Data rarely speaks for itself.
Office Hours via Calendly
August 19 – Dec. 9, 2019
By itself, the facts contained in raw data are difficult to understand, and in the absence of beauty and order, it is impossible to understand the truth that the data shows.
In this class, you’ll learn how to use industry-standard graphic and data design techniques to create beautiful, understandable visualizations and uncover truth in data.
In this course, you will (1) evaluate and critique visualization designs, (2) learn visualization techniques & theory, (3) implement interactive data visualizations, and (4) develop a substantial visualization project.
Specifically, you should:
- Understand the principles of data and graphic design
- Evaluate the credibility, ethics, and aesthetics of data visualizations
- Create well-designed data visualizations with appropriate tools
- Share data and graphics in open forums
- Be curious and confident in consuming and producing data
Syllabus Subject to Change
The standards and requirements set forth in this syllabus may be modified by the course instructor. Notice of such changes will be made in advance and by announcement in class.
The course books are optional to purchase as the textbooks have free online versions available. Although, I’d still highly recommend buying physical copies (but it’s completely optional).
- Kieran Healy, Data Visualization: A Practical Introduction (Princeton: Princeton University Press, 2018), http://socviz.co/. [FREE online; $30.62 new at Amazon]
- Claus E. Wilke, Fundamentals of Data Visualization (Sebastopol, California: O’Reilly Media, 2018), https://serialmentor.com/dataviz/. [FREE online; $36.94 new at Amazon]
- Hadley Wickham and Garrett Grolemund, R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (Sebastopol, California: O’Reilly Media, 2017), http://r4ds.had.co.nz/. [FREE online; $18.17 new at Amazon]
We’ll also use a book in development for Shiny.Unfortunately, there is not a good standard textbook for R Shiny yet; this book will likely become that but is still a year away from completion.
- Hadley Wickham, Mastering Shiny (O’Reilly Media, 2020), https://mastering-shiny.org/. Online version
There will also occasionally be additional articles and videos to read and watch. When this happens, links to these other resources will be included on the reading page for that week.
I also highly recommend subscribing to the R Bloggers. This e-mail is sent daily with helpful tutorials about how to do stuff with R.
R and RStudio
You will do all of your visualization work in this class with the open source (and free!) programming language R. You will use RStudio as the main program to access R. Think of R as an engine and RStudio as a car dashboard—R handles all the calculations and the actual statistics, while RStudio provides a nice interface for running R code.
R is free, but it can sometimes be a pain to install and configure. To make life easier, you can (and should!) use the free RStudio.cloud service, which lets you run a full instance of RStudio in your web browser. This means you won’t have to install anything on your computer to get started with R! We will have a shared class workspace in RStudio.cloud that will let you quickly copy templates for labs and problem sets.
RStudio.cloud is convenient, but it can be slow and it is not designed to be able to handle larger datasets, more complicated analysis, or fancier graphics. Over the course of the semester, you’ll probably want to get around to installing R, RStudio, and other R packages on your computer and wean yourself off of RStudio.cloud.
Online help and Slack
Computer programming can be difficult. Computers are stupid and little errors in your code can cause hours of headache (even if you’ve been doing this stuff for years!).
Fortunately there are tons of online resources to help you with this. Two of the most important are StackOverflow (a Q&A site with hundreds of thousands of answers to all sorts of programming questions) and RStudio Community (a forum specifically designed for people using RStudio and the tidyverse (i.e. you)).
Searching for help with R on Google can be tricky because the language is, um, a single letter. Try searching for “rstats” instead. If you use Twitter, post R-related questions and content with #rstats. If you post something related to our class, consider using the hashtag #dsba5122.
Additionally, we have a class chatroom at Slack where anyone in the class can ask questions and anyone can answer. Ask questions about the readings, problem sets, and projects in the class Slack workspace. I will monitor Slack regularly, and you should also all do so as well. You’ll likely have similar questions as your peers, and you’ll likely be able to answer other peoples’ questions too.
Be nice. Be honest. Don’t cheat.
Orderly and productive classroom conduct
I will conduct this class in an atmosphere of mutual respect. I encourage your active participation in class discussions. Each of us may have strongly differing opinions on the various topics of class discussions. The conflict of ideas is encouraged and welcome. The orderly questioning of the ideas of others, including mine, is similarly welcome. However, I will exercise my responsibility to manage the discussions so that ideas and argument can proceed in an orderly fashion. You should expect that if your conduct during class discussions seriously disrupts the atmosphere of mutual respect I expect in this class, you will not be permitted to participate further.
Recording in the classroom
Electronic video and/or audio recording is not permitted during class unless the student obtains permission from the instructor. If permission is granted, any distribution of the recording is prohibited. Students with specific electronic recording accommodations authorized by the Office of Disability Services do not require instructor permission; however, the instructor must be notified of any such accommodation prior to recording. Any distribution of such recordings is prohibited.
Discussion of grades and performance
Such discussion shall occur between the student and the instructor(s). Sharing information regarding grades and performance in places such as discussion forums or email blasts is prohibited.
Code of Student Responsibility
“The purpose of the Code of Student Responsibility (the Code) is to protect the campus community and to maintain an environment conducive to learning. University rules for student conduct are discussed in detail. The procedures followed for any Student, Student Organization or Group charged with a violation of the Code, including the right to a hearing before a Hearing Panel or Administrative Hearing Officer, are fully described.” (Introductory statement from the UNC Charlotte brochure about the Code of Student Responsibility). The entire document may be found at this site: https://legal.uncc.edu/policies/up-406
All students are required to read and abide by the Code of Student Academic Integrity. Violations of the Code of Student Academic Integrity, including plagiarism, will result in disciplinary action as provided in the Code. Students are expected to submit their own work, either as individuals or contributors to a group assignment. Definitions and examples of plagiarism and other violations are set forth in the Code. The Code is available from the Dean of Students Office or online at: https://legal.uncc.edu/policies/up-407.
Faculty may ask students to produce identification at examinations and may require students to demonstrate that graded assignments completed outside of class are their own work.
Assignments and grades
You can find descriptions for all the assignments on the assignments page.There may be extra credit opportunities.
|Attendance & Participation||10%|
|4 Datacamp courses||10%|
|4 Problem sets||20%|
|4 Online Quizzes||20%|
|Group: Design Contest||10%|
|Group: Final Project||30%|
|A (Comendable)||90% – 100%|
|B (Satisfactory)||80% – 89.99%|
|C (Marginal)||70% – 79.99%|
|U (Unsatisfactory)||< 70%|
Let’s have fun and explore!