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 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.
Upcoming Courses
May 1, 2, 3, 4 from 10:00am to 12:00pm
This workshop is a four-part introductory series that will teach you R from scratch. You will learn how to 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.
R Data Wrangling and Manipulation: Parts 1-2
May 1, 2 from 10:00am to 1:00pm
It is said that 80% of data analysis is spent on the process of cleaning and preparing the data for exploration, visualization, and analysis. This two-part R workshop will introduce the dplyr and tidyr packages to make data wrangling and manipulation easier. Participants will learn how to use these packages to subset and reshape data sets, do calculations across groups of data, clean data, and other useful tasks.
Python Fundamentals Pilot: Parts 1-3
May 1, 2, 3, 4 from 2:00pm to 4:00pm
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.
Python Data Wrangling and Manipulation with Pandas
May 4 at 1:00pm to 4:00pm
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.