Becoming an expert in storytelling with data requires practicing. Indeed,
Learners need to practice, to imitate well, to be highly motivated, and to have the ability to see likenesses between dissimilar things in [domains ranging from creative writing to mathematics] (Gaut 2014).
For practice, students are assigned four individual homeworks, three of which relate to data graphics, and one relates to written communication. Further, students become members of groups that collectively write a proposal to a chief analytics officer, create an interactive communication for a marketing executive, and present the demonstrated value of their work to a chief executive officer. The progression and grade weighting for each exercise follow:
Class discussions are an important component of learning from one another. To facilitate these, we will discuss topics during class. Along with dialogue during class, we will collectively learn through polls and online discussions. All these forms of participation earn you grade points.
Discussions are not meant to be an end, but a beginning, giving students hand-selected, seminal and cutting-edge references to read and study for the concepts discussed. Go down these rabbit holes, following citations and studying the discussed material. Of note: most of the references are available free on an identified website and/or on Columbia University Library, CLIO — use our library 🤓! For references not available, I will upload a pdf/html file.
1 Lectures with discussion
1.1 Course overview: data analyses, visualization, narrative. Jan 18
In our first discussion, we will learn about our collective background and experiences. Then, we will introduce course objectives, structure, deliverables, and tools. Finally, we will consider benefits of storytelling and communication in applied analytics, and briefly introduce workflow and software (e.g., R and Python) for the visualization component of the course.
Before your first class, get yourself familiar with the course website, begin to setup your computing environment. Read (Spencer 2021, secs. 1–1.2.2). Say hello on Ed Discussion.
1.1.1 Supplemental resources
1.2 Data types; coordinates; visual encodings; grammar of graphics. Jan 25
Data visualization is the presentation of data as graphical encodings. In this discussion, we will introduce a purpose for data visuals. Then, we consider various components and ways of thinking about their construction, including use of coordinate systems, data encodings, the grammar of graphics. Once we consider the basics of these components, we will practice identifying and using them while exploring data related to our class example case study.
Read Spencer (2021), Sec. 2-2.1.2.3 to become familiar with visual variables and the grammar of graphics, and Wickham, Navarro, and Lin (2021) Sec. 1.2 and 13 to become familiar with the underlying components of a graphic. If you have not already, start working to understand the code I gave you in homework one (remember, you should be learning from my code, not merely answering questions).
1.2.1 Supplemental resources
1.3 Comparing encoded data; encoding with color; design principles. Feb 1
Continuing our discussion on visual channels for encoding, this session we will focus on three channels of color: hue, chroma (saturation), and luminance. Then, we consider empirical studies on the effectiveness in using encodings and their limitations. Third, we will consider elements of design we can use for our data graphics. Finally, we will practice these concepts.
Read Spencer (2021), § 2.1.3.1 - 2.2.4 for an overview of the material; Heer and Bostock (2010) (find it in Clio) to understand one way we can empirically test the effectiveness of a graphical decoding and comparison and to learn what kinds of encodings have less error in decoding; and Edward R. Tufte (2001b) for understanding how to experiment with graphics to improve our communications.
1.3.1 Supplemental resources
1.4 Exploring to explaining: data-ink; annotations; information hierarchy. Feb 8
So far, we’ve been concerned with understanding tools and language for mapping or encoding data to visual channels of graphics. In this discussion, we’ll begin to shift our focus from exploring data with graphics to explaining those graphics. Specifically, we consider our audience in redesigning exploratory graphics for purposes of communication, discuss how to address complexity of graphics for an audience, and begin to more substantially consider graphics as driving a narrative. We’ll begin with annotating graphics, and creating a hierarchy of information.
Read Spencer (2021), § 1.4.1, 1.4.4-1.4.5, 2.1.4 for an overview of the material; Doumont (2009c) for understanding the fundamentals of all modes of communication; and Lupi (2015) to gain an information designer’s perspective on how information is layered to form a complete narrative.
1.4.1 Supplemental resources
1.5 Elements of writing. Feb 15
Annotated graphics sometimes stand-alone. But graphics can also benefit from, and add value to, written narrative. Here, we will explore elements of writing, work through example memos as a means of communicating an idea for a data analytics project to an analytics executive. As a group exercise, we will revise an analytics proposal writeup for a new audience.
Read Spencer (2021), § 1.1-1.3 for an overview; Sharot (2017a)1 to understand what factors or circumstances change audience’s minds, and consider how her insights relate to Doumont’s advice to begin communications by finding “common ground”; Storr (2020a) to understand how we might follow Doumont’s advice to “get our audience to pay attention…”; Booth et al. (2016) to improve how we structure each sentence and paragraph; Zetlin (2017) to categorically understand our first audience: analytics executives.
1.5.1 Supplemental resources
1.6 Numeracy in narratives; composition and layout. Feb 22
In a multimodal document, tables and data-driven graphics can add context and illustrate the text, and vice versa. Here, we begin to draw from prior discussions in both data graphics and written narrative. We will consider how to apply visual design to a multi-component document containing text and data, and integrate text and visual information.
We will discuss how layout design and typography can work together to integrate visuals with narrative content, and will enable a deeper exploration of data-driven visual design.
Read Spencer (2021), § 1.4 for an overview; J. E. Miller (2007) to consider design of tables; Edward R. Tufte (2001a) for integrating data visuals and tables with narratives; and skim Butterick (2018) to become aware of what typographic choices influence readability (ergo, attention and understanding) for our audiences.
1.6.1 Supplemental resources
1.7 Effective business writing with audience analysis. Mar 1
We will continue discussing the elements of effective business writing for a particular audience and purpose, and learn about the role of revision and getting feedback on your writing.
Read Spencer (2021), § 1.2-1.3 for an overview and review this example draft proposal; Graff, Birkenstein, and Gillen (2021) to learn to place your data communication in the context of previous work and give yours purpose; Doumont (2009b) for another perspective on how to structure business documents.
1.7.1 Supplemental resources
1.8 Communicating context, uncertainty, and variation. Mar 8
We’ve discussed the importance of comparison and context, whether communicating with words, visual encodings of data, or both. We’ll discuss combining graphics together and within a larger narrative to communicate context, uncertainty, and variation.
Read Spencer (2021), § 2.3 for an overview; Nolan and Stoudt (2021c) for describing statistical concepts; explore examples from the R package Kay (2021) for various choices in visually communicating uncertainty; and Lupi (2016) to expand upon the importance of context for data.
1.8.1 Supplemental resources
1.9 Foundations of interactive design. Mar 22
In our discussion, we consider the foundations of modern user interaction in the context of data-driven, visuals and narratives. Such interactions may include scrolling, overview, zoom, filter, details-on-demand, relation, history, extraction; brushing and linking; hovering; clicking; selecting; and gestures. Part of allowing interaction results in authors having less control over the intended narrative and, thus, may think about interaction as giving the audience some level of co-creation in the narrative.
Read Spencer (2021), § 3-3.1 for an overview; explore Hohman et al. (2020) for best practice examples in communicating with interactivity; Tominski and Schumann (2020), § 4-4.1 to understand interactivity.
1.9.1 Supplemental resources
1.10 Technologies and tools of interactive design. Mar 29
Interactivity is inherently technology dependent. We discuss modern approaches to interactivity as related to data-driven, visual graphics and narratives. As such, we will introduce a modern technology stack that includes html, css, svg, and javascript, within which technologies like d3.js (a javascript library) and processing operate. Most award-winning interactive graphics, especially from leading news organizations, use this stack.
But we can enable interactivity through interfaces, too, from R and Python interactive and markdown notebooks and code, to htmlwidgets, Shiny, and plotly. Then, we have drag-and-drop alternatives like Lyra2 or Tableau. While Tableau is functionally much more limited, a use-case for Tableau may be, for example, when a client has a license for it, wants basic interactivity for someone else to setup the visual with interactive options. The range of tools is becoming ever larger. More generally, knowing a range of tools will help us not only build our own interactive graphics, but will also help us work with specialists of these technologies.
From our discussions of the principles and tools of interactivity with data graphics, and ways we can give them organization, we begin considering how to include these tools and concepts to guide our audience in our communications. How should be think about organizing interactive graphics? Would a “dashboard” or “scrollytelling” be useful? Does it depend on our audience and their goals? How should we guide our readers both through the graphics and in their use of interactivity? Consider the ideas and examples from our readings.
Read Spencer (2021), § 3.2-3.3 for an overview; explore Gohel and Skintzos (2021) for extending the grammar of graphics interactively; explore the various types of interactive, visualization tools in the table of tools of our topical resources; review McKenna et al. (2017) for an understanding on factors that can shape the flow of your data-driven stories.
1.10.1 Supplemental resources
1.11 Pacing and animation, interactive docs, story. Apr 5
In this lecture, we’ll continue discussing interactive communication. First, we’ll peek inside the tech of scrollytelling as a form of communication. Then, we’ll consider how animation and pacing can help our audiences follow our messages. Finally, we will dig into approaches to creating an interactive story.
Read Spencer (2021), § 3.3, 4-4.1 for an overview; review Nolan and Stoudt (2021b) for ideas on how to begin composing a data story; watch Chu (2016) for thoughts about how animation and pacing can help our audiences follow our stories.
1.11.1 Supplemental resources
1.12 User-centered, content design; multi-modal presentations. Apr 12
Now, for the last few weeks, we’ve been working hard to learn an entire stack of technologies that, taken together, enable interactivity in our communications. And this builds on all the best practices we’ve already been diligently practicing. I’m certain that the last few weeks has been a challenge to become more proficient with these tools, and that’s made it an accomplishment just to get these things working, much more putting them to use in communications.
In this discussion, we will consider how to help one another through user-centered, content design, and pairwise prototyping. Finally, we will wrap by considering some ideas on how we combine verbal with the data visual: think multi-modal presentations.
Read Spencer (2021) § 4.2 for an overview; skim J. Schwabish (2016) for ideas on preparing, creating, and delivering presentations. Compare similarities and differences in style, audience, and usage of slides and visuals as described in J. Schwabish (2016) (practiced here) with those created and presented in this course, those created and presented by Lisa Charlotte Muth, and those by Nadieh Bremer.
1.13 Project work. Apr 19
1.14 Project work. Apr 26
1.14.1 Supplemental resources
2 Academic Integrity
Columbia University expects its students to act with honesty and propriety at all times and to respect the rights of others. It is fundamental University policy that academic dishonesty in any guise or personal conduct of any sort that disrupts the life of the University or denigrates or endangers members of the University community is unacceptable and will be dealt with severely. It is essential to the academic integrity and vitality of this community that individuals do their own work and properly acknowledge the circumstances, ideas, sources, and assistance upon which that work is based. Academic honesty in class assignments and exams is expected of all students at all times.
SPS holds each member of its community responsible for understanding and abiding by the SPS Academic Integrity and Community Standards. You are required to read these standards within the first few days of class. Ignorance of the School’s policy concerning academic dishonesty shall not be a defense in any disciplinary proceedings.
3 Grading policy
Final grades will be assigned from your overall percentage, calculated as a weighted average of your assignments (individual 40%, group 50%) and participation (10%):
Grade | Precentage |
|---|---|
A+ | [98,100] |
A | [93,98) |
A- | [90,93) |
B+ | [87,90) |
B | [83,87) |
B- | [80,83) |
C+ | [77,80) |
C | [73,77) |
C- | [70,73) |
D | [60,70) |
F | [0,60) |
Late submissions of the homework assignments will be subject to an automatic 10% penalty per day and will not be accepted once the next class discussion begins (as we will typically discuss answers). Late submissions of the final project will not be accepted.
4 Participation
It is important to attend the lectures and read the readings. Each lecture will assume that you have read and are ready to discuss the week’s readings.
Class participation includes both in-class activities (e.g., through polls and group work) and weekly engagement on the class online discussion. To foster a shared learning experience, all enrolled students are required to submit at least one substantive discussion post per week related to the course readings or lecture material. Thoughtful comments typically exhibit one or more of the following:
- Generalizing from examples in discussion or readings
- Devising examples from general principles discussed or in readings
- Critiques of arguments made in the readings
- Analysis of implications or future directions for work discussed in lecture or readings
- Clarification of some point or detail presented in class
- Insightful questions about the readings or answers to other people’s questions
- Links to web resources or examples that pertain to a lecture or reading
5 Assignments
5.1 Individual practice
Find individual assignments in the menu bar at the top.
5.2 Group project
Find group project deliverables in the menu bar at the top.
6 Office hours
Use our office appointment calendars to schedule a one-on-one with Dr. Spencer and include a message that explains what you’d like to discuss. If no available time works for you, then email us with your reason for our meeting and some available times that work for you.