research

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.

After clicking the registration link for your desired workshop, be sure to use your @berkeley.edu or @lbl.gov email address in the Zoom registration box to ensure a seamless process. Additionally, when joining the workshop, participants need to be logged in with their institutional email address in Zoom to be granted admission. You…

This three-part interactive workshop series is a follow-up to D-Lab’s Python Fundamentals(link is external). 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…

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. Register for this Python Geospatial Fundamentals Workshop!  

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…

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,…

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. Web scraping is typically only done when Web APIs are not available. Platforms like Twitter, Reddit, or…

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…

Your research is important and we are here to help keep it safe and secure. This brown bag session will focus on secure campus tools and services that Research IT and Berkeley IT offer to researchers, tips on navigating campus security processes, and cybersecurity best practices for keeping your research…

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…

Anyone pursuing a faculty career should understand the conventions of the academic job market. Panelists will share their experiences and what trainees should know to better navigate the hiring process. Your questions are welcome! Registration for this Zoom panel is required!

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.

Geospatial data are an important component of data visualization and analysis in the social sciences, humanities, and elsewhere. The R programming language is a great platform for exploring these data and integrating them into your research. This workshop focuses on fundamental operations for reading, writing, manipulating and mapping vector…

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…