Software Tools

Thomas Lai

Consulting Drop-In Hours: Fri 3pm-5pm

Consulting Areas: Python, Matlab, APIs, Data Manipulation and Cleaning, Data Science, Data Visualization, Machine Learning, Python Programming, Software Tools, Git or Github, Spotfire

Quick-tip: the fastest way to speak to a consultant is to first submit a...

R Data Visualization

October 4, 2023, 2:30pm
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.

Michael Ruiz

IUSE Research Team
Psychology

Michael earned his B.A.in Psychology from UC Berkeley and currently works as the manager of Professor Okonofua's Equity, Diversity, and Empathy Navigation Sciences Lab in the UC Berkeley Psychology department.

Hikari Murayama

Senior Data Science Fellow, Senior Instructor
Digital Health Social Justice
Energy and Resources Group

Hikari is a graduate student in the Energy and Resource Group. Her research interests involve utilizing remote sensing and geospatial analysis to address pressing problems at the intersection of humans and climate. She recently served as a Data Science for Social Good Fellow at the University of Washington eScience Institute in the summer of 2020. She is experienced and happy to help in the areas of geospatial analysis, remote sensing, and other statistical analyses and methods. Hikari is devoted to helping community members realize their potential to conduct...

Lauren Chambers

Consultant
School of Information

Lauren Chambers is a Ph.D. student at the Berkeley School of Information, where she studies the intersection of data, technology, and sociopolitical advocacy with Prof. Deirdre Mulligan. Previously Lauren was the staff technologist at the ACLU of Massachusetts, where she explored government data in order to inform citizens and lawmakers about the effects of legislation and political leadership on our civil liberties. Lauren received her Bachelor's from Yale in 2017, where she double-majored in astrophysics and African American studies, and she spent two years after graduation in...

R Data Wrangling and Manipulation: Parts 1-2

September 25, 2023, 10:00am
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.

Cheng Ren

Senior Data Science Fellow
School of Social Welfare

Cheng Ren is a D-Lab Senior Data Science Fellow and a Ph.D. student at the School of Social Welfare. His research interests are community engagement and assessment, nonprofit development, community database, computational social welfare, and data for social goods.

Bash + Git: Introduction

September 13, 2023, 1:00pm
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.

Python Data Visualization Pilot: Parts 1-2

September 6, 2023, 10:00am
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 Deep Learning: Parts 1-2

June 12, 2023, 2:00pm
The goal of this workshop is to build intuition for deep learning by building, training, and testing models in Python. Rather than a theory-centered approach, we will evaluate deep learning models through empirical results.
See event details for participation information.