Lectures Out, Active Learning In? How About Somewhere in Between?

Online via Zoom

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***

Python Web APIs

Online via Zoom

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

Covidence: Getting Started

Online via Zoom

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.

Major Insights: ASML Virtual Panel Series – Materials Science

Online via Zoom

Materials Science is a discipline that is on the leading edge of technology through the development and optimization of new materials and the improvement of material processing and characterization for applications in all engineering fields. At ASML, those with a Materials Science and Engineering background can find themselves working on advanced characterization, coating development and processing, mechanics and fracture analysis, tribology and wear, material selection and supplier development for dynamic applications, advanced materials for vacuum environments, and more over a wide range of materials including metals, ceramics, polymers, and composites.

Python Data Wrangling and Manipulation with Pandas

Online via Zoom

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 Software Requirements:Installation Instructions for Python Anaconda