Log in

Past Working Groups

Sign up for our weekly newsletter!

This is an archive of our past working groups. We are looking to include working groups topics not yet covered here. Is there something not currently on the list? Send us a proposal.

E.g., 19-Jun-24
E.g., 19-Jun-24

Design for Equity Lab

When & Where
Schedule: 
This was a working group Fall 2017 - Fall 2018
Location: 
Barrows 356: Convening Room
Description

 This was a working group Fall 2017 - Fall 2018 and is no longer active.

Are you interested in developing quantitative and qualitative research skills as a researcher on inclusive teaching?

Directed by Andrew Estrada Phuong and Judy Nguyen, the Design for Equity Lab (D4E Lab) examines how adaptive learning environments and organizational practices can enhance equity. The D4E Lab employs quantitative and qualitative research methods to address a variety of research questions regarding equity, access, and inclusion.

In the D4E Lab working group, you will engage in quantitative and qualitative data analysis. You will learn how to use statistical and data visualization software, such as Stata. Using randomized control trials, we’re studying how adaptive equity-oriented pedagogies and non-adaptive pedagogies impact the following outcomes in higher education:

  • student achievement

  • stereotype threat

  • psychosocial outcomes (e.g., motivation, sense of self-efficacy, sense of belonging, etc.)

  • growth mindsets

  • academic trajectories

  • persistence

Controlling for multiple intersectional identities, our goal is to identify high-impact teaching practices that benefit all students, especially those from low-income, underrepresented, and marginalized backgrounds. This research will inform innovations in teaching and instructor professional development on campus and beyond!

If you would like more information, please email aphuong@berkeley.edu . Some of the D4E Lab’s core research areas include teaching and learning, organizational learning, faculty development, and student success programs.

Here’s an example publication of our work that is hyperlinked below:

Phuong, A. E., Nguyen, J. & Marie, D. (2017b). Evaluating an adaptive equity-oriented pedagogy: A study of its impacts in higher education. The Journal of Effective Teaching. 17(2), 5-44.

 

Keyword: 
Methodologies/Approaches, Case Studies, Surveys and Interviews, Professional Development
Details
D-lab Facilitator: 
Claudia von Vacano
Participant Technology Requirement: 
Laptop (optional)

Machine Learning Working Group

When & Where
Schedule: 
Alternating Wednesdays, 3-5PM, first meeting Wednesday September 5
Location: 
Barrows 356B: Convening Room
Description

**On hiatus Spring 2019**

Are you new to machine learning but do not know how to get started? Do you have experience with machine learning and are looking for a venue to practice presenting your research? Would you like to lead a coding walkthrough? If you answered yes to either of these questions, then come join the UC Berkeley D-Lab Machine Learning Working Group! 

This brown-bag series will introduce you to central themes in the form of workshop-style coding walkthroughs using R and Python.

In Fall 2018 the focus will be on unsupervised methods:

  - September 5: Principal Component Analysis   

  - September 19: k-means clustering  

  - October 3: hierarchical clustering  

  - October 17: Medoid partitioning  

  - October 31: tSNE  

  - November 14: UMAP  

  - November 28: TextXD / Latent Class Analysis (?)  

  - December 12: Lightning talks  

We will focus on key frameworks in R such as caret and SuperLearner and in Python like scikit-learn, tensorflow, and keras. 

We also encourage you to bring topics for discussion that focus on a variety of themes including data cleaning, visualization, automation, cloud computing, and parallel processing. 

Prior knowledge: R FUN!damentals: Parts 1 through 3 or previous intermediate working knowledge of R or Python FUN!damentals and previous work with NumPy and SciPy are recommended. 

Click here download install R

Click here to download RStudio Desktop Open Source License FREE

Click here to download Python Anaconda Distribution

Visit our GitHub repo at: https://github.com/dlab-berkeley/MachineLearningWG

Keyword: 
Python, R, Machine Learning
Details
D-lab Facilitator: 
Evan Muzzall
Participant Technology Requirement: 
Laptop

Cloud Working Group

When & Where
Schedule: 
By request "On-Demand" — or Wed 3-5pm breakout session from Machine Learning or Text Analysis WGs
Location: 
Barrows 356: Convening Room
Description

The group coordinates talks and trainings focused on cloud computing services such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform; as well as emerging cloud platforms on national infrastructure like XSEDE. The specific activities and topics will depend on the interests and needs of the members, including virtual machines, docker containers, high performance computing, and related topics.

The group is open to people of all skill levels and the activities in subgroups will accommodate interests from Absolute Beginner to Seasoned Pro. Mentors and consultants who have experience with cloud computing will be available during the sessions to work with members of the group.

 

 

Keyword: 
Software Tools, Python, R, Case Studies, Machine Learning, Text Analysis, Data Sources, Databases and Data Management, Web

Women in Numbers Working Group

When & Where
Schedule: 
Barrows 371
Description

D-Lab's newest working group hopes to bring together women scholars from a wide range of disciplines to engage in work that uses quantitative/computational/digital data and analysis. The group provides a forum for discussions on pressing issues and questions central to quantitative data analysis, discussions on pedagogy, data collection and analysis demonstrations and academic talks as well as any issues or concerns. We seek to promote quantitative analysis for all who feel their work falls under this umbrella. 

The purpose of the group is twofold: one, to foster an interdisciplinary community and discussion about current practices by bringing together women scholars at UC Berkeley who are working on (or hope to work on) topics at the intersection of their discipline and quantitative analysis; and two, to provide a safe space for women scholars to share their work and experiences as well as learn new topics and methods. 

Please join us for WiN's inaugural meeting, Friday October 13th, 2017, 1pm, 371 Barrows. We will discuss the semester's schedule and goals and get to know one another. Refreshments will be served. If you have any questions, please contact Nora Broege (nbroege@berkeley.edu).

Keyword: 
Software Tools, Quantitative Analysis

Women in Numbers Working Group

When & Where
Schedule: 
Barrows 371
Description

D-Lab's newest working group hopes to bring together women scholars from a wide range of disciplines to engage in work that uses quantitative/computational/digital data and analysis. The group provides a forum for discussions on pressing issues and questions central to quantitative data analysis, discussions on pedagogy, data collection and analysis demonstrations and academic talks as well as any issues or concerns. We seek to promote quantitative analysis for all who feel their work falls under this umbrella. 

The purpose of the group is twofold: one, to foster an interdisciplinary community and discussion about current practices by bringing together women scholars at UC Berkeley who are working on (or hope to work on) topics at the intersection of their discipline and quantitative analysis; and two, to provide a safe space for women scholars to share their work and experiences as well as learn new topics and methods. 

Please join us for WiN's inaugural meeting, Friday October 13th, 2017, 1pm, 371 Barrows. We will discuss the semester's schedule and goals and get to know one another. Refreshments will be served. If you have any questions, please contact Nora Broege (nbroege@berkeley.edu).

Keyword: 
Software Tools, Quantitative Analysis

Women in Numbers Working Group

When & Where
Schedule: 
Barrows 371
Description

D-Lab's newest working group hopes to bring together women scholars from a wide range of disciplines to engage in work that uses quantitative/computational/digital data and analysis. The group provides a forum for discussions on pressing issues and questions central to quantitative data analysis, discussions on pedagogy, data collection and analysis demonstrations and academic talks as well as any issues or concerns. We seek to promote quantitative analysis for all who feel their work falls under this umbrella. 

The purpose of the group is twofold: one, to foster an interdisciplinary community and discussion about current practices by bringing together women scholars at UC Berkeley who are working on (or hope to work on) topics at the intersection of their discipline and quantitative analysis; and two, to provide a safe space for women scholars to share their work and experiences as well as learn new topics and methods. 

Please join us for WiN's inaugural meeting, Friday October 13th, 2017, 1pm, 371 Barrows. We will discuss the semester's schedule and goals and get to know one another. Refreshments will be served. If you have any questions, please contact Nora Broege (nbroege@berkeley.edu).

Keyword: 
Software Tools, Quantitative Analysis

Women in Numbers Working Group

When & Where
Schedule: 
Barrows 371
Description

D-Lab's newest working group hopes to bring together women scholars from a wide range of disciplines to engage in work that uses quantitative/computational/digital data and analysis. The group provides a forum for discussions on pressing issues and questions central to quantitative data analysis, discussions on pedagogy, data collection and analysis demonstrations and academic talks as well as any issues or concerns. We seek to promote quantitative analysis for all who feel their work falls under this umbrella. 

The purpose of the group is twofold: one, to foster an interdisciplinary community and discussion about current practices by bringing together women scholars at UC Berkeley who are working on (or hope to work on) topics at the intersection of their discipline and quantitative analysis; and two, to provide a safe space for women scholars to share their work and experiences as well as learn new topics and methods. 

Please join us for WiN's inaugural meeting, Friday October 13th, 2017, 1pm, 371 Barrows. We will discuss the semester's schedule and goals and get to know one another. Refreshments will be served. If you have any questions, please contact Nora Broege (nbroege@berkeley.edu).

Keyword: 
Software Tools, Quantitative Analysis

Women in Numbers Working Group

When & Where
Schedule: 
Barrows 371
Description

D-Lab's newest working group hopes to bring together women scholars from a wide range of disciplines to engage in work that uses quantitative/computational/digital data and analysis. The group provides a forum for discussions on pressing issues and questions central to quantitative data analysis, discussions on pedagogy, data collection and analysis demonstrations and academic talks as well as any issues or concerns. We seek to promote quantitative analysis for all who feel their work falls under this umbrella. 

The purpose of the group is twofold: one, to foster an interdisciplinary community and discussion about current practices by bringing together women scholars at UC Berkeley who are working on (or hope to work on) topics at the intersection of their discipline and quantitative analysis; and two, to provide a safe space for women scholars to share their work and experiences as well as learn new topics and methods. 

Please join us for WiN's inaugural meeting, Friday October 13th, 2017, 1pm, 371 Barrows. We will discuss the semester's schedule and goals and get to know one another. Refreshments will be served. If you have any questions, please contact Nora Broege (nbroege@berkeley.edu).

Keyword: 
Software Tools, Quantitative Analysis

Women in Numbers Working Group

When & Where
Schedule: 
Barrows 371
Description

D-Lab's newest working group hopes to bring together women scholars from a wide range of disciplines to engage in work that uses quantitative/computational/digital data and analysis. The group provides a forum for discussions on pressing issues and questions central to quantitative data analysis, discussions on pedagogy, data collection and analysis demonstrations and academic talks as well as any issues or concerns. We seek to promote quantitative analysis for all who feel their work falls under this umbrella. 

The purpose of the group is twofold: one, to foster an interdisciplinary community and discussion about current practices by bringing together women scholars at UC Berkeley who are working on (or hope to work on) topics at the intersection of their discipline and quantitative analysis; and two, to provide a safe space for women scholars to share their work and experiences as well as learn new topics and methods. 

Please join us for WiN's inaugural meeting, Friday October 13th, 2017, 1pm, 371 Barrows. We will discuss the semester's schedule and goals and get to know one another. Refreshments will be served. If you have any questions, please contact Nora Broege (nbroege@berkeley.edu).

Keyword: 
Software Tools, Quantitative Analysis

Women in Numbers Working Group

When & Where
Schedule: 
Barrows 371
Description

D-Lab's newest working group hopes to bring together women scholars from a wide range of disciplines to engage in work that uses quantitative/computational/digital data and analysis. The group provides a forum for discussions on pressing issues and questions central to quantitative data analysis, discussions on pedagogy, data collection and analysis demonstrations and academic talks as well as any issues or concerns. We seek to promote quantitative analysis for all who feel their work falls under this umbrella. 

The purpose of the group is twofold: one, to foster an interdisciplinary community and discussion about current practices by bringing together women scholars at UC Berkeley who are working on (or hope to work on) topics at the intersection of their discipline and quantitative analysis; and two, to provide a safe space for women scholars to share their work and experiences as well as learn new topics and methods. 

Please join us for WiN's inaugural meeting, Friday October 13th, 2017, 1pm, 371 Barrows. We will discuss the semester's schedule and goals and get to know one another. Refreshments will be served. If you have any questions, please contact Nora Broege (nbroege@berkeley.edu).

Keyword: 
Software Tools, Quantitative Analysis

Qualitative Methods Group (QMG)

When & Where
Schedule: 
varies - look for "QMG Presents" on our calendar or trainings page
Location: 
356 Barrows Hall
Description

QMG continues this fall! To register for workshops and presentations, look for events titled "QMG Presents" on the D-Lab calendar or trainings page.

This group is for students who want to deepen their understanding of qualitative, as well as mixed-methods, research though attendance at the following:

  • methodological presentations, including extensive Q&A sessions with experienced researchers,
  • workshops on a variety of relevant topics, such as using qualitative data analysis (QDA) software and drawing conclusions using qualitative data, and
  • planning and work meetings, used to provide and receive peer support and feedback, as well as determine topics for presentations and workshops.

Participants have various levels of education about and experience with qualitative and mixed-methods research. Some have a background only in qualitative or even quantitative research, while others have a background in mixed-methods research. All members of the UC Berkeley community, regardless of their research education or experience, are welcome!

Please note that workshops and presentations may be scheduled as little as two weeks in advance. Keep an eye on D-Lab's website and newsletter for up-to-date information.

Please contact D-Lab's Qualitative Research Lead, Josué Meléndez Rodríguez, at melendez@berkeley.edu with any questions or comments

Keyword: 
Digital Humanities, Qualitative Analysis
Details
D-lab Facilitator: 
Josué Meléndez Rodríguez

Women in Numbers Working Group

When & Where
Schedule: 
Barrows 371
Description

D-Lab's newest working group hopes to bring together women scholars from a wide range of disciplines to engage in work that uses quantitative/computational/digital data and analysis. The group provides a forum for discussions on pressing issues and questions central to quantitative data analysis, discussions on pedagogy, data collection and analysis demonstrations and academic talks as well as any issues or concerns. We seek to promote quantitative analysis for all who feel their work falls under this umbrella. 

The purpose of the group is twofold: one, to foster an interdisciplinary community and discussion about current practices by bringing together women scholars at UC Berkeley who are working on (or hope to work on) topics at the intersection of their discipline and quantitative analysis; and two, to provide a safe space for women scholars to share their work and experiences as well as learn new topics and methods. 

Please join us for WiN's inaugural meeting, Friday October 13th, 2017, 1pm, 371 Barrows. We will discuss the semester's schedule and goals and get to know one another. Refreshments will be served. If you have any questions, please contact Nora Broege (nbroege@berkeley.edu).

Keyword: 
Software Tools, Quantitative Analysis

Math for Machine Learning

When & Where
Schedule: 
(Math for Machine Learning will reconvene in Summer 2018)
Location: 
Barrows 356: D-Lab Convening Room
Description

Although programming aspects of machine learning in R and Python are challenging in themselves, the need to understand fundamental mathematical aspects is obligatory. It is unfortunately common in the social sciences to apply machine learning techniques to solve a particular problem without necessarily understanding their numerical underpinnings. Join the Math for Machine Learning summer reading group to engage with some of the math behind machine learning and your favorite algorithms!

We will meet Wednesdays during summer 2017 from 12-2pm on the following dates in 356 Barrows: May 24, June 7, June 21, July 5, July 19, and August 2.  Math for Machine Learning will reconvene in Summer 2018 and the Machine Learning Working Group will reconvene as normal on September 8, 2017 at 12:30pm in 356 Barrows Hall.

Keyword: 
Software Tools, Methodologies/Approaches, Machine Learning
Details
D-lab Facilitator: 
Evan Muzzall

Math for Machine Learning

When & Where
Schedule: 
(Math for Machine Learning will reconvene in Summer 2018)
Location: 
Barrows 356: D-Lab Convening Room
Description

Although programming aspects of machine learning in R and Python are challenging in themselves, the need to understand fundamental mathematical aspects is obligatory. It is unfortunately common in the social sciences to apply machine learning techniques to solve a particular problem without necessarily understanding their numerical underpinnings. Join the Math for Machine Learning summer reading group to engage with some of the math behind machine learning and your favorite algorithms!

We will meet Wednesdays during summer 2017 from 12-2pm on the following dates in 356 Barrows: May 24, June 7, June 21, July 5, July 19, and August 2.  Math for Machine Learning will reconvene in Summer 2018 and the Machine Learning Working Group will reconvene as normal on September 8, 2017 at 12:30pm in 356 Barrows Hall.

Keyword: 
Software Tools, Methodologies/Approaches, Machine Learning
Details
D-lab Facilitator: 
Evan Muzzall

Math for Machine Learning

When & Where
Schedule: 
(Math for Machine Learning will reconvene in Summer 2018)
Location: 
Barrows 356: D-Lab Convening Room
Description

Although programming aspects of machine learning in R and Python are challenging in themselves, the need to understand fundamental mathematical aspects is obligatory. It is unfortunately common in the social sciences to apply machine learning techniques to solve a particular problem without necessarily understanding their numerical underpinnings. Join the Math for Machine Learning summer reading group to engage with some of the math behind machine learning and your favorite algorithms!

We will meet Wednesdays during summer 2017 from 12-2pm on the following dates in 356 Barrows: May 24, June 7, June 21, July 5, July 19, and August 2.  Math for Machine Learning will reconvene in Summer 2018 and the Machine Learning Working Group will reconvene as normal on September 8, 2017 at 12:30pm in 356 Barrows Hall.

Keyword: 
Software Tools, Methodologies/Approaches, Machine Learning
Details
D-lab Facilitator: 
Evan Muzzall

Math for Machine Learning

When & Where
Schedule: 
(Math for Machine Learning will reconvene in Summer 2018)
Location: 
Barrows 356: D-Lab Convening Room
Description

Although programming aspects of machine learning in R and Python are challenging in themselves, the need to understand fundamental mathematical aspects is obligatory. It is unfortunately common in the social sciences to apply machine learning techniques to solve a particular problem without necessarily understanding their numerical underpinnings. Join the Math for Machine Learning summer reading group to engage with some of the math behind machine learning and your favorite algorithms!

We will meet Wednesdays during summer 2017 from 12-2pm on the following dates in 356 Barrows: May 24, June 7, June 21, July 5, July 19, and August 2.  Math for Machine Learning will reconvene in Summer 2018 and the Machine Learning Working Group will reconvene as normal on September 8, 2017 at 12:30pm in 356 Barrows Hall.

Keyword: 
Software Tools, Methodologies/Approaches, Machine Learning
Details
D-lab Facilitator: 
Evan Muzzall

Math for Machine Learning

When & Where
Schedule: 
(Math for Machine Learning will reconvene in Summer 2018)
Location: 
Barrows 356: D-Lab Convening Room
Description

Although programming aspects of machine learning in R and Python are challenging in themselves, the need to understand fundamental mathematical aspects is obligatory. It is unfortunately common in the social sciences to apply machine learning techniques to solve a particular problem without necessarily understanding their numerical underpinnings. Join the Math for Machine Learning summer reading group to engage with some of the math behind machine learning and your favorite algorithms!

We will meet Wednesdays during summer 2017 from 12-2pm on the following dates in 356 Barrows: May 24, June 7, June 21, July 5, July 19, and August 2.  Math for Machine Learning will reconvene in Summer 2018 and the Machine Learning Working Group will reconvene as normal on September 8, 2017 at 12:30pm in 356 Barrows Hall.

Keyword: 
Software Tools, Methodologies/Approaches, Machine Learning
Details
D-lab Facilitator: 
Evan Muzzall

Research in Practice Working Group

When & Where
Schedule: 
Alternating Tuesdays, 4:00-5:00pm, reconvening February 28th
Location: 
Barrows 356
Description

So you’ve got some of your graduate classes under your belt, and it's time to begin an original research project. But how exactly are you going to go about surveying voters in Tanzania, interviewing public health officials in France, or running focus groups with seasonal farm workers in the Central Valley? How will you decide whom to speak to, efficiently collect your data, and handle the challenges of working far from home (or even close to home)?  

The Research in Practice Working Group will cover the logistics and ethics of collecting both quantitative and qualitative data in a diverse range of settings. We aim to provide support to graduate students across many disciplines, including the social sciences, public policy, and public health. We will provide a mixture of workshops on specific research tools (such as software for computer-assisted interviewing) and panel discussions on various aspects of the research process (such as hiring research assistants, doing archival research, and thinking through the ethics of doing research in low income countries). We’ll also hold more informal meetings where students can come together and discuss their work.  

This semester we will have sessions on the following evenings: 

  • Februrary 28
  • March 14
  • April 11
  • April 27
  • May 9

Feel free to contact Justine Davis (justine.davis@berkeley.edu) and Allison Grossman (allison.grossman@berkeley.edu) with any questions. See you soon!

Keyword: 
Qualitative Analysis, Quantitative Analysis, Surveys and Interviews, Data Sources
Details
D-lab Facilitator: 
Stephanie Smith

Python Practice Working Group

When & Where
Schedule: 
Mondays, 4:00-5:30pm, reconvening January 30, 2016
Location: 
Barrows 356: D-Lab Convening Room
Description

Python Practice is a working group on the UC Berkeley campus, sponsored by the D-Lab. We hold informal weekly meetings teaching and learning about different topics in the Python programming language, especially for social science, data science, and visualization.

This is ideal for learners who are new to Python — you'll have a group of peers instead of being by yourself. If you're a little more advanced, you can try to teach something. You are welcome to attend any or all meetings throughout the semester.

You can check out or improve our learning resources. We also have a list of topics from past meetings. Please send an email or join us on Mondays from 4:00-5:30pm at D-Lab (356 Barrows Hall, UC Berkeley). 

Coordinators: Megan Carey (mcarey@berkeley.edu) and Liza Praprotnik (liza.praprotnik@berkeley.edu) 

Keyword: 
Python
Details
D-lab Facilitator: 
Patty Frontiera

Berkeley Learning Analytics Group

When & Where
Schedule: 
Tuesdays, 4:30 to 5:30pm, reconvening March 21st
Location: 
Barrows 356: D-Lab Convening Room
Description

The emerging discipline of Learning Analytics represents a unique opportunity to build bridges between campus researchers, research computing, and teaching and learning software and practitioners (see https://solaresearch.org/). The Berkeley Learning Analytics Group aims to nurture these connections through discussions, panels, and speakers focused on key issues in the field and the promotion of relevant campus projects.

Learning Analytics is a relatively new field in big data analysis that focuses on the user activities captured in the broader learning environment. Transactional and real time system events, which have usually been recorded for system administration purposes, when combined with traditional business intelligence data, are now being recognized as a major foundational component of various “student success” initiatives and services that aim to promote retention, remediation, and improved learning outcomes. See http://tinyurl.com/z6f75ao for a visual of the intersections between user event and demographic, academic, and financial data.

Upcoming Meetings: 

Tuesday, March 21, 4:30-5:30pm

Tuesday, April 4, 2016, 4:30 - 5:30pm

Tuesday, April 18, 2016, 4:30 - 5:30pm

Tuesday, May 2, 2016, 4:30 - 5:30pm

 

While some of you have familiarity with the wider definition of Learning Analytics, there are many readily available sources to provide additional background.

We ask that you register for the meeting to ensure that there is adequate space and seating.

Looking forward to seeing you there!

 

Pages