The full syllabus is available on our CourseWeb site, along with other important documents.
Useful information from around the web can be found at a page I use for my Writing for the Public classes, but has relevant information for Writing With Data, as well (e.g., options for website builders, repositories for images/audio/etc. in public domain and under CC license).
Below is the schedule for the course, subject to change (check in regularly!)
To view the lesson plan for the day (and to access possible activities/links/etc. for that day’s lesson), click the date in the schedule to navigate to the lesson plan webpage (e.g., click “Tu, 8/26”). Lesson plans will be live by the beginning of class–though they may sometimes be live a few hours or a day earlier.
Schedule
Tu, 8/27: Introductions
| Intros; background and interests survey; go over syllabus; defining data; rhetoric and data; looking for datasets to write about; going over study; go over Learning Narrative; go over Public Writing Assignment; start Python set-up.
Readings due: None Writing due: None |
Th, 8/29: Finding and Exploring Data
| Comparing kinds of data (activity: class survey vs. journal articles); Explore lists of datasets from Data is Plural (subscribe to Data is Plural newsletter here), Awesome Data, data.gov, KD Nuggets (has other data repositories here), and from Dataquest blog post; choose something to look at for Data Exploration; Introduction to and getting set up with Jupyter Notebook*, Python, Pandas, etc. *credit to Matt Burton and his Data Basics workshop where I adapted a lot of this code from. Other due: 1. Go back to lesson plan on August 27 and follow directions to download Anaconda. It could take as long as an hour to download the software. Readings due: 1. D’Ignazio and Klein, Data Feminism, Chapter 5: “The Numbers Don’t Speak for Themselves” Writing due: Journal 1: on interests to write about this semester: what excites you in your major? When you read the news, blogs, or your social media? Or perhaps something you watch on the internet or TV? Can these interests be channeled into analyses of data? What are some possible ways? Is there anything from D’Ignazio and Klein that you could see as helpful to how you might go about starting up a project? What and why? (300-500 words) |
Tu, 9/3: Exploring Data Toward Writing
| Peer review of journal on data exploration; start work on proposal; Basics of distribution, variance, and measures of central tendency—common pitfalls when writing about these. Continue Python work as needed. Readings due: 1. Miller, Chapter 1 Writing due: Work on Journal 2 (see prompt in writing due for next class on 9/5) |
Th, 9/5: How Do Data Journalists Make Data Interesting?
| What makes for “interesting” writing; what makes for “accessible” writing; work on proposal in class; schedule conferences for next week; range of genres for public writing Readings due: 1. Miller, chapter 13 on applied audiences 2. Choose one of three data journalism pieces from USA Today, FiveThirtyEight, and Buzzfeed News. Read it and be prepared to discuss what intrigued you most as a reader, why, and how moments of quantification were accessible to you. Writing due: Journal 2: Choose a dataset from the range we discussed in class. Play around with it using your Jupyter Notebook code. Use some of the tools to add up, find means and medians, and make quick bar or line charts. Write a Data Exploration: 300-500 words of your experience looking into the dataset, talk about what you found interesting. Explain your process in search and selecting. What are your initial impressions? What questions do you have? |
Tu, 9/10: Conferences *NO CLASS*
| On Tuesday (9/10), Wednesday (9/11), and Thursday (9/12), we will meet one on one to discuss potential directions for your writing this term, any questions about the course, and how I can best help you do the writing you are most interested in doing this term. We will meet for about 10 minutes. Readings due: None Writing due: Public Writing Proposal (300-500 words). Submit by 9/9 at 11:59pm. |
Th, 9/12: Ethically Writing With Data
| -Principles to consider as you start writing about your data. Readings due: 1. Miller, Chapter 2 Writing due: Learning Narrative 1 (see CourseWeb>Course Documents>Assignment Prompts for the prompt for this piece). Due by 11am today. |
Tu, 9/17: Technical Concerns
| -Getting a little more technical -more on design and accessibility -talk about Learning Narrative 1 Readings due: 1. Miller chapter 4 on more technical principles Writing due: None, work on public writing assignment draft |
Th, 9/19: Quantitative Comparisons
| -Making Comparisons -Signaling evaluation Readings due: 1. Miller chapter 5 on quantitative comparisons Writing due: Public Writing Project draft*: Hand in your draft of your public writing assignment. *Bring in two printed in-progress copies to class, submit online to CourseWeb by 11:59pm tonight (September 19). |
Tu, 9/24: Amplification- Style Workshop 1
| Workshop public writing drafts; style and quantitative writing with focus on amplification; in-class reflection activity. Go over Public Writing Revision and Learning Narrative 2 Assignment Reading due: 1. Fahnestock, chapter on amplification from Rhetorical Style (just pages 390-405) Writing due: Journal 3: Pick one of the methods of amplification that Fahnestock describes and go back through the writing you have done so far this term and see if you can find an example of you doing that in your own writing. Now, try to amplify it with even greater force than it is as present and then try to diminish the original with less force than it is at current (include both attempts in the journal submission). You can use the same method of amplification or different one to do this. Use your best judgment, as going “up and down” here can be a little fuzzy. What are your thoughts? What is gained and lost here? What do you like or dislike about these alternative versions? 300-500 words |
Th, 9/26: Examples
| Examples and data Reading due: 1. Miller, chapter 8 on examples Writing due: None, work on public writing revision |
Tu, 10/1: Probability
| Writing about probability; catching up as needed Reading due: 1. “Some ideas on communicating risk to the general public” Writing due: None, work on public writing revision |
Th, 10/3: Writing with Inferential Statistics, Part I
| -Inferential stats and probability, standard error, confidence intervals. -communicating advanced calculations to public and technical/professional audiences. Readings due: 1. Chapters 3 in Miller on causality, statistical significance, and substantive significance Writing due: None, work on public writing revision |
Tu, 10/8: Writing with Inferential Statistics, Part II
| -Statistical significance and rhetorical significance. Explore some example texts that use the term “statistical significance” and how they communicate what that means to the reader -Python workshop on running a t-test. Practice on your dataset (choose another dataset or use other data, as needed). -go over journal 4 Readings due: None Writing due: Journal 4: Calculate the confidence interval and the margin of error on the bear data (choose any column you’d like–csv file is in CourseWeb>Course Documents>Confidence Interval Materials) using the Jupyter Notebook we used today. Explain to someone interested what this means in the best way you can. Try it out and write about your experience trying to do it. (300-500 words) |
Th, 10/10: More on Writing about Advanced Calculations
| Circle back and review elements of writing with inferential stats so far; go over journal 4, 5, and think about patterns of writing about more advanced calculations; in-class time to work on revisions and Learning Narrative 2. -Mid-term survey on how course is going** Readings due: None Writing due: Journal 5: On your own, create two groups of data from the Bear csv file that you worked on for Journal 4. Try to run a t-test for it. What did you get? Choose a way to explain it to a friend who does not know much about statistics. How did you explain the results? Finally, explore what is confusing about t-tests, what don’t you get? What isn’t quite clear? (300-500 words) |
Tu, 10/15: Transitioning Major Projects
| -Survey results -In-class time on projects -start talking about next Sci/Tech Writing project, proposal, and possible advanced calculations Readings due: None Writing due: Work on revision and LN2 |
Th, 10/17: Writing about Correlation
| – What are some considerations of language use when writing about correlation? What are some techniques for constructing an argument around correlation or using it as a supplement to a larger argument? What are some things to avoid or look out for when it comes to rhetorical considerations around correlation? -proposal for next major assignment -in class activity on possible advanced calculations to write about for next assignment -Go over some genres of sci/tech/professional writing more generally to consider for project and for proposal (will go over in more detail on 10/22) Reading due: Review chapter 3 in Miller, especially on causality. Writing due: 1. Revision of Public Writing Assignment 2. Learning Narrative 2 |
Tu, 10/22: Sci/Tech Writing, Rhetoric, Data
| -Technical and scientific genres: who reads them? What do they expect? What are your options? (e.g., grant proposal, technical report, academic/scientific paper, white paper) -Writing for specialized audiences vs. lay audiences -discussion of “rhetorical situations” as we move from writing about the “same” content in a new context -Deciding on what advanced calculations will be relevant to your dataset and argument, and who your argument might be for. -look at example sci/tech/professional writing Reading due: 1. Chapter 11 in Miller on writing scientific reports and papers Writing due: Scientific/Technical Writing Proposal. Submit by end of day your proposal for the second major writing assignment. 300-500 words. |
Th, 10/24: Writing About Methods
| -Writing about methods and “showing your work” strategically and ethically -Being transparent about your data, how you analyze it, why you did things that way, and what limitations there are Reading due: 1. Miller chapter 10 on writing about method Writing due: Work on scientific/technical writing project |
Tu, 10/29: Creating Effective Tables
| Introduction of Data Visualization Unit and Data Visualization assignment due at end of term. Tables and rhetoric Readings due: 1. Miller, chapter 6 Writing due: Work on scientific/technical writing |
Th, 10/31: Creating Effective Charts
| Charts and rhetoric Readings due: Miller, chapter 7 Writing due: Journal 6: Go back to your public writing assignment or the current draft of your scientific/technical piece. Do you have any tables or charts? Where are they located? Why are they located there? What rhetorical choices are embedded in those visualizations? What is effective about them do you think? Use this space to consider the pros and cons of tables and/or charts you currently have. Use chapter 6 and 7 from Miller to help you here. If you do not use tables or charts, use this space to explore what you could do and why/how you’d do it. 300-500 words. |
Tu, 11/5: Data Viz Beyond Bars, Pies, and Lines
| Compare and compose lists of elements important to data visualizations from the mundane to the lively. How can we leverage the tools of rhetoric in an effective and ethical manner in data visualization, especially emotional appeals? Looking at infographics effective or lacking: caffeine, drone strikes, music industry, tech companies. -Also: get set up with Tableau Readings due: 1. D’Ignazio and Klein, Data Feminism, Chapter 2 Writing due: Journal 7: D’Ignazio and Klein argue that most of the efforts of the 20th century to try to remove emotion from data visualization were short-sighted. Do you think there is value in emotion? Explain. Quote/paraphrase/summarize from this chapter as a way to start a conversation with their ideas to test out your own ideas about the rhetorical possibilities and limitations of visualizing data. Finally, use this space to connect what they write about to what you will do for the Data Visualization Assignment. 300-500 words |
Th, 11/7: Tableau Workshop
| Today we will take some time to learn how to use the free version of Tableau. We will also talk together about other programs out there that might be useful (e.g., Excel, PowerPoint, Photoshop, InDesign, Gephi, things you can do with Python or other programming languages). Also will have time in class for peer response. Readings due: None Task and Writing due: Download Tableau (only if you have Windows or Mac OS): -Go to https://public.tableau.com/en-us/s/ -Enter email address and click “download the app” right next to that. -Make sure you have the correct version for Windows or Mac. -Might take about 10 min to download. -Run the application to download the program (will have to restart computer when done, I’m pretty sure. Scientific/Technical Writing draft* *Bring in two printed copies to class, submit online to CourseWeb by 11:59pm tonight (November 7). |
Tu, 11/12: Collections as Data: Part to Whole Workshop on Data Viz, Part 1
| Link to slides/site for today: bit.ly/2PzNYij Today and 11/21, we will have some folks from the Collections as Data: Part to Whole initiative come to our class to work on a project visualizing collections of data associated with Pitt’s library. This will involve some hands-on coding that will be due for homework on 11/18 (11:59pm), some brainstorming for ways to visualize a unique aspect of the sample data, and an attempt at executing that vision. Hopefully this class effort will be some good practice for your data visualization assignment due at the end of the term. Workshop scientific/technical reports; catch up on anything related to data visualizations as needed Reading due: None Writing due: Work on scientific/technical writing revision |
Th, 11/14: Revision Workshop
| Will have time in class to work on revision for 11/24. I have some targeted topics in mind, but will have a good chunk of time set aside for in-class writing. Tyrica from last class will also be by at 12pm to answer any questions about the homework assignment due on 11/18. Readings due: None Writing due: 1. Work on scientific/technical writing revision 2. Work on proposal and hand-coding for 11/18 (11:59pm) (NOTE: this counts as a Journal assignment in final grade) |
Tu, 11/19: Writing Block
| Today we will commit to some planning in our writing for the revision and LN3. Readings due: None Writing due 11/18 and 11/24: 1. Work on proposal and hand-coding for 11/18 (11:59pm) (NOTE: this counts as a Journal assignment in final grade) 2. Work on revision of scientific/technical writing revision (due 11/24 by 11:59pm) 3. Work on Learning Narrative 3 (due by 11/24 11:59pm) |
Th, 11/21: Collections as Data: Part to Whole Workshop on Data Viz, Part 2
| Will finish up the second part of this workshop. Readings due: None Writing due: 1. Work on revision of scientific/technical writing revision Writing due on 11/24 by 11:59pm: 1. Revision of Scientific/Technical Writing 2. Learning Narrative 3 |
Tu, 11/26 and Th, 11/28: Thanksgiving Break (NO CLASS)
| NO CLASS |
Tu, 12/3: Final Assignments
| Will explore how to conduct a reflective rhetorical analysis for one of the twofinal assignments for the course, the Experiential-Learning Document. Will also spend time working on the Visualization project. Reading due: None Writing due: Journal 8: Consider our exploration of rhetoric and data throughout the term and use this space to work on some initial thoughts on the tools you have as a writer to not just “write up” results when it comes to quantitative writing, but how to do so in a way that captures attention, makes clear what the story/argument is, engages a reader’s emotions, etc. and does so in an ethical fashion. What does that all mean to you? What is important to remember as a quantitative writer? Thinking about this stuff now can help you in the final reflection project due at the end of the term. Think of this journal entry as a first step toward that project. 300-500 words. |
Th, 12/5: Final Assignments and Close Out
| A continuation of December 3, we will work on the final projects together and then close out the semester. Reading due: None Writing due: By December 12 at 11:59pm, you will submit: 1. Data Visualization added/revised for Public Writing project or Scientific/Technical writing project. 2. Experiential-Learning Document |
