About the Course



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,, and D3. No prior experience with programming or journalism is required. {M}{WI}


This course satisfies the communication requirement for the SDS major.



Additional prerequisite for this iteration of SDS 236: SDS 192

  • an introductory statistics course (including MTH/SDS 220, SOC 201, GOV 203, ECO 220, PSY 201)
  • SDS 192




Suggested as supplementary references



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.


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


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

    1. Data diary entries
    2. Reading responses
    3. Other shorts 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. Final 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 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.

Technology in the Classroom

We will use the R statistical software package extensively and exclusively. R and RStudio are open source software that are available for free on Mac, Windows, and Linux operating systems. There are two ways to access R for this class:

Recommended for laptop users

RStudio Desktop: You can download R and RStudio to your computer and run things locally. The program is open source and free to install. You will need to download both R and RStudio for class. Bring a laptop to every class and come ready to install R and RStudio as part of your first lab assignment.

Recommended for Chromebook and tablet users

RStudio Server: Enrolled students will get an account on the Smith College RStudio Server shortly after enrolling in the class. The server is located at (

The advantages of using the Server version are that it is cloud-based: your work will be automatically saved and backed up, and you can work from any computer that has an Internet connection and a web browser (note: you may need to be on a Smith College IP address to access the server, to access it off-campus, you may need to establish a VPN connection to Smith). The disadvantages of using the Server are that like any shared resource, there are limits on how much data you can store and how quickly it processes during periods of high use. You also will have greater ability to customize your local RStudio installation.

If you do not have a laptop, please see Smith’s Laptop Loan program


Smith is committed to providing support services and reasonable accommodations to all students with disabilities. To request an accommodation, please register with the Disability Services Office at the beginning of the semester. To do so, call (413) 585-2071 to arrange an appointment with the Office of Disability Services.



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!


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.

In keeping with Smith’s core identity and mission as an in-person, residential college, SDS affirms College policy (as per the Provost and Dean of the College) that students will attend class in person. SDS courses will not provide options for remote attendance. Students who have been determined to require a remote attendance accommodation by the Office of Disability Services will be the only exceptions to this policy. As with any other kind of ADA accommodations, please notify your instructor during the first week of classes to discuss how we can meet your accommodations.

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.



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.

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.


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.


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.


  • GitHub
  • Slack is the primary mechanism for course-related discussions of all kinds. Please do not email me with course-related questions! Instead, post these on #questions on Slack. If discretion is absolutely necessary, private message me on Slack.


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.