Syllabus

About the Course

Instructors

Description

Data journalism is the practice of telling stories with data. This course will focus on journalistic practices, interviewing data as a source, and interpreting results in context. We will discuss the importance of audience in a journalistic context, and will focus on statistical ideas of variation and bias. The course will include hands-on work with data, using appropriate computational tools such as R, Python, and data APIs. In addition, we will explore the use of visualization and storytelling tools such as Tableau, plot.ly, and D3. No prior experience with programming or journalism is required. {M}{WI}

Important

This course satisfies the communication requirement for the SDS major.

Prerequisites

  • an introductory statistics course (including MTH/SDS 220, SOC 201, GOV 203, ECO 220, PSY 201)

Textbooks

Required

None

Suggested as supplementary references

Evaluation

Time

This is a 4 credit course, meaning that by federal guidelines, it should consume about 12 hours per week of your time. We meet for 3 hours per week. That means you should be spending about 9 hours per week, or nearly 90 minutes per day, on this course outside of class.

Warning

You should be spending about 9 hours per week on this course outside of class.

Grading

  • Homework [30%]: Assignments will alternate between:

    1. Reading responses
    2. Other short assignments

  • News stories [60%]:

    1. One number story [15%]: A short piece during the first quarter of the class.
    2. Investigative piece [25%]: A longer piece during the second portion of the class.
    3. closeread project [20%]: A final piece due at the end of the semester.

  • Engagement [10%]: Active participation in class, engagement with group work, activity on GitHub, helpfulness on Slack, and regular attendance will comprise the remainder of your grade.

  • Extra Credit [?]: Extra credit is applied at the end of the semester when a student is near the boundary of a letter grade. It can be earned in several ways:

    • attending an out-of-class lecture (as will be announced) and writing a short reflection paper about it
    • pointing out a substantial mistake in the book or a homework exercise
    • drawing our attention to an interesting data set or news article, etc.

Extensions

Extensions up to 48 hours will typically be granted when requested at least 48 hours in advance. Longer extensions, or those requested within 48 hours of a deadline will typically not be granted. Please plan accordingly. Please note that because many of the assignments in this class are collaborative, individual extensions for group assignments will be problematic. All extended deadlines will appear on Moodle.

Late assignments will be penalized at the rate of 20% per day, up to a minimum grade of 20% of the assigned value.

Accommodations

Smith is committed to providing support services and reasonable accommodations to all students with disabilities. To request an accommodation, please register with the Accessibility Resource Center (ARC) at the beginning of the semester. To contact ARC, please email .

Policies

Inclusion

Inclusion policy

We are committed to fostering a classroom environment where all students thrive. We are committed to affirming the identities, realities and voices of all students, especially those from historically marginalized or underrepresented backgrounds. We are dedicated to creating a space where everyone in the class is respected, is free from discrimination based on race, ethnicity, sexual orientation, religion, gender identity, disability status, and other identities, and feel welcome and ready to learn at your highest potential.

If you have any concerns or suggestions for how to make this class more inclusive, please reach out to your instructor.

We are here to support your learning and growth as data journalists and people!

Attendance

We expect you attend class in person. When you come to class, we expect your full attention. Please put your phone away during class unless otherwise directed.

If you are unable to attend class for any reason, please follow the materials on the course website and check with another student about what happened in class.

Collaboration

Warning

Much of this course will operate on a collaborative basis, and you are expected and encouraged to work together with a partner or in small groups to study, complete labs, and prepare for exams. However, all work that you submit for credit must be your own. Copying and pasting sentences, paragraphs, or blocks of code from another student or from online sources is not acceptable and will receive no credit. No interaction with anyone but the instructors is allowed on any exams or quizzes.

Academic Honor Code Statement

All students, staff and faculty are bound by the Smith College Honor Code, which Smith has had since 1944.

Smith College expects all students to be honest and committed to the principles of academic and intellectual integrity in their preparation and submission of course work and examinations. Students and faculty at Smith are part of an academic community defined by its commitment to scholarship, which depends on scrupulous and attentive acknowledgement of all sources of information, and honest and respectful use of college resources.

Cases of dishonesty, plagiarism, etc., will be reported to the Academic Honor Board.

Use of Generative AI

This course has a flexible policy towards the use of generative AI tools.

  • Use:
    • In writing code: The use of generative AI is permitted in this course as long as you properly cite the AI-generated content and use it responsibly. Specific assignments may have more restrictive use policies.
    • In writing text: The use of generative AI tools is limited to pre-writing activities (e.g., brainstorming, gathering information, organizing an outline, etc.). AI tools are specifically not permitted for writing entire sentences, paragraphs, drafts, or papers to complete class assignments. (Please see the policy on Abuse below.)
  • Abuse: Attempts to pass of AI-generated content as your own (including but not limited to failure to properly cite generative AI tools) is considered plagiarism and could be a violation of Smith’s Academic Honor Code.
  • Disclosure: If you choose to use generative AI as a learning aid, it is essential to disclose its use on your assignments to maintain academic integrity. If you use generative AI, make sure to add a “Generative AI Disclosure:” callout block at the bottom of your assignment (see below). Your disclosure should state what program you used and how you used it, including links to the specific prompts you used, if possible. Properly citing the AI-generated content allows me to understand your process better and gives credit to the assistance received from these tools.
Sample Generative AI Disclosure Statement

Generative AI Disclosure: This assignment was supported by use of the AI platform ChatGPT. Specifically, I used GPT 3.5 to assist in the title creation (link here), although the final title was modified slightly. I also used ChatGPT to help me plan my outline (link here). I implemented the chatbot’s recommendations.

Warning

Remember that generative AI is not intelligent, doesn’t think, and has no idea what is true or false. You are solely responsible for the veracity of anything (e.g., code or text) you submit.

Code of Conduct

As the instructor and assistants for this course, we are committed to making participation in this course a harassment-free experience for everyone, regardless of level of experience, gender, gender identity and expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, or religion. Examples of unacceptable behavior by participants in this course include the use of sexual language or imagery, derogatory comments or personal attacks, deliberate misgendering or use of “dead” names, trolling, public or private harassment, insults, or other unprofessional conduct.

As the instructor and assistants we have the right and responsibility to point out and stop behavior that is not aligned to this Code of Conduct. Participants who do not follow the Code of Conduct may be reprimanded for such behavior. Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the instructor.

Important

All students, the instructor, the lab instructor, and all assistants are expected to adhere to this Code of Conduct in all settings for this course: lectures, labs, office hours, tutoring hours, and over Slack.

This Code of Conduct is adapted from the Contributor Covenant, version 1.0.0, available here.

Resources

Moodle and course website

The course website and Moodle will be updated regularly with lecture handouts, project information, assignments, and other course resources. Homework and grades will be submitted to Moodle. Please check both regularly.

Computing

The use of the R statistical computing environment with the RStudio interface is thoroughly integrated into the course. You have two options for using RStudio:

  • The server version of Posit Workbench on the web. The advantage of using the server version is that all of your work will be stored in the cloud, where it is automatically saved and backed up. This means that you can access your work from any computer with a web browser (Firefox is recommended) and an Internet connection.
  • A desktop version of RStudio IDE installed on your machine. The downside to this approach is that your work is only stored locally, and you will have to manage your own installation.

Note that you do not have to choose one or the other – you may use both. However, it is important that you understand the distinction so that you can keep track of your work. Both R and RStudio are free and open-source, and are installed on most computer labs on campus.

Unless otherwise noted, you should assume that it will be helpful to bring a laptop to class. It is not required, but since there are no workstations in the classroom, we will need a critical mass (i.e. at least 12) computers in the classroom pretty much everyday.

Communication

  • r fontawesome::fa("slack") Slack is the primary forum for course-related discussions of all kinds. Please do not email us with course-related questions! Instead, post those #questions on Slack. If discretion is absolutely necessary, private message me on Slack.
  • r fontawesome::fa("github") GitHub will host all of the code for projects associated with this course. All repositories are private by default.

Writing

Your ability to communicate results—which may be technical in nature—to your audience—which is likely to be non-technical—is critical to your success as a data analyst. The assignments in this class will place an emphasis on the clarity of your writing.

Writing Enriched Curriculum

This course is part of Smith College’s Writing Enriched Curriculum. As such, the course supports the Writing Plan of the Program in Statistical & Data Sciences.

Please read the SDS Writing Plan for more information.

The Spinelli Center

The Spinelli Center (now in Seelye 207) supports students doing quantitative work across the curriculum. In particular, they employ:

Your fellow students are also an excellent source for explanations, tips, etc.

Tentative Schedule

Please see the Schedule at the glance for more specific information about readings and assignments.