DS-2003 Communicating with Data (Fall 2022)

Syllabus Schedule Assignments
PreClass Finals Github
Week 1 Week 2 Week 3 Week 4
Week 5 Week 6 Week 7 Week 8
Week 9 Week 10 Week 11 Week 12
Week 13 Week 14 Week 15 Week 16

DS 2003: Communicating with Data


Section 001 (17703) (T/Th 9:30-10:45a; Ridley Hall G006)

Bruce Corliss, PhD

TA: Shan Xu

Other Information

Subject Area and Catalog Number: Data Science, DS 2003, Section 17703 -or- 19026

Class Title: Communicating with Data

Level: Undergraduate

Credit Type: Grade (A-F)

About the Course

This course provides practical experiences about how data is commonly used in communication. The general objective is to make you familiar with how to effectively summarize and visualize data in order to share your story. All the examples and analyses will be done mainly in R, with a particular focus on graphical presentations with the R visualization tools like ggplot2, Plotly, and Shiny. Additionally, there will be readings and discussions about how graphs are used in our everyday lives and how, as data scientists, we can effectively, and ethically, display data for stakeholders.

What You Will Learn Along the Way

By the end of the course, I hope that you will:

  1. Be adept in summarizing and visualizing data using R.

  2. Be able to construct meaningful messages from data and effectively deliver them with visualization tools.

  3. Have confidence in interacting and collaborating with colleagues.

  4. Understand how charts can be used (intentionally or unintentionally) to manipulate results and mislead readers. And how to avoid making these mistakes.

How You'll Know You Are Learning

This course will be a combination of lectures and labs. As the course progresses, it will become more lab based. During lab sessions, you should expect to spend your time on your computer working through examples in small groups. The best way to become comfortable with the material is to continually practice. The idea by making this lab-based class is that you can practice in an environment where you can ask questions and trouble-shoot with peers. Every class won't go perfectly, but week after week you should be more comfortable with R and the material.

 Reflections (25%): There will be two written reflections based on the course reading, How Charts Lie. Students should draw on their own opinions and thoughts to answer the prompt while framing their response in what they have learned from the book.
 Coding Assignments (30%): There will be weekly assignments due. These assignments are meant to reiterate the content from the week. Students are asked to work independently on these assignments. 
 Mid-term Project (20%): A group project will be assigned for the midterm and consist of finding and cleaning data, developing a question, and 	   	designing 2-3 charts that assist in answering the proposed question.
 Final Project (25%): The final project will also be group-based. The end product will be a Shiny interactive app which displays data and graphs. 

These percents are meant as a reference and are subject to change- in reality they are done in a points based manner for the grading. If we don't get through all the assignments, I am not going to re-weigh each assignment to preserve these ratios exactly.


Courses carrying a Data Science subject area use the following grading system: A, A-; B+, B, B-; C+, C, C-; D+, D, D-; F. The symbol W is used when a student officially drops a course before its completion or if the student withdraws from an academic program of the University.

Grading Scale:

Summary of grading for the course: a typical class has about 10% B+'s, 20% A's, and the rest A-'s. There may be a handful of lower grades. I tend to grade harshly on the reflection assignments and the group projects. At the end of the semester, I will start by curving the thresholds for the letter grades to initially match this distribution. I will then cross check the work of individual students near the threshold to the work and merit of previous years and the other section offered for this class. I then adjust the threshold as necessary; the ratios may change from this initial distribution. I will also consider the class participation of students near the initial grade thresholds, but I will not adjust or bump an individual student's grade over others. I will only adjust the global threshold. Over the course of the semester, if I think that you are going to get below a B+, I will reach out to you and provide extra support.

My thoughts behind this:

  1. I uphold equity by identifying the students who are having trouble and providing them with extra support, along with opportunities to improve their grade. I do not want to give any grade below B+ (but I will if I absolutely must).
  2. I uphold equality by awarding grades based on merit that is cross referenced to the other section for this class and in previous years.
  3. I give class participation special consideration for adjusting the global threshold for final letter grades.
  4. I do not bump an individual student’s grade based on class participation, or how strong of a student they are, or how much I approve of them. It is impossible to do that without my personal biases influencing the process.

Important note: after the final project is due, I do not give students an opportunity to improve their grade. I do not "hold court" to hear their stories, nor do I bump or adjust a student's grade, nor give them an opportunity to resubmit assignments or do additional work. Before the final project is due, students may resubmit their coding assignments one time to improve their grade.

University of Virginia Honor System

All work should be pledged in the spirit of the Honor System at the University of Virginia. The instructor will indicate which assignments and activities are to be done individually and which permit collaboration. The following pledge should be written out at the end of all quizzes, examinations, individual assignments, and papers: “I pledge that I have neither given nor received help on this examination (quiz, assignment, etc.)”. The pledge must be signed by the student. For more information, visit www.virginia.edu/honor.

Required Texts

  Cairo, A. How Charts Lie. WW Norton & Company. 2019.  

Tech Stack

 R/Rstudio - Environment in which we will code 

 Collab – Submission of assignments, grades, class-wide communications 

 Github – Repository of class material 


Cheat Sheets

SDAC and Other Special Accommodations

If you have been identified as a Student Disability Access Center (SDAC) student, please let the Center know you are taking this class. If you suspect you should be an SDAC student, please schedule an appointment with them for an evaluation. I happily and discretely provide the recommended accommodations for those students identified by the SDAC. Please contact your instructor one week before an exam so we can make appropriate accommodations. Website: https://www.studenthealth.virginia.edu/sdac

If you are affected by a situation that falls within issues addressed by the SDAC and the instructor and staff are not informed about this in advance, this prevents us from helping during the semester, and it is unfair to request special considerations at the end of the term or after work is completed. We request you inform the instructor as early in the term as possible your circumstances. If you have other special circumstances (athletics, other university-related activities, etc.) please contact your instructor and/or TA as soon as you know these may affect you in class.

Student Mental Health and Wellbeing:

The University of Virginia is committed to advancing the mental health and wellbeing of its students, while acknowledging that a variety of issues, such as strained relationships, increased anxiety, alcohol/drug problems, and depression, directly impacts students’ academic performance. If you or someone you know is feeling overwhelmed, depressed, and/or in need of support, services are available. For help, contact Counseling and Psychological Services (CAPS) at 434-243-5150 Monday-Friday, 8:00am-4:30pm and after-hours including weekends and holidays. For a comprehensive list of services provided by CAPS including individual therapy, group therapy, crisis services, and Outreach and Consultation, visit https://www.studenthealth.virginia.edu/caps.

For a list of online resources students may access independently, visit https://www.studenthealth.virginia.edu/caps-online-resources.

For access to community mental health referrals, visit https://www.studenthealth.virginia.edu/community-referrals.

Diversity and Inclusion

The School of Data Science expects everyone to contribute to an inclusive and respectful classroom culture that reflects the School’s commitment to being a space in which you can find true belonging and a sense of shared community. The diversity (referring to the multiple ways that we identify ourselves, including but not limited to race, color, national origin, language, sex, disability, age, sexual orientation, gender identity, religion, creed, ancestry, belief, veteran status, or genetic information) of our classroom is a strength. You are expected to respectfully embrace the opportunity to engage, collaborate, and learn with/from a diverse team of classmates.

Additionally, I will note that it is possible that, even though our course material is primarily scientific in nature, there may be covert biases in the material due to the lens with which it was written. I welcome feedback and suggestions to improve upon the inclusivity of the material. We are all responsible for ensuring that our actions/experience align with our stated values. Please consider yourselves to be my accountability partners in creating an inclusive environment that supports a diversity of perspectives, do not hesitate to reach out if you have concerns, ideas, or questions about your experience.

As part of our shared effort to promote a classroom culture of inclusion, we will each have the opportunity to indicate our preferred name and pronouns. I will do my best to refer to all students accordingly.

If you find yourself in need of additional support, please consider the following resources:

SDS Associate Dean for DEI, Siri Russell ssr5v@virginia.edu

UVA Just Report It https://justreportit.sites.virginia.edu/