IT offers technical training courses to Berkeley Lab employees in partnership with UC Berkeley. Click on the course titles below to learn more, then register through the Berkeley Lab portal.
- Berkeley Lab staff may contact Erin Scharfstein at ittraining@lbl.gov for training feedback.
- For trouble registering for courses, Zoom links, etc., contact dlab-frontdesk@berkeley.edu.
- Visit it.lbl.gov/training for more IT learning and consulting opportunities.
Course Listing
R Fundamentals: Parts 1-4
August 14, 2023, 10:00 a.m. to August 17, 2023, 12:00 p.m.
This workshop is a four-part introductory series that will teach you R from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the open-sourced R Studio software, understand data and basic manipulations, import and subset data, explore and visualize data, and understand the basics of automation in the form of loops and functions. After completion of this workshop you will have a foundational understanding to create, organize, and utilize workflows for your personal research.
Stata Fundamentals: Parts 1-3
August 14, 2023, 10:00 a.m. to August 16, 2023, 1:00 p.m.
This workshop is a three-part introductory series that will teach you Stata from scratch with clear introductions, concise examples, and support documents. Each of the parts is divided into a lecture-style coding walk-through, interrupted by challenge problems, discussions of the solutions, and breaks.
Python Fundamentals: Parts 1-3
August 14, 2023, 2:00 p.m. to August 16, 2023, 4:00 p.m.
The goal of this workshop is to build intuition for deep learning by building, training, and testing models in Python. 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.
Python Data Wrangling and Manipulation with Pandas
August 17, 2023, 2:00 p.m. to 5:00 p.m.
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.