Programming Languages

R Data Wrangling and Manipulation

November 5, 2021, 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 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 Data Visualization

March 13, 2023, 2:00pm
For this workshop, we'll provide an introduction to visualization with Python. We'll cover visualization theory and plotting with Matplotlib and Seaborn, working through examples in a Jupyter notebook.

Bash + Git: Introduction

June 1, 2022, 9:00am
This workshop will start by introducing you to navigating your computer’s file system and basic Bash commands to remove the fear of working with the command line and to give you the confidence to use it to increase your productivity. And then working with Git, a powerful tool for keeping track of changes you make to the files in a project.

Stata Fundamentals: Parts 1-3

February 13, 2024, 10:00am
This workshop is a three-part introductory series that will teach you Stata from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the Stata 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.

Qualtrics Fundamentals

January 9, 2024, 9:00am
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, market research, pre/post-event feedback, and other uses with some creativity.

R Introduction to Deep Learning: Parts 1-2

November 17, 2021, 10:00am
This workshop introduces the basic concepts of Deep Learning — the training and performance evaluation of large neural networks, especially for image classification, natural language processing, and time-series data. Like many other machine learning algorithms, we will use deep learning algorithms to map input data to their appropriately classified outcome labels.

QGIS Geospatial Fundamentals: Parts 1-2

February 22, 2023, 1:00pm
This workshop will introduce methods for working with geospatial data in QGIS, a popular open-source desktop GIS program that runs on both PCs and Macs as well as linux computers. Participants will learn how to load, query and visualize point, line and polygon data. We will also introduce basic methods for processing spatial data, which are the building blocks of spatial analysis workflows. Coordinate reference systems and map projections will also be introduced.

R Data Visualization

March 20, 2024, 2:00pm
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 statistics, transforming scales, and coloring or panelling by groups. You will learn how to make histograms, boxplots, scatterplots, lineplots, and heatmaps as well as how to make compound figures.

Python Visualization

October 5, 2022, 3:00pm
For this workshop, we'll provide an introduction to visualization with Python. We'll cover visualization theory and plotting with Matplotlib and Seaborn, working through examples in a Jupyter notebook.

Python Introduction to Machine Learning: Parts 1-2

February 23, 2022, 10:00am
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.