Events

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

In this workshop, we provide an introduction to machine learning algorithms by making use of the tidymodels package. First, we discuss what machine learning is, what problems it works well for, and what problems it might work less well for. Then, we'll explore the tidymodels framework to learn how to fit machine learning models in R. Finally, we will apply the tidymodels framework to explore multiple machine learning algorithms in R. By the end of the workshop, learners should feel prepared to explore machine-learning approaches for their data problems. 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. Prerequisites: D-Lab’s R Fundamentals or equivalent knowledge; previous experience with base R is assumed and basic familiarity with the tidyverse. Workshop Materials: https://github.com/dlab-berkeley/R-Machine-Learning Software Requirements: Installation Instructions for getting started with using R and RStudio.