The D-Lab will be closed next Thursday, November 11 for the academic holiday. On this day we will not be operating our virtual front desk, hosting consulting drop-in hours, or offering workshops. We will be business as usual on Friday, November 12.
- D-Lab Virtual Space -
D-Lab Frontdesk
Stop by our virtual frontdesk, open Monday-Friday from 9am-5pm! Our undergraduate technicians (UTech) can answer general questions about workshops or other D-Lab services and can link you up with a consultant during drop-in hours.
Our physical space will remain closed to the public as we re-imagine
how to best make use of our physical space on the 3rd floor of the social sciences building.
Title: Large-scale Spatial Network Models for modeling disease and information passing for people experiencing homelessness in metropolitan areas Speakers:Zack Almquist, Assistant Professor of Sociology, University of Washington
Recent increases in homelessness in the United States have been described as a nationwide emergency. The negative impacts of homelessness on communities and individuals are well-established, including significant impacts to health, safety, and social and economic equality. To address the effects of increasing homeless populations, particularly in cities on the west coast of the US where numbers are growing rapidly, social scientists must understand the size and distribution of their homeless populations, as well as how information and resources are diffused throughout these communities... read more.
- D-Lab is Hiring! -
D-Lab is Hiring Consultants
For Spring 2021 we are hiring 10 graduate students as D-Lab consultants to work between 3-10 hours per week, including weekly 2-hour drop-in sessions at our virtual front desk and a 30-minute biweekly team meeting. All work can be done remotely.
We are looking for graduate students with experience in the following areas:
• Statistical methods and tools such as STATA, SASS, SPS
• Qualitative research methods and tools such as MAXQDA,
NViVo, Praat, Atlas.Ti
• Tableau or Excel for data analysis & visualization
• Survey design or survey analysis
If you have any of the above skills, please apply here by Nov 16th.
D-Lab is Hiring Instructors
We are looking for graduate students with experience in the following areas:
1-2 MaxQDA Instructors
2 Stata Instructors
1 Qualitative Methods Instructors
1 Instructor for QGIS / ArcGIS
2-3 Instructors for geospatial methods using R and/or Python
Qualitative Graduate Student Research Position
The D-Lab is hiring Graduate Student Researchers for the NSF-funded project, Undergraduate Data Science at Scale. The position is for 10-12 hours per week at $26 per hour with no fee remission included.
The qualitative research team is currently focused on outreach to recruit participants for student focus groups and developing protocols for upcoming interviews. We are looking to hire individuals with backgrounds and/or interests in qualitative methodologies. Your role would include:
Outreach to student organizations and implementation of focus groups and interviews
Data transcription and coding using MaxQDA
Attendance at weekly qualitative research team meetings and project team meetings
To submit your application materials, please fill out this survey by November 12, 2021 and note NSF IUSE as the research project you are interested in joining. We are looking for individuals who could start immediately or at the start of the Spring 2022 semester.
Undergraduate Student Advisory Board
The D-Lab is hiring Undergraduate Student Advisory Board Members for the NSF-funded project, Undergraduate Data Science at Scale. This part-time position for work-study eligible students starts at $18/hr for up to 5 hours per week, with the possibility of continuing for multiple semesters. Undergraduate students will serve on the project’s advisory board meetings biweekly and conduct informational outreach to peers. As well, board members are encouraged to engage in critical dialogue about student experience and research as the team develops this multi-year project.
To apply, interested Berkeley undergraduate students should submit this survey by November 12, 2021.
Data Science students and candidates in their sophomore and junior years from diverse or underrepresented backgrounds are encouraged to apply.
D-Lab has an opportunity for undergraduates to participate in our NSF-funded research project on Improving Undergraduate STEM Education (IUSE)
Focus Group: NSF IUSE Undergraduate Data Science at Scale
Have you transferred to UC Berkeley? Are you a re-entry student? Are you interested in Data Science Education? Have you taken Data Science courses at UC Berkeley?
If you answered yes, we would like to invite you to join us for a focus group exploring the experiences of transfer and re-entry students in Data Science at UC Berkeley. The NSF IUSE, Undergraduate Data Science at Scale project is holding focus groups in October to learn more about student experiences to improve educational opportunities. Focus groups will be approximately 90 minutes and participants will receive a $25 gift card for their participation.
The Division of Computing, Data Science, and Society’s Data Science Discovery Program is accepting project partner applications for Spring 2022. If you are interested in getting talented Berkeley data science students to work on your data science research project, please apply today! The Data Science Discovery Program is open to all UC Berkeley faculty, graduate students, and postdocs, along with non-profits, government agencies, and UC Berkeley-affiliated startups.
Since 2015, the Data Science Discovery Program has connected thousands of undergraduate data scientists with hands-on, team-based opportunities in hundreds of cutting-edge data-centered research projects with various organizations at UC Berkeley and beyond.
In addition to helping recruit student researchers, Discovery Program can provide free cloud computing credits and mentorship from graduate students with data science expertise. The Discovery Program also offers project management training for students and additional consultations for projects that are attempting to get off the ground.
We're accepting applications through January 1st and the recruitment timeline along with other information can be found on our website. The application can be accessed here.
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.
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.
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 Text Analysis Fundamentals: Parts 1-3
Nov 8, 10, 12 | 12pm-3pm | Register for Zoom link
This three-part workshop series will prepare participants to move forward with research that uses text analysis, with a special focus on humanities and social science applications.
Python Introduction to Machine Learning: Parts 1-2
Nov 8, 10 | 9am-12pm | Register for Zoom link
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.
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.
Advanced Data Wrangling aims to help students to learn powerful data wrangling tools and techniques in R to wrangle data with less pain and more fun. This workshop will show how R can make your data wrangling process faster, more reliable, and interpretable.
Geospatial data are an important component of data visualization and analysis in the social sciences, humanities, and elsewhere. The R programming language is a great platform for exploring these data and integrating them into your research. This workshop focuses on fundamental operations for reading, writing, manipulating and mapping raster data, which typically represents geographic information in a grid of regular sized cells.
Python Data Wrangling and Manipulation with Pandas
Nov 15 | 2pm-5pm | Register for Zoom link
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.
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.
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.
R Introduction to Machine Learning with tidymodels: Parts 1-2
Nov 16, 18 | 1pm-4pm | Register for Zoom link
Machine learning often evokes images of Skynet, self-driving cars, and computerized homes. However, these ideas are less science fiction as they are tangible phenomena that are predicated on description, classification, prediction, and pattern recognition in data. During this two part workshop, we will discuss basic features of supervised machine learning algorithms including k-nearest neighbor, linear regression, decision tree, random forest, boosting, and ensembling using the tidymodels framework. To social scientists, such methods might be critical for investigating evolutionary relationships, global health patterns, voter turnout in local elections, or individual psychological diagnoses.
Python Introduction to Artificial Neural Networks
Nov 17 | 9am-12pm | Register for Zoom link
This workshop presents a brief history of Artificial Neural Networks (ANNs) and an explanation of the intuition behind them; a step-by-step reconstruction of a very basic ANN, and then how to use the scikit-learn library to implement an ANN for solving a classification problem.
R Introduction to Deep Learning: Parts 1-2
Nov 17, 19 | 10am-1pm | Register for Zoom link
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.
Since 1790, the US Census has been THE source of data about American people, providing valuable insights to social scientists and humanists. Mapping these data by census geographies adds more value by allowing researchers to explore spatial trends and outliers. This workshop will introduce three key packages for streamlining census data workflows in R: tigris, tidycensus and tmap. Participants will learn how to download census tabular data for one or more geographic aggregation units or years, download the associated census geographic data and then join these data for analysis and mapping.
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.
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.
The Geospatial Innovation Facility (GIF) team is excited to announce a new series of online geospatial workshops! They are collaborating with the UC ANR's Informatics and GIS team this semester to bring you these great hands-on training opportunities. Please take a look at their workshop page to see what's available and to register.
Workshop on Code-Switching in Graduate School and Beyond
Nov 3 | 1pm-2:30pm | Register and read more
Given the many challenges and risks related to backing up research data, UC is developing an RFP for a Research Data Backup system. The RFP committee defines in-scope data as any “data used directly in support of UC research,” which can include file-based data, databases, cloud-resident only-copy data, and data up to and including highly sensitive (P4) data.
Support D-Lab
Join our community of donors by making a gift to D-Lab. Contributions of any size will support free, inclusive workshops and resources for the UC Berkeley community. Give today!
