Course Information

Course Location Meeting Days Time
Chambers 2234 Tuesdays & Thursdays 1:10-2:25pm

Instructor Information

Instructor Information Office Location Hours
Dr. Jason S. Byers Main Office
Chambers 2256
MW: 3-4pm

(704) 894-2760
Data CATS, drop in hours
Hurt Hub


Course Home
Everything you need for this class (announcements, resources, assignments and other activities) will be posted on the course website. Please plan to check the page regularly.

Course Meeting Link

Course Description
Data plays a central and important role in modern society. Nearly every web search, phone call, transaction, and countless other activities are tracked, analyzed, and used to predict future actions. Data science is a collection of analytical and computational methods to enable insight, understanding and predictions to be drawn from data. This course provides an introduction to the methods of data science, including visualization, manipulation, programming and modeling, in the context of social justice. All work will be done in R, a freely available data analysis software.

Learning Outcomes
Together, we will strive for your individual and collective success in achieving the learning outcomes of this course. At the conclusion of this course, students will be able to:

  • Define and describe the varied nature of data and data science
  • Describe and discuss ethical issues in the practice of data science
  • Organize and clean data using R
  • Communicate clearly and persuasively with data, using accurate, unbiased and aesthetically pleasing visualizations
  • Apply data science methods to illuminate and analyze issues of injustice or structural inequality

Curricular Connections
This course satisfies the graduation requirement in Justice, Equality, and Community. It is also an introductory course for the Data Science minor, and satisfies the Mathematical & Quantitative Thought distribution requirement.

There are no prerequisites for this course. In particular, I will assume no previous experience with R, computer programming or statistics.

Course Materials
To maximize access to this class, we will use freely available textbooks, videos, and other resources, with a focus on the following:

Reference Texts
- Supplementary Text (DSB): Cetinkaya-Rundel, Mine. 2021. “Data Science in a Box.” This book is freely available online.

You will use two freely available programs, R and RStudio, in order to complete the assignments for this course. R and RStudio are installed on all Davidson campus computers. They are also freely available to install on your own computer.

Access and Accommodation
The college welcomes requests for accommodations related to disability and will grant those that are determined to be reasonable and maintain the integrity of a program or curriculum. To make such a request or to begin a conversation about a possible request, please contact the Office of Academic Access and Disability Resources, which is located in the Center for Teaching and Learning in the E.H. Little Library: Beth Bleil, Director, , 704-894-2129; or Alysen Beaty, Assistant Director, , 704-894-2939. It is best to submit accommodation requests within the drop/add period; however, requests can be made at any time in the semester. Please keep in mind that accommodations are not retroactive.

Course Organization
Modes of learning in this class (whether assessed directly or indirectly) require a range of skills and abilities. Every student’s success is important to me, and I am happy to work with you to develop strategies for success in this class. For Fall 2021, we will be meeting both in person and remotely, to allow everyone to participate fully in the collaborative environment that is necessary to maximize your learning.

Learning R and using it to illuminate and analyze issues of injustice or structural inequality takes regular and repeated practice. This is not a lecture class, but an active learning environment, relying on support from classmates, the embedded tutor and the instructor, and interspersed with discussions and short demonstrations and presentations.

  • In-Class Activities. Each class day will involve a significant amount of hands-on work with R and data. In order to learn from these activities, you must do the assigned readings and videos before you come to class, and be prepared to ask (and answer) questions before diving into a small group discussion or activity.

  • Labs. Bi-Weekly assignments (due approximately every other Thursday at 5 pm Eastern, for a total of 5 labs) will provide you with regular practice using data science methods in R, and applying these methods to illustrate or advocate for a social justice policy. These assignments will build on the material presented in class, and require you to apply the basic concepts in new ways. All lab work is to be done with a partner live on Zoom. While you may be excited to try something on your own, you should never spend more than 15 minutes working on an assignment by yourself. All code must be the work of you and your partner. You may get help on labs from Data CATS tutors, from the embedded tutor, or from me. You may also search for existing advice on the Internet. You may not ask any other person, whether at Davidson or elsewhere (including the Internet) to help you solve a problem.

  • Quizzes. In general, weeks without labs will have quizzes released on Thursday and due on the following Thursday at 5 pm Eastern, for a total of 5 quizzes. Quizzes cover all course material that has been presented between each quiz, with an emphasis on techniques used in the previous lab. Quizzes are to be completed individually, with no help from anyone, in a limited amount of time. Quizzes are open book, and open notes.

  • Final Group Project. The goal of the final project is for you to apply the data science skills learned in this course to real data to answer a question that helps illuminate and analyze issues of injustice or structural inequality. You will work in teams of three to build a dashboard with graphics and text explanations and interpretations. Intermediate deadlines will entail submitting your research questions, data sources, design sketch, and initial maps and graphs.

Attendance Policy
Missing class will adversely affect your grade in many ways. In addition, the college attendance policy will be enforced: missing more than 25% of class meetings makes you eligible for a failing grade. Please look carefully at the syllabus during the first week of class. Should there be a conflict between any class session or assignment due date and a religious holiday or observance, athletic contest, or another academic or personal commitment please let me know well in advance. Religious observance warrants a legitimately excused absence. If you must miss class for any reason, excused or otherwise, you are responsible for getting notes from a classmate and turning in all work on time. Each student will be granted 2 unexcused absences.

Getting Help
It is normal and expected that all students will need help outside of class with the material in this course. Because a language like R is only learned with practice, an important source of help is additional exercises, in the required textbook or optional online resources provided on the course web page. The following additional resources are also available.

  • AT Sessions. The AT for this class will attend class and help me answer your questions as they arise. There will also be at least two different AT sessions during the week. All students are strongly encouraged to attend an AT session every week.

  • Office Hours. I welcome you to visit me during the hours listed at the beginning of this document. It is a good practice to make an appointment with me even if outside of the listed hours of availability.

  • Data Cats. Data CATS offers free assistance to students working on data-focused assignments and projects. Student consultants are highly qualified to help you debug R code, find datasets, perform analyses, and make visualizations. Data CATS is located in the Hurt Hub.

  • Math and Science Center. The Math & Science Center offers free assistance to students in all areas of math and science, with a focus on the introductory courses. Trained and highly qualified peers hold one-on-one and small-group tutoring sessions on a drop-in basis or by appointment, as well as timely recap sessions ahead of scheduled reviews. Emphasis is placed on thinking critically, understanding concepts, making connections, and communicating effectively, not just getting correct answers. In addition, students can start or join a study group and use the MSC as a group or individual study space. It is located in the Center for Teaching & Learning (CTL) on the first floor of the College Library.

  • Reusing/Sharing Code. Many of the datasets we will discuss and analyze are publicly available, so they may have been extensively discussed and analyzed. Unless explicitly instructed otherwise, you may use available code and resources for course activities (e.g., Github repos, StackOverflow answers) but you must cite the source of the code/resource within your program files and/or document. Recycled code that is discovered that is not properly cited may be considered as plagiarism. When working in groups on class assignments you are welcome to discuss problems together and ask for general advice, but you may not share or use code from another group.

  • Honor Code. Please adhere to the Davidson College Honor Pledge.


Category Points
Attendance 10 Points
Participation 10 Points
Labs 30 Points
Quizzes 30 Points
Final Project 20 Points


A tentative class schedule of topics, readings and due dates is available below. Minor adjustments will be made as needed, on the course web page. Please double check the web page before doing each reading assignment.

Week 1


  • Introduction
  • Downloading R/RStudio
  • Introduction to R and RStudio
  • R Markdown

Date \(~~~~\) Readings

8/24 \(~~~~\) HOPR Appendix A

8/26 \(~~~~\) HOPR Chapters 1-3
\(~~~~~~~~~~~\) R4DS Chapter 4
\(~~~~~~~~~~~\) R4DS Chapter 6
\(~~~~~~~~~~~\) R4DS Chapter 27.1-27.5


  • Download and install R and RStudio

Week 2


  • Getting and Loading Data
  • Dealing with Messy Data

Date \(~~~~\) Readings

8/31 \(~~~~\) HOPR Appendix D
\(~~~~~~~~~~~\) R4DS Chapter 11
\(~~~~~~~~~~~\) Class Notes

9/2 \(~~~~~\) HOPR Chapter 7
\(~~~~~~~~~~~\) R4DS Chapter 12.5
\(~~~~~~~~~~~\) Class Notes

Week 3


  • Data Visualization
  • Understanding Economic Mobility

Date \(~~~~\) Readings

9/7 \(~~~~~~\) R4DS Chapter 3.1-3.4
\(~~~~~~~~~~~\) Data Visualization in R
\(~~~~~~~~~~~\) Class Notes

9/9 \(~~~~~~\) R4DS Chapter 3.5-3.10
\(~~~~~~~~~~~\) LOTF Executive Summary
\(~~~~~~~~~~~\) LOTF Chapter 1
\(~~~~~~~~~~~\) Class Notes


Week 4


  • Data Visualization
  • Transforming Data

Date \(~~~~\) Readings

9/14 \(~~~~\) R4DS Chapter 28
\(~~~~~~~~~~~\) Class Notes

9/16 \(~~~~\) R4DS Chapter 5.1-5.4
\(~~~~~~~~~~~\) Class Notes


Week 5


  • Transforming Data
  • Segregation and Education
  • Exploring Data

Date \(~~~~\) Readings

9/21 \(~~~~\) R4DS Chapter 5.5-5.7
\(~~~~~~~~~~~\) R4DS Chapter 12.3-12.7
\(~~~~~~~~~~~\) LOTF Chapter 3
\(~~~~~~~~~~~\) Class Notes

9/23 \(~~~~\) R4DS Chapter 7.1-7.4
\(~~~~~~~~~~~\) Class Notes


Week 6


  • Exploring Data

Date \(~~~~\) Readings

9/28 \(~~~~\) R4DS Chapter 7.5-7.7

9/23 \(~~~~\) NO CLASS (Fall Break)


  • Homework 2 DUE
  • Quiz 2 Assigned

Week 7


  • Tidy Data
  • Child & Family Stability
  • Relational Data

Date \(~~~~\) Readings

10/5 \(~~~~\) R4DS Chapter 9
\(~~~~~~~~~~~\) R4DS Chapter 10.1-10.5
\(~~~~~~~~~~~\) R4DS Chapter 12.1-12.3
\(~~~~~~~~~~~\) LOTF Chapter 5

10/7 \(~~~~\) R4DS Chapter 12.4-12.7
\(~~~~~~~~~~~\) R4DS Chapter 13.1-13.4


  • Homework 3 Assigned
  • Quiz 2 DUE

Week 8


  • Relational Data
  • Group Workshop I

Date \(~~~~\) Readings

10/12 \(~~\) R4DS Chapter 13.5-13.7
\(~~~~~~~~~~~\) R4DS Chapter 14.1-14.2
\(~~~~~~~~~~~\) R4DS Chapter 15.1-15.2
\(~~~~~~~~~~~\) R4DS Chapter 16.1-16.2

10/14 \(~~\) Workshop Data Sets and Research Questions


  • Homework 3 DUE
  • Quiz 3 Assigned

Week 9


  • Mapping Geospatial Data
  • Segregation

Date \(~~~~\) Readings

10/19 \(~~\) USDR Chapter 1
\(~~~~~~~~~~~\) GCR Chapter 1
\(~~~~~~~~~~~\) LOTF Chapter 2

10/21 \(~~\) USDR Chapter 3.1-3.4
\(~~~~~~~~~~~\) GCR Chapter 8.1-8.2


  • Homework 4 Assigned
  • Quiz 3 DUE

Week 10


  • Mapping Geospatial Data
  • Shiny R Dashboard

Date \(~~~~\) Readings

10/19 \(~~\) USDR Chapter 3.5-3.6
\(~~~~~~~~~~~\) GCR Chapter 8.3-8.6

10/28 \(~~\) MSR Chapter 1-4
\(~~~~~~~~~~~\) MSR Chapter 12


  • Homework 4 DUE
  • Quiz 4 Assigned

Week 11


  • Group Workshop II
  • Shiny R Dashboard

Date \(~~~~\) Readings

11/2 \(~~~~\) Group Workshop II

11/4 \(~~~~\) MSR Chapter 5
\(~~~~~~~~~~~\) MSR Chapter 6


  • Homework 5 Assigned
  • Quiz 4 DUE

Week 12


  • Shiny R Dashboard
  • Data Science and Modeling

Date \(~~~~\) Readings

11/9 \(~~~~\) MSR Chapter 7
\(~~~~~~~~~~~\) MSR Chapter 8

11/11 \(~~\) MSR Chapter 9
\(~~~~~~~~~~~\) MSR Chapter 10


  • Homework 5 DUE
  • Quiz 5 Assigned

Week 13


  • Group Workshop III
  • Topics in Data Science

Date \(~~~~\) Readings

11/16 \(~~\) Group Workshop III

11/18 \(~~\) TBD

Week 14


  • Group Workshop IV
  • Thanksgiving Break

Date \(~~~~\) Readings

11/16 \(~~\) Group Workshop IV

11/18 \(~~\) NO CLASS (Thanksgiving)

Week 15


  • Group Presentations

Date \(~~~~\) Readings

11/16 \(~~\) Group Presentations

11/18 \(~~\) Group Presentations