. We have invited employers ranging from technical assistance providers to farmers and small businesses, food producers and manufacturers, manufacturers, and distributors, to name a few, all working toward a food system that prioritizes justice, equity, and responsible land stewardship.

In this workshop, we provide practical guidance on data wrangling using R's tidyverse suite of packages. In Part 2, we cover how to wrangle messy data into tidy data. After this workshop, you will be able to: Use tidyverse functions for transforming your data to be used for visualization and analysis Identify how an "untidy" dataframe gets in the way of data visualization and analysis Use pivot_longer and pivot_wider to wrangle a dataframe into "tidy" form, and join multiple dataframes Prerequisites: D-Lab’s R Fundamentals or equivalent knowledge; previous experience with base R is assumed. Workshop Materials: https://github.com/dlab-berkeley/R-Data-Wrangling Software Requirements: Installation Instructions for R and RStudio

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. Workshop Materials: https://github.com/dlab-berkeley/Python-Intermediate

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. Register here Prerequisites: None. Workshop Materials: Workshop Step by Step PDF (or DOCX) and slides (PPTX)

The 2024 LARB Publishing Workshop will be online (fully remote) from June 24 to July 26, 2024. Fellows from around the world will learn from over 40 speakers representing presses, magazines, literary organizations, and new media companies. They will have the opportunity to network one-on-one with speaker and LA Review of Books staff while working on job application materials. Join the LARB Publishing Workshop Director, Lindsay Wright, Workshop Coordinator, Livia Lima, and alumni to learn more about the workshop and applications. Questions are welcome! The First Info Session is Tuesday, February 13, 9 - 10 a.m. PST

The ability to successfully apply for extramural funding is a critical skill for academic investigators, including graduate students, postdoctoral scientists, and other junior investigators. Grantsmanship involves much more than simply writing the application. Investigators must identify funding agencies that align with their research interests and understand the goals of different types of funding programs. Graduate students and postdoctoral fellows gain crucial experience in this area by submitting grant applications early in their careers, impacting their future success as independent investigators. Join The Academy as Dr. Jaime Rubin, Vice Chair for Investigator Development at Columbia University, addresses relevant topics and skills required for successful funding and grantsmanship. Registration required.

This workshop is a three-part introductory series that will teach you Stata from scratch with clear introductions, concise examples, and support documents.

In this workshop, we provide practical guidance on data wrangling using R's tidyverse suite of packages. In Part 2, we cover how to wrangle messy data into tidy data. After this workshop, you will be able to: Use tidyverse functions for transforming your data to be used for visualization and analysis Identify how an "untidy" dataframe gets in the way of data visualization and analysis Use pivot_longer and pivot_wider to wrangle a dataframe into "tidy" form, and join multiple dataframes Prerequisites: D-Lab’s R Fundamentals or equivalent knowledge; previous experience with base R is assumed. Workshop Materials: https://github.com/dlab-berkeley/R-Data-Wrangling Software Requirements: Installation Instructions for R and RStudio

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. Workshop Materials: https://github.com/dlab-berkeley/Python-Intermediate

In this webinar, participants learn how to interview successfully for positions and roles at U.S. universities.

This workshop will help you develop your personal criteria for choosing active learning strategies that align with your instructional goals.

In this workshop, we cover how to scrape data from the web using Python. Web scraping involves downloading a webpage's source code and sifting through the material to extract desired data.

Interested in applying for our Insight or Diversity Connect programs? Come hear from McKinsey consultants with advanced professional degrees (APD) about their journeys and ask questions about our Insight and Diversity Connect program. McKinsey values consultants with PhDs and other graduate-level degrees - they have fantastic research skills, specific domain knowledge, and ways of thinking that are important to our firm and our clients.

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.

Capital One professionals will walk participants through two of the aspects of the Capital One Tech Interview Process: Technical Interview

This is a joint event by the Berkeley Career Engagement and Berkeley International Office to help international students get ready for the campus recruiting season of this Spring semester.

Qualtrics is a powerful online tool available to Berkeley community members that can be used for a range of data collection activities. Primarily, Qualtrics is designed to make web surveys easy to write, test, and implement, but the software can be used for data entry, training, quality control, evaluation, market research, pre/post-event feedback, and other uses with some creativity. This overview will introduce a simple workflow using the system with an orientation to the main interfaces for web survey design, sample management, corresponding with sample members, and exporting data at the end of the field period. Examples from completed surveys in public health, economics, program evaluation, and other disciplines will be incorporated in the overview. This workshop will introduce students to the basics of designing a survey instrument using the Qualtrics platform, such as randomization and survey flow. We will also cover more advanced topics like implementing embedded data and using javascript, as well as tips and tricks on how to use your design to maximize the number of quality responses you get. Prerequisites: None Workshop Materials: https://github.com/dlab-berkeley/Qualtrics-Fundamentals Feedback: After completing the workshop, please provide us feedback using this form. Questions? Email: [email protected] LOCATION: Remote via Zoom. Link will be sent on the morning of the event

In this webinar, you will learn about the importance of peer reviewing for dismantling the Western canon and democratizing knowledge, as well as strategies you can use to engage in more inclusive peer reviewing practices.

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. Workshop Materials: https://github.com/dlab-berkeley/Python-Intermediate

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 Software Requirements: Installation Instructions Is Python Not working on your laptop? Attend the workshop anyway, we can provide you with a cloud-based solution until you figure out the problems with your local installation. Feedback: After completing the workshop, please provide us feedback using this form Questions? Email: [email protected] LOCATION: Remote via Zoom. Link will be sent on the morning of the event.

This two-hour workshop offers a comprehensive introduction to designing and conducting survey studies. Tailored for beginners, it provides clear, step-by-step guidance complemented by concise examples, practical considerations, and useful support materials. Participants will learn the entire process, from formulating a research question to creating, administering, and analyzing surveys, as well as interpreting results and communicating their findings. Special emphasis will be placed on crafting survey instruments that are succinct, unbiased, and effectively aligned with your research objectives. Additionally, instructors will share a curated list of recommended resources.

This workshop will provide an introduction to graphics in R with ggplot2. Participants will learn how to construct, customize, and export a variety of plot types in order to visualize relationships in data. We will also explore the basic grammar of graphics, including the aesthetics and geometry layers, adding statistics, transforming scales, and coloring or panelling by groups. You will learn how to make histograms, boxplots, scatterplots, lineplots, and heatmaps as well as how to make compound figures.

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.