September 19, 2025
CSSCR Autumn Newsletter 2025
Welcome to the autumn quarter and the 2025-2026 academic year!
CSSCR has used the summer to do some updates and maintenance. Among other things, we have deployed a new poster printer, a Canon TM-350. The new printer replaces a 10-year-old HP DesignJet T520 printer that was suffering from both quality issues and some maintenance and reliability problems. The new printer uses 36″ wide paper, and produces higher quality prints with ink that is more smudge and water resistant. The HP printer will serve as a backup.
With the new printer comes a new way to request poster printing. We now have a Google form-based submission process that should streamline requests and ensures we receive the information we need to print your poster. Details can be found on our Poster Printing page.
This year CSSCR will continue to offer a series of workshops each quarter. You can read about the workshops and sign up for them below in this newsletter.
Note that our consulting services will be closed on September 22 and 23. We will open on September 24 and drop-in consulting will be open from 8am to 6pm weekdays, except for holidays (Nov 11 and Nov 27-28). As part of the college cost cutting, CSSCR will no longer be offering evening on-line or in-person consulting.
We look forward to serving you this quarter. Have a great fall!
Darryl Holman
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CSSCR Workshop Offerings Autumn Quarter 2025
***Workshops are listed in the order of the dates they will be delivered***
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Introduction to Python
Description: This workshop assumes no familiarity with python and is even appropriate for new coders as well. We will start the basics including installation, choosing a programming environment, learning some key syntax, and ending with data analysis.
- Instructor: Tynan Challenor, CSSCR Consultant
- Date: Wednesday, October 8, 2025
- Time: 10:30am – 11:50am
- Location: Savery 121 (Small Lab)
- Register here.
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Introduction to R
Description: This workshop aims to introduce basic tools and functions of R for reading, management and examining datasets. Attendees are assumed to have little to no experience with R.
- Instructor: Alireza Aminkhaki, CSSCR Consultant
- Date: Wednesday, October 8, 2025
- Time: 1:30pm – 2:50pm
- Location: Savery 121 (Small Lab)
- Register here.
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Efficient R Programming: Working with Many Columns, Functions, and Models
Description: A good rule of thumb in data wrangling and programming is: if you find yourself copying and pasting a block of code more than twice, it’s time to stop and think about a more efficient approach. This course will introduce functional programming and other techniques to reduce redundancy and enhance the computational efficiency of your R code. We will cover practical skills frequently used in data projects, such as manipulating multiple columns, writing anonymous functions, using map(), nesting dataframes within tibbles, and running multiple regressions and comparing them results simultaneously. Attendees are expected to have basic familiarity with data wrangling using dplyr in R.
- Instructor: Brian Leung, CSSCR Consultant
- Date: Thursday, October 9, 2025
- Time: 10:30am – 11:50am
- Location: Savery 121 (Small Lab)
- Register here.
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Introduction to STATA 13
Description: In this crash course, we will cover topics from viewing, cleaning, to graphing data in stata. The lab computers have the software installed.
- Instructor: Biying Zheng, CSSCR Consultant
- Date: Wednesday, October 15, 2025
- Time: 9:00am – 10:20am
- Location: Savery 121 (Small Lab)
- Register here.
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Data Wrangling in R
Description: This workshop will cover some of R’s useful tools for data management and exploration. Most of class will be devoted to learning Hadley Wickham’s excellent “tidyr” and “dplyr” packages. Attendees are assumed to have basic familiarity with R/RStudio.
- Instructor: Victoria Sass, CSSCR Consultant
- Date: Thursday, October 16, 2025
- Time: 1:30pm – 2:50pm
- Location: Savery 121 (Small Lab)
- Register here.
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Machine Learning Methods for Supervised Single-Label and Multi-Label Classification
Description: This workshop aims to provide attendees with the foundational knowledge needed to perform supervised single and multi-label classification using both traditional machine learning methods (Logistic Regression, Multinomial Logistic Regression) and more advanced machine methods (Random Forests and Neural Networks). The workshop will also cover distinguishing between supervised and unsupervised classification research questions. Attendees are expected to have a basic familiarity with conducting statistical analysis in Python.
- Instructor: Yale Quan, CSSCR Consultant
- Date: Thursday, October 23, 2025
- Time: 1:00pm – 2:20pm
- Location: Savery 121 (Small Lab)
- Register here.
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Introduction to Web Mapping
Description: Learn how to use ArcGIS Online to create interactive web maps and bring your spatial data to life. We’ll also build a dashboard to showcase tabular data that connects to your web map—a great way to advance your data visualization skills.
- Instructor: Yuying Xie, CSSCR Consultant
- Date: Monday, October 27, 2025
- Time: 12:00pm – 1:20pm
- Location: Savery 117 (Big Lab)
- Register here.
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Introduction to Thematic Analysis in Atlas.ti
Description: This workshop provides a practical introduction to working in ATLAS.ti, by marrying the fundamentals of the qualitative methodology with the functionality of this program. This will include conceptualizing and creating quotations, codes, memos, and comments followed by establishing thematic relationships through systematic analysis. The hands-on course assumes no familiarity with Atlas.ti
- Instructor: Baishakhi Basu, CSSCR Consultant
- Date: Wednesday, October 29, 2025
- Time: 3:00pm – 4:20pm
- Location: Savery 121 (Small Lab)
- Register here.
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If you have questions or problems registering please send a note to CSSCR@uw.edu.
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ICPSR access at CSSCR
CSSCR maintains the UW membership in the Inter-university Consortium for Political and Social Research (ICPSR). All members of the UW community have direct access to ICPSR’s data archive of over 9,900 studies. Please contact us (csscr@uw.edu) if you have questions or need more information about using this resource for accessing data or adding your data to the ICPSR repository. ICPSR new data releases and other news can be found here.
CSSCR in-house data sets
CSSCR maintains an extensive catalog of data sets beyond those available through ICPSR. Please contact us for more information about data sets you are looking for. This quarter, we have added two new data sets to our collection:
- 2024 Detailed General Election Data for President—US County Level
- 2024 Detailed Presidential Election Data—Washington Precinct Level
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About CSSCR
The Center for Social Science Computation and Research (CSSCR) is an interdepartmental computer center in the College of Arts and Sciences at the University of Washington. CSSCR provides facilities and consulting support for computing activity related to teaching and research at the University for our member departments, schools, and centers.
Hours of Operation
CSSCR is primarily an in-person service
Drop in In-Person Consulting (Savery 119)—no appointment needed just stop on by!
Autumn Hours: Monday-Friday 8AM—6PM
If you prefer an online meeting during the above times, e-mail csscr@uw.edu with your question or problem and we will arrange a Zoom meeting or answer your questions via e-mail.
Main office and other services (Savery 110)
Main office is open Monday-Friday 9AM-5PM — Please send queries to csscr@uw.edu.
We are closed weekends and for all University holidays.
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Contact Us:
Center for Social Science Computation & Research
University of Washington
110 Savery Hall
Box 353345
Seattle, Washington 98195 U.S.A.
(206) 543-8110 csscr@uw.edu http://csscr.washington.edu
If you would like to request academic accommodations due to a disability, please contact Disabled Student Services, 448 Schmitz, 543-8924 (V/TDD). If you have a letter from Disabled Student Services requesting academic Accommodations, please present the letter to Darryl Holman at CSSCR so we may discuss the necessary classroom accommodations.
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