EDS 240: Lecture 1.1

Course logistics & syllabus

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Week 1 | January 7^th, 2026

Welcome to EDS 240!

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This course will focus on the basic principles for effective communication through data visualization and using programmatic tools and workflows for creating and sharing data visualizations with diverse audiences.

Artwork by Allison Horst

Meeting times & locations

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• Lecture: Wednesdays 1:30-4:00pm PT
  • Bren 3022A (SCF)
• Lab: Thursdays 12:30-1:50pm PT
  • Bren 3022A (SCF)

Teaching team

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Instructor

Sam Shanny-Csik
Email: scsik@ucsb.edu
Learn more: samanthacsik.github.io
Student hours: Tuesdays 1:00-2:00pm

Co-instructor

Annie Adams
Email: aradams@ucsb.edu
Learn more: annieradams.github.io
Student hours: Thursdays 2:00-3:00pm

Meet one another!

Spend the next few minutes getting to know your Learning Partners! Below are some conversation starters:

Where do you feel most at home?

What parts of Santa Barbara have you enjoyed exploring?

What’s the most exciting thing you’ve learned this year, so far?

What’s your favorite color or typeface?

02:00

Student Resources

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• Disabled Students Program (DSP): https://dsp.sa.ucsb.edu/students/how-apply
• Basic Needs Resources & Food Security: https://basicneeds.ucsb.edu/ (schedule a CalFresh Appoinment or Basic Needs Advising Session)
• Counseling & Psychological Services (CAPS): http://caps.sa.ucsb.edu
• Resource Center for Sexual and Gender Diversity (RCSGD): https://rcsgd.sa.ucsb.edu/
• Undocumented Student Services (USS) Program: https://uss.sa.ucsb.edu/
• Campus Learning Assistance Services (CLAS): http://clas.sa.ucsb.edu
• Student Resource Building (SRB): http://www.sa.ucsb.edu/student-resource-building/home
• Multicultural Center (MCC): http://mcc.sa.ucsb.edu/
• Campus Advocacy, Resources, & Education (CARE): http://wgse.sa.ucsb.edu/care/home
• Financial Crisis Response Team: financialcrisis@sa.ucsb.edu (contact)
• Health and Wellness: https://wellbeing.ucsb.edu/

Applying for DSP accommodations is a multi-step process and students should begin this as soon as possible as it can take 10 days to process applications.

Step 1: Submit an application with DSP, upload documentation and select the services needed.

Step 2: For exam accommodations (more time, private setting, etc.) students need to submit a proctor request for each exam in each course that they need accommodations for. DSP has instructions on their website for completing these tasks and students can login to the DSP portal to check the status of their application and requests.

Everything you’ll need lives on the course website

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https://eds-240-data-viz.github.io/

Please read the syllabus in full this week, if you haven’t already!

Tentative Schedule & Materials

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Week	Date	Tentative Topic
1	1/07	course logistics, intro, {ggplot2} review
2	1/14	tbd
3	1/21	tbd
4	1/28	tbd
5	2/05	tbd
6	2/11	tbd
7	2/18	tbd
8	2/25	tbd
9	3/04	tbd
10	3/11	tbd

Complete all items under Pre-class Prep before lecture

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Please be sure to carefully complete all required prep (e.g. installing packages, downloading data) under the Pre-class Prep section (organized by week) before lecture – be mindful that some items may take time to download/install.

It is highly recommended that you do this well in advance¹ of attending lecture.

Assignments

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Your course grade will be based off the following:

• 3 Homeworks (HWs) - longer assignments where you’ll apply conceptual knowledge & technical skills to data viz tasks
• 5 Final Project Milestones (FPMs) - required milestones designed to help you make ssteady progress on y our final project deliverables
• 3 Self-reflections (SRs) - a place to reflect on your learning plan / goals, challenges, etc.
• 3 rounds of Peer Feedback (PF) - opportunities to give and receive constructive feedback on your final project data visualizations (lab sessions, weeks 7-9)

PLEASE NOTE: Your last FPM is an in-person flash talk during finals week (date TBD)

How will I be evaluated?

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This class will implement an alternative grading approach called specifications (specs) grading.

“an alternative grading method where instructors create a list of specifications that describe the qualities and characteristics of a successful submission for an assignment. Student work is graded holistically based on those specifications, earning a single mark: “Satisfactory” or “Not Yet”. Students have the chance to use feedback by revisiting and resubmitting for full credit.”

-expert from “Grading for Growth: A Guide to Alternative Grading Practices That Promote Authentic Learning and Student Engagement in Higher Education”, by David Clark & Robert Talbert

Why Specs grading?

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“Traditional” grading can come with some challenges:

• lacks feedback loops
• benefits those who learn fast or have prior experience
• bias-prone (e.g. awarding points, granting extensions)
• can discourage learning for its own sake
• can promote unhealthy student-instructor relationships

Traditional grading = points are awarded for correct answers on assessments; graded assessments contribute some percentage towards your overall course grade; course score (typically a percentage) is convered to a letter grade

How does specs grading look in practice for this course?

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each assignment will have a clear rubric that outlines what must be completed and how in order to receive a Satisfactory mark; not meeting all specifications results in a Not Yet mark

if you receive a Not Yet mark, you will receive feedback from your instructors and have one opportunity to revise & resubmit at no penalty to earn a Satisfactory mark

earn tokens by completing self reflections (SRs 1 & 2) and by attending lab (weeks 1-6)

trade tokens for a second revision & resubmission attempt and / or 24hr extension on assignments; track & use your tokens using the EDS 240 Token Dashboard [ADD LINK]

Everyone has different responsibilities & demands – tokens give you the power and freedom to ask for the accommodations you need. You do not need to provide a reason to request an extension or resubmission, but you must have enough tokens to do so.

All assignments are published

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All assignments (HWs, FPMs, SRs) are published on the assignments page.

You are not expected to look ahead, but materials are available so you can see what’s coming throughout the quarter and plan your time, if helpful.

Please note: we will not be grading assignments before thier listed due dates.

Policy on Generative AI (GenAI)

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You may use GenAI tools in this class as long as it aligns with the learning outcomes or goals associated with assignments. You are fully responsible for the information you submit based on a GenAI query.

Permitted use:

• Troubleshooting code or technical issues
• Clarifying concepts, methods, documentation
• Iterating on your research question
• Strengthening your prose through revision suggestions
• As a sounding board to explore ideas or approaches

Not permitted use:

• Relying on AI to complete substantial portions of an assignment in place of your own reasoning and understanding
• Using GenAI output without verifying its correctness or ensuring alignment with the defined task(s)

To promote transparency and support reflective practice in your use of generative AI tools, you must submit a GenAI Statement of Use with each assignment.

Attendance is important!

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For both building a collaborative learning community and because it mirrors professional expectations (it’s also UCSB policy).

If you need to miss a class, be proactive and work with your instructors on a catch up plan.

Please stay home if you’re sick!

Ask questions in our Slack channel

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All course-related content questions should be asked in the #eds-240-data-viz channel – oftentimes there are others who have the same question and will benefit from seeing the discussion.

We will do our best to respond to Slack questions within 24hr (during the week). Responses after hours (5pm - 9am) are not guaranteed.

What is EDS 240?

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EDS 240: Data Visualization and Communication is about two related, but distinct things:

1. The theory of effective communication and data design

How people perceive and interpret graphical information
Human-centered design as it relates to data visualizations

2. The physical act of building effective data visualizations using software and data science tools

Using the Grammar of Graphics / {ggplot2} framework to create effective, truthful, and beautiful data visualizations

What is EDS 240?

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The topic of data visualization is pretty darn massive.

We cannot and will not cover every data visualization type, consideration, package, etc.

We will work towards a conceptual and technical understanding of data viz fundamentals.

Data viz is a science and technical skill, but there’s also a lot of space for creativity.

What you create can be used in your professional portfolio! The more you put in, the more you’ll get out.

Course Learning Objectives

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Identify which types of visualizations are most appropriate for your data and your audience

Prepare (e.g. clean, explore, wrangle) data so that it’s appropriately formatted for building data visualizations

Build effective, responsible, accessible, and aesthetically-pleasing visualizations using the R programming language, and specifically {ggplot2} + ggplot2 extension packages

Write code from scratch and interpret and adapt code written by others

Apply a DEI (Diversity, Equity & Inclusion) lens to the process of designing data visualizations

Assess, critique, and provide constructive feedback on data visualizations

Take a Break

~ This is the end of Lesson 1 (of 3) ~

05:00