Useful Information

Professor: Kelly McConville

Course Assistant: Grayson White

Office Hours: Check out the office hours schedule!

Course websites:

Learning Outcomes

In this course, you will hone your ability to tell a compelling and accurate story with data. By the end of the course, you will be better able to accomplish the following tasks:

  • Wrangle and interact with a variety of data types, including spatial data, text, factors, dates, relational databases.
  • Create static and interactive data visualizations of multivariate data.
  • Use appropriate exploratory data analysis methods to extract knowledge from data.
  • Effectively write about data for a non-technical audience.
  • Implement coding habits that align with best practices in the field.
  • Utilize a reproducible and collaborative workflow for data analysis.
  • Reason through ethical issues regarding data.
  • Disseminate data through R data packages.


How does Math 241 fit into the statistics curriculum?

Math 241 is an applied statistics course with a heavy emphasis on building data acumen, creating data visualizations, and telling data stories. In contrast, Math 141 is an introductory course that focuses deeply on statistical inference (i.e., hypothesis testing and confidence intervals) and Math 243 largely focuses on statistical modeling. Therefore, while inference and modeling are important tools when extracting knowledge from data, they are not the focus of Math 241. Of course, you are likely to use concepts and tools from Math 141 in your projects.

Learning Tools

RStudio Server

  • We will use R/RStudio to conduct analyses and to reinforce key statistical concepts. R is the statistical software package and RStudio is the user interface for R.
  • The Reed College RStudio server is located at
  • We will use computers fairly extensively. Departmental laptops are available for rental (see me for more information), along with the Lottery Mac program.
  • If you prefer a local version or if the server is down, you can download R and RStudio for free to your personal computer.
  • Work done in any program other than R will not receive credit.
  • During lecture, you should not use your computer for any activity that is not directly related to class.

Course Materials

All of the required readings will come from free, online resources and will be linked to in the Schedule. The resources we will use repeatedly include:

Avenues for Help

It is very important to stay on-top of the material since we will move at a fairly brisk pace. If you feel yourself falling behind, please seek out help.

  • We will use class time to actively engage with the material. Attending class and lab is an important component of the process of learning statistics.
  • Come visit the course assistant or me during office hours. Make sure to check out the office hours schedule to see what times are good for you!
  • Form study groups with other students in the course.

Course Components

Your grade will be based on your performance on the following key components of the course:

  • Homework: Most weeks you will receive a lab assignment.
    • Each assignment is due before the next week’s Thursday class.
    • Don’t wait until the night before a lab is due to work on it! They are designed to encourage consistent application and practice and are not structured to be completed in a single evening.
    • I do not answer Slack messages (or emails) in the evening or the weekend. But feel free to help each other via Slack during these times.
    • No late homework is accepted.
    • The lowest homework grade will be dropped.
  • Projects: We will have two mini projects and one final project.
    • Since data science is a collaborative field, the mini-projects projects will be done in groups. You will have the option of completing the final project solo or in a group.
    • The projects will involve writing blog posts for the website.
      • You have the right to request that your blog post not be included on the course blog or that your name be removed before it is posted.
  • Participation: You are expected to come to class or to watch the videos of the lectures. Participation includes asking and answering questions in-class or on Slack, participating in class discussions and activities, not checking email during class, etc.
    • To receive full participation points, all students must attend office hours at least twice and must contribute at least two content related posts on Slack during the first four weeks. Examples include:
      • Asking a project related question that is content related (“When is X due?” does not count).
      • Answering or at least providing guidance on a lab or project related question
      • Showcasing a neat function you found


If you are a student with a documented disability in need of accommodations, I encourage you to reach out to Reed’s Disability Services Office, or its director, Theresa Lowrie, to make the necessary arrangements. If you already have accommodations, in place, please submit your accommodations to me through the DSS portal, and then come to discuss your accommodation needs with me in person during office hours or by appointment.

Academic Honesty

I encourage you to collaborate on assignments but every piece of work you do must be your own. Copying and pasting other people’s work or code is not acceptable. The Honor Principle must guide your conduct in this class. The following section from the Guidebook to Reed College summarizes the expectations for this class:

Reed College is a community of scholars. The fundamental ethical principle governing scholarship is that one should never claim or represent as one’s own work that which is not one’s own. Proper academic conduct requires that all work submitted for academic purposes – including, but not limited to examinations, laboratory reports, essays, term papers, homework exercises, translations, and creative work—be entirely the work of the person or persons who submit it, and that, in the case of work based upon experiment and observation, the experimental results and observations be reported faithfully. The principle thus requires that no one claim authorship to the work of another and that no one falsify or misrepresent empirical data. This principle should be clear to every scholar, although determining its application in particular circumstances may require careful thought and guidance.