English 146DS (W 2023) – Schedule

Schedule for English 146 (W 2023)

Data Stories: Theory and Practice of Data-driven Narratives in the Digital Age

Manicule All readings are from online sources. (See A Note About Access to Reading Materials For This Course and also Guide to Downloading and Organizing Online Readings)
Manicule Some “Class Notes” pages are protected by a password (available on the course Canvas site)


Class 1 (Jan. 12, 2023) [rescheduled from Jan. 10] — Introduction to Course


Please view the following 5-minute video before the first class:

Class 2 (Jan. 17, 2023) — Taking a First Look at Data Stories



The below are not readings so much as “browsings.” Quickly explore the following sampler of data stories to get a sense of varieties of such stories and how they work. When you are done, choose one that you think is the “best” in the set for narrating data, and be prepared to say why in class.

Note: in regard to stories from The New York Times: UCSB Library provides students with free subscriptions to the Times (see info).

Data journalism stories

  1. Jin Wu, et al., “How the Virus Got Out,” New York Times, March 22, 2020. (If you do not subscribe to The New York Times, you will be prompted to create a free online account to access this article.)
  2. Nate Cohn, et al., “Four Ways to Measure Coronavirus Outbreaks in U.S. Metro Areas” New York Times, March 27, 2020
  3. Laure Leatherby, “Why Are Coronavirus Cases Decreasing? Experts Say Restrictions Are Working,” New York Times, Aug. 24, 2020. (If you do not subscribe to The New York Times, you will be prompted to create a free online account to access this article.)
  4. Thomas Curwen, “How Will We Grieve Once the Coronavirus Pandemic Is Over?” Los Angeles Times, April 22, 2020; updated April 28, 2020) (original headline: “How do we craft the narratives that will define a pandemic? Look to the numbers”)
  5. Matthew C. Klein, “How Americans Die,” Bloomberg.com, April 17, 2014.
  6. “A Nation Divided,” Zeit Online, Oct. 29-Nov. 19, 2014. (Click on “Einverstanden,” which means “I agree,” to read the article for free but with the newspaper’s normal advertising and tracking. [Translation of what you are agreeing to.])
  7. Denise Lu, “There Are 2,373 Squirrels in Central Park. I Know Because I Helped Count Them,” New York Times, Jan. 8, 2020.
  8. Volodymyr Agafonkin, “Visualizing Air Raid Sirens in Ukraine,” Observable, 2022.

Data stories created in Tableau Public and ArcGIS Online
1-px transparent spacer[Info on UCSB access to ArcGIS online]

  1. Tableau Help Content, “Earthquakes: Are they on the rise?” (2015)
    • [Optional: see Tableau’s help page on how to auhthor a “story” on the Tableau Public platform]
  2. Mark Jackson, “China’s Urbanization: An exploration of the new story feature in #Tableau 8.2” (2014) [click on the screenshot image in the post to access the interactive data story]
  3. Tatiana Hlinka, Chris Serrao, and Bani Meghadri, “Spoiled Oceans: Student Reality of Microplastics and Food Waste in Our Ecosystems, Communities, and School Lunch Table” (2021)

Storymaps created in ArcGIS Online and StoryMapJS

  1. NOAA/NOS, Old Dominion University, and ESRI, “Coastal Flooding” (2021)
  2. Knight Lab.,“StoryMapJS” (2021) [see the example storymap on “U.S. Westward Expansion”]

Infographics created with Piktochart
1-px transparent spacer[Includes free plan for education]

  1. Examples from Piktochart’s “Inspire Me” gallery page: ( a | b | c )
Manicule Optional: For a wider variety of data story formats (which you can return to for ideas in future classes), see this table of data stories classified by the kinds of techniques. Click around to explore. The table is from Charles D. Stolper, et al., “Emerging and Recurring Data-Driven Storytelling Techniques: Analysis of a Curated Collection of Recent Stories” (2016). (Reading the article is not required.)

1. What’s a (Good) Story?

Class 3 (Jan. 19, 2023) — The Idea of Narrative


Class 4 (Jan. 24, 2023) — Narrative Discourse & Structure


Related Materials (optional)

Students who wish to learn more about the theory and analysis of narrative may be interested in the field of “narratology.” See the following online resource for a guide:

Solo assignment icon Due on This Date: Solo Assignment 1 — Narrative Analysis
10% of final grade

For this class, reread chapter 1 (“Wanting In”) of Paul Tough’s The Inequality Machine: How College Divides Us (2021) via the free preview (“Look Inside”) on the Amazon site. Then, considering the chapter as a “narrative,” conduct a narrative analysis of it using this Narrative Analysis Template. Submit your competed analysis as a PDF through this course’s Canvas site here. Grading Rubric

Class 5 (Jan. 26, 2023) —

Narrative Discourse & Structure


  • [Continued]

2. What’s (Good) Data?

Class 6 (Jan. 31, 2023) — The Idea of Data


Class 7 (Feb. 2, 2023) — Data Models & Structures


In-class activity icon
In-Class Activity

Form project teams of 3-4 members each. (Students will be added as members to a Google “shared drive” assigned for each team that will serve as a common workspace for team activities.)

Class 8 (Feb. 7, 2023) — From Data to Big Data


Class 9 (Feb. 9, 2023) — Data Science Process


Solo assignment icon Due by time of class on this date: Solo Assignment 2 — Conceptual Spreadsheets
10% of final grade

Using a spreadsheet program (Excel or Google Spreadsheets), prepare two spreadsheets of data according to the instructions below. Export your spreadsheets as PDFs and submit them on this course’s Canvas site here. (See examples of “easy” and “hard” spreadsheets.) Grading Rubric

  • Conceptually easy data spreadsheet: Using any books, music tracks, videos, films, or similar items with familiar data values (e.g., author, genre, date, etc.) that are easily available to you, make a very small spreadsheet about those items (e.g., covering just 5 to 10 items). Each row in your spreadsheet will be the data record of one item. Columns (with labels you create at the top) will be for the kinds of data values you are recording about your items (e.g., author name, genre, length, publisher, date, gender of author, etc.). Make a decision about the purpose of the spreadsheet—i.e., what kind of pattern or meaning you might want it to allow you to discover. On the basis of that purpose, choose what kinds of data values you want your columns to record (create 4 to 10 columns with labels for such values). For example, if your items are films, do you want to record the gender of the director, or language of the film, and why?
    Finally, write into an empty cell in your spreadsheet a brief explanation of the purpose of your spreadsheet, and include any thoughts you have about your choice of data values or examples This writing should be the equivalent of about 1-2 paragraphs, or about 200-300 words. (Use word-wrap and/or merge-cells to make all the text visible in the exported PDF of the spreadsheet.)
  • Conceptually difficult data spreadsheet: Follow the same instructions as above to create a data spreadsheet for a set of items that do not have obvious, standard, or familiar data values (though they may have values assigned by scholarly specialists). For example, consider traditional American quilting patterns, traditional African masks, or human feelings, which do not have the typical kind of data values that libraries or playlists use (“author,” “publisher,” etc.). What are the important data values you can think of to record about these items, and why?
    The “why” is the purpose of the spreadsheet, which in an empty cell you should explain, including any thoughts you have on your choice of data values or examples, epistemological or ethical issues you encountered, etc. (about 1-2 paragraphs, or 200-300 words).

Class 10 (Feb. 14, 2023) — Exploring & Assessing Datasets (Part 1)


In advance of this class, explore the sources for public datasets listed below. In this class and the next, teams will draw on these sources to choose a dataset as the basis for their data-narrative project.

Sources of public datasets that might be the basis for a student team’s data-narrative project:

  1. Kaggle (public datasets) [about Kaggle (Wikipedia])
  2. Curated Datasets (from the Observable platform for collaborative data analysis, visualization, and communication based on using “data notebooks”)
  3. Datasets listed by Melanie Walsh for book and course at Cornell on “Introduction to Cultural Analytics & Python” (2020)
  4. Our World in Data — “Research and data to make progress against the world’s largest problems.” (This resource is organized as a collection of “articles” on topics related to important world problems. When viewing an article, scroll to the section at the bottom on “Data Sources” for the dataset sources. In the case of broken or obsolete links, try using a Web search engine to find the current location of a dataset source, or try finding an archived copy of the original site in the Internet Archive.)
  5. MEAD (Magazine of Early American Datasets)
    Representative examples:

    1. Philadelphia Migrant Landing Reports 1798-1801 Dataset
    2. York County Probate Records 1700-1800 (what people owned in Early America)
  6. U.S. Census Data (Census Bureau)
    Suggested Data “Profiles” (In each section of a “profile,” click on a table, labeled in a format like “Table: DP05,” to see the underlying data and download it in CSV format):

    1. United States Profile
    2. Los Angeles County Profile
  7. National Archives Datasets (U.S. National Archives, Open Government Initiative)

    1. Amending America: Proposed Amendments to the United States Constitution, 1787 to 2014
    2. National Historical Publications and Records Commission (NHPRC) Grants, 1965-Present
    3. Social Media at the National Archives (what people view or engage with among the National Archives’ social media posts and blogs; Excel data download)
  8. Pew Research Center Datasets (datasets from the Pew Research Center) (Data downloads are in SPSS .sav format and require SPSS to work directly with the data or to export to Excel or other formats. See UCSB student access to SPSS. However, Tableau Public will open .sav files for visualization.)
    Representative example:

    1. American News Pathways June 2020 Survey (“Americans Who Mainly Get Their News on Social Media Are Less Engaged, Less Knowledgeable”) (Download dataset)
  9. World Bank Open Data
    Representative examples:

    1. Gender Statistics
    2. Education Statistics
  10. World Health Organization Datasets (Only some datasests are downloadable)

    1. Maternal, Newborn, Child and Adolescent Helath and Ageing (download by clicking on “Export” to Excel icon)
  11. United Nations Statistics Division, “Other UNSD Databases” (CSV download of all data; data for particular countries and issues will need to be extracted manually into a separate spreadsheet for analysis and visualization)
  12. HUD Exchange (US Department of Housing and Urban Development)

    1. 2015 Estimates of Homelessness in the U.S.
  13. Raj Chetty, et al., “Mobility Report Cards: The Role of Colleges in Intergenerational Mobility (Revised Version)” (2017) (data)

Other sources and search portals for datasets

  1. Google Dataset Search (search results include a variety of open and for-pay dataset sources)
  2. Humanities Data (“Humanitiesdata.com seeks to help collect and disseminate information about publicly available data of particular interest to digital humanities and humanities computing”) (tagged collection of links to datasets compiled by Matthew J. Lavin)
  3. Data Is Plural — Structured Archive (list of datasets compiled by Jeremy Singer-Vine)
  4. Wikidata (“Wikidata is a free and open knowledge base that can be read and edited by both humans and machines. Wikidata acts as central storage for the structured data of its Wikimedia sister projects including Wikipedia, Wikivoyage, Wiktionary, Wikisource, and others.”)
    1. Read the Wikidata “Introduction”
    2. Get example datasets (visualizations and downloadable data) through the Wikidata Query Service. (The “Examples” of queries can also teach you how to build your own queries.)
In-class activity icon
In-Class Activity

Teams discuss the above public dataset sources and choose a short list of two best sources, and three specific datasets from those sources, that they might want to base their data-narrative project on. The best datasets for the purpose will have four properties:

  • The dataset(s) are fully public, meaning that they can be freely downloaded and then used, adapted, or shared unencumbered by intellectual-property restrictions;
  • The dataset(s) are on a topic the team is interested in or think is important for a scholarly, social, cultural, or other reason;
  • The dataset(s) have the potential to make for a good data narrative (e.g., there is a surprise or contradiction in the data that allows a data narrative to be told in a logical form like the following, where the “but” is the equivalent of a narrative agon or tension: “The data suggests A, but it also turns out that the data shows B”);
  • The datasets(s) seems “good” in the combined senses of having sufficient data quality, documentation, and ethics.

(On data quality, see the readings for Class 11. For a quick preview, see this table. It may also be helpful to look ahead to the prompt for Solo Assignment 3 due in Class 13, which asks students to describe and critique their team’s final choice of a dataset.)

Best practice is for each team to start in its Google shared drive a collaboratively created document titled, for example, “Scouting Datasets” to take notes and record outcomes.

Class 11 (Feb. 16, 2023) — Exploring & Assessing Datasets (Part 2)


Data Quality

Data Ethics

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In-Class Activity

Continuing from the previous class, teams settle on a single public dataset (or combination of a small number of datasets) that they will use for their data-narrative project. (Teams are free to excerpt only parts of datasets and to adapt, restructure, or add to them, so long as appropriate credit is given to the original datasets.)

Best practice is for each team to create in its Google shared drive a new folder titled “Datasets,” and in that folder to create a document for notes and planning titled “Dataset Work Log.” Also, that folder can be the place to save any downloads from datasets (e.g., downloaded CSV files or spreadsheets, downloaded visualizations, etc.).

Team Coordination Weekly Planning Reports

Beginning at the end of this week in the course, each individual team member must fill out a brief Team Coordination Weekly Planning Report by the Friday of each week. Download this template for your report about what you understand to be your own duties/role on the team project in the next week (and the duties/role of your team members too). Upload the report as a file in Canvas here for this assignment. Be sure to save the file on your computer for future use. Then, during each subsequent week, append a new report based on the template to your file (but do not delete previous reports), and upload the new file in Canvas as a resubmission for this assignment.

The purpose of the Team Coordination Weekly Planning Report is to help you with planning and recording your role on your team, and it will give the instructors a “behind the scenes” look at your process. It will also bring a sustained level of accountability to the team. (For example, if there is an issue with a team member not doing their work, or if miscommunication is happening at any stage of the process, the instructors want to know sooner rather than later!). The Team Coordination Weekly Planning Report is a confidential communication from each student to the instructors. 

Although there is no grade attached to these weekly planning reports, they are required each week (begnning in week 6). Also, please feel free to reach out directly over email or otherwise to the instructors about planning or other issues.

Reminder: The duties/roles you record on this sheet should be for the upcoming week, not the past week.

3. Making Data Stories

Class 12 (Feb. 21, 2023) — Telling (and Showing) Data Stories


Manicule For examples of many kinds of data stories, see the readings for Class 2 and also the links in  this table of data stories classified by the kinds of techniques they use. The table is from Charles D. Stolper, et al., “Emerging and Recurring Data-Driven Storytelling Techniques: Analysis of a Curated Collection of Recent Stories” (2016).

Class 13 (Feb. 23, 2023) — Showing (and Telling) Data Stories


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In-Class Activity

Frameworking: Teams begin filling out a Framework Planning Document that prepares for making a data-narrative project. (Download this Framework Planning Document template  with detailed instructions and copy it to your team workspace.) The document asks teams to imagine their audience; the purpose of their intended data narrative (and what is at stake); their key data; their primary media and form or genre; and their main distribution channel. It then asks for a “long sentence” about the dataset that is like a free-writing exercise from which a compelling data narrative can eventually be structured.

If the worksheet cannot be finished during class, teams are expected to collaborate on finishing it outside class.

Credits: This activity combines, adapts, and adds to Cole Nussbaumer Knaflic’s suggestion of a “Big Idea worksheet”; Alberto Cairo’s suggestion of a “long sentence”; and the Framework Institutes’ suggestions for framing policy recommendations.
Solo assignment icon Due on by time of class on this date: Solo Assignment 3 — Datasheet Report for Your Dataset
10% of final grade

Each member of a team individually creates a “datasheet” for their team’s chosen dataset. Use this Datasheet for Dataset Template. as a model.  (Choose only one dataset to report on if the team is working with a combination of more than one.) For the rationale and examples of datasheets, see Timnit Gebru et al., “Datasheets for Datasets” (2019). Grading Rubric

This is a solo writing assignment. Of course, teams will have already discussed their dataset together. But each team member must write a report individually without borrowing directly from anyone else’s writing. It is fine, however, to draw on collective team discussion that has already occurred so long as there is a clear footnote or endnote crediting the team (e.g., “This idea comes from our team discussion,” or, “I borrow with variation an idea that came up in our team discussion”).

Submit this datasheet report as a PDF file through the course Canvas site here.

Class 14 (Feb. 28, 2023) — Showing & Telling Data Stories: Story Maps


In-class activity icon
In-Class Activity

Storyboarding: Using their Framework Planning Document as a starting point, teams begin storyboarding their data narrative. (See Wikipedia article on the idea and applications of storyboarding.)

Suggested practice:

  1. Create in your team Google shared drive a Google Slides document called “Storyboard.” You can start with this storyboard template if you wish (download and copy to your team’s shared drive).
  2. Using or adapting your “long sentence” from the Framework Planning Document, create  slides with short, headline-like titles for each logical unit of the sentence. For example, imagine that your long sentence begins as follows: “During 1970-1990, X percent of national wealth was owned by people defined as belonging to the middle class, while Y percent belonged to the lower class, and Z percent was the property of the ‘rich,’; but beginning around 2000 the percentages began to rebalance significantly.” The statements about X, Y, and Z, and also the important “but” statement in the long sentence could all be separate slides with their own title.
  3. The next step is to try to shape the “story (fabula)” of the long sentence about your data into a “narrative (syuzhet)” with a beginning, middle, and end that follows a narrative arc of rising action, point of agon or tension, and falling action resulting in closure. The above example of the beginning of a long sentence follows a chronology that naturally seems to pivot around a “but” that is a point of narrative agon or tension. However, if chronology or any other “as found” order of your data does not make a good narrative, then there are many other ways of shaping the narrative along an arc of rising action, crisis or tension, and falling action. For example, each of the following ways of organizing the presentation of data creates the equivalent of a narrative arc:
    • Problem indicated by the data Arrow right Suggested Action based on a specific part of the data Arrow right Outcome
    • Background sketched by the data Arrow right Opportunity identified by a part of the data Arrow right Proposal
    • Desired Outcome (i.e., “lead with the goal and not the problem”) Arrow right Suggested Action Arrow right Data on the problem
  4. Create in a separate slide a drawing of a narrative arc, and use text boxes to label its main parts (see example in the Google Slides template). Then create icon representations of your slides and arrange them along the arc.
    Credits: This exercise is indebted to Cole Nussbaumer Knaflic’s ideas about storyboarding in her Storytelling with Data: Let’s Practice!, and also to Alberto Cairo’s suggestion of a “long sentence.”

Class 15 (Mar. 2, 2023) –Showing & Telling Data Stories: Timelines


Visualization (and Visual Storytelling) Tools: Future in-class and project-making activities will involve creating data visualizations. Spreadsheet programs such as Excel or Google Sheets are capable of generating many visualizations from data. But teams may also want to experiment with, or at least be introduced to, one or more of the following tools (create free accounts as needed):

  • ArcGIS StoryMaps (available to students with their UCSBnetID through campus Library subscription to ArcGIS Online [login here; create stories here after login]) — “ArcGIS StoryMaps let you combine authoritative maps with text, images, and multimedia content, and make it easy to harness the power of maps and geography to tell your story. StoryMaps can be used for a wide variety of purposes; for advocacy and outreach, virtual tours, travelogues, delivering public information, and many more” (from Bern Szukalski and Allen Carroll, “The Myriad Uses of StoryMaps”).  See examples of ArcGIS StoryMap stories that mix text, multimedia, data, and maps. For a tutorial, see “Getting Started With ArcGIS StoryMaps.”
  • Tableau Public — “Tableau Public is a free platform to publicly share and explore data visualizations online. Anyone can create visualizations using either Tableau Desktop Professional Edition or the free Public Edition.” (from “About”). Visualizations in the form of data “dashboards” and “stories” can be created online or on a desktop app. Visualizations are public;, and uploaded datasets for visualizations are by default also public. See Tableau Public FAQ & Terms of Service. (To get started with Tableau Public, see this “Step By Step Guide to Learn Tableau Public” with an example Excel sheet as example. See also this short video: Introduction to Tableau Public. Video icon  See also gallery of examples.)
  • Flourish — Uploaded datasets and created visualizations are public if using a free account. (Help & Examples) (Tutorial videos by Alberto Cairo — e.g., the first three videos in the series:  1  |  2  |  3 )
  • Canva — Design platform and tools designed for collaborative work; includes design templates and free images and graphics. Can be used with free or educational accounts.
  • Google Data Studio — Datasets and visualizations are private unless shared. (Help & Examples) (Tutorial course videos from Google Analytics Academy)
  • “StoryMapJS”  — “StoryMapJS is a free tool to help you tell stories on the web that highlight the locations of a series of events.” See the example storymap on the tool’s home page of “U.S. Westward Expansion.”
  • Piktohart — “Piktochart is the visual content maker for businesses looking to improve their internal and external communication. Easily create infographics, reports, presentations, and prints using one online platform.” Includes free education plan accounts. Examples of infographics from Piktochart’s “Inspire Me” gallery page: ( a | b | c )
  • Observable — Collaborative data analysis, visualization, and communication platform deisgned to allow team members to work together on datasets using “data notebooks.” Free plan available. (Appropriate for users with more technical skills, such as familiarity with data notebooks such as Jupyter notebooks and with SQL for querying databases. There are some affordances for non-technical users to build on the platform using examples and pre-built materials.)
  • Note: Specialized statistics programs such as SPSS or programming languages such as R may also be used to create visualizations if students are familiar with them. However, this course does not assume that students have such familiarity.
In-class activity icon
In-Class Activity
  • Teams continue storyboarding their data narrative , now concentrating on the following tasks:
    • Streamlining and structuring the Data, Telling, and Showing components;
    • Detailing key components among the above.
  • Teams begin experimenting with using visualization tools to create data visualizations for the “Showing” components on their storyboard.

Class 16 (Mar. 7, 2023) — Showing & Telling Data Stories: Data Art


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In-Class Activity

Continued teamwork, now pivoting from storyboarding to creating the final data narrative project.

Class 17 (Mar. 9, 2023)

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In-Class Activity

Continued teamwork on data narrative project.

Class 18 (Mar. 14, 2023)

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In-Class Activity

Continued teamwork on data narrative project.

Class 19 (Mar. 16, 2023) — Team Presentations of Data Narratives

Team assignment icon Due on This Date: Presentations of Team Data Narrative Projects

(Submit the projects on Canvas by end of following Monday)

40% of final grade

The data narrative that is the team project for this course can be relatively short (because of the limited time to work in UCSB’s quarter system). It should tell/show its data in a way that answers a question, makes a recommendation, or in some other way comes to a point (or concluding, further question)–where the telling/showing of that point is compelling because it follows some of the principles of good narrative.

The main goal is to demonstrate in compact form an understanding of the basic paradigm of an effective data story: using good data to make a good story that gets people to care about information.

Content: The data narrative project should include both text and data visualizations (and other visual elements as appropriate). It should move through at least 5 logical or narrative “scenes” (where a “scene” is loosely defined to mean an identifiably separate unit of telling/showing). There should also be an “About” statement for readers, and an “Information for the Instructors” statement (see details in Grading Rubric)

Staging Location: During design and development, data narratives can be created in a team’s Google shared drive or on the platform of a visualization service such as ArcGIS StoryMaps, Tableau Public, etc.

Final Location: By default, a project’s final location will be, on the team’s Google shared drive with permissions set to publicly viewable or on an online data visualization, mapping, or similar service. Students are also free to post stories on their own blog or other websites (e.g., a WordPress.com or Reclaim Hosting site).

Intellectual Property: Projects must be careful to respect the copyright constraints and conditions of any materials they use and make publicly viewable. In regard to the copyright status of the projects themselves: a team’s data narratives should by default be put online with a declaration that it is published under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license. However, teams are free to decide on a different option. For example, they can choose alternative Creative Commons options or declare traditional, restrictive copyright in the name of an individual or individuals. (By default, the “Student Work” page on the course site will include links to team projects, though students may request otherwise.)

See Grading Rubric for a detailed view of how the instructors will be evaluating the projects.

Presentations on the team project occur in the course’s last class.

Submit this assignment by no later than the end of Monday March 20th on the course Canvas site here in a form appropriate for the nature of your project (e.g., as a document containing a URL, a PDF of the project, etc.). Remember to provide instructors with the permissions needed to view and comment on any online materials that require sharing permissions (“commenter” permissions in Google Drive, for example).

Only one member of a team needs to submit this assignment (which is a “group assignment” in Canvas).

(Due by Mar. 21, 2023) — Final Assignment

Solo assignment icon Due March 21 (by 11:59 pm): Solo Assignment 4 — Essay About Project
20% of final grade

Each member of a team individually writes a three-page essay (approximately 900 words) that reflects critically on their team’s data narrative project. “Critically” means that the essay should identify both the strengths and problems of the specific data narrative, and possibly also those of data narratives in general.

The essay can begin with, or include, a description of the student’s team project and its essential message. But it must go beyond that to think critically about what works well and what doesn’t in the data narrative or in data narratives generally.

Conclude the essay with a paragraph offering a utopian vision of what the ideal version of the team data narrative would add if you had all the time and resources you needed.

Address the essay to a hypothetical general audience and not just “insiders” to our class who already know all the necessary context or information about your project. Include notes that cite any sources, borrowings, or quotations.

This is a solo writing assignment. Of course, teams will have already discussed their data narrative project together. But each team member must write an essay individually without borrowing directly from anyone else’s writing. It is fine, however, to draw on collective team discussion that has already occurred so long as there is a clear footnote or endnote crediting the team (e.g., “This idea comes from our team discussion,” or, “I borrow with variation an idea that came up in our team discussion”).

Grading Rubric

Submit the essay as a Word or PDF file through the course Canvas site here.

Solo assignment icon Additional Solo Grade for Participation in Team Project and in Class Discussion
10% of final grade

The instructors will assign an additional 10% of the final grade based on their assessment of a student’s participation throughout the course in their team project (as witnessed in visible contributions to the final project or background contributions in a team’s shared drive) as well as in class discussion. Any student who participates equally in the team project and also speaks up during class discussion should be able to earn the full 10% of this grade.

A Note About Access to Reading Materials For This Course

All readings are online. Paywalled articles can be accessed over the UCSB network (or from off-campus by using the campus Pulse VPN service or the campus Library Proxy Server. You can also try to find open-access versions of paywalled materials using the Unpaywall extension for the Chrome or Firefox browsers. (Advice: It is a good idea to download materials as early as possible in case, for example, PDFs that are currently available open-access, on the open net, or through a UCSB Library digital database subscription later become inaccessible.)

Because so many readings are online (an increasingly prevalent trend in college courses), students will need to develop a method or workflow for themselves that optimizes their ability to study the materials. While everyone has their own personal preferences and technical constraints, the following guide includes suggested options for handling online materials:

Guide to Downloading and Managing Online Readings