Useful Information


Course Assistants:

Office Hours: Check out the office hours schedule!

Course websites:

Learning Outcomes

In this course, we will learn how to “think with data” by stepping through the data analysis workflow. While most of our time will be spent learning techniques related to the Exploration and Visualization step and the Modeling and Inference step, we will also practice the other important pieces of data analysis. Furthermore, since computation is an integral part of modern statistical analyses, we will learn how to work with data using the statistical programming language R and the user interface, RStudio.

By the end of the course, you will have improved your ability to “think statistically”. More concretely, you will be better able to accomplish the following tasks, which have been broken down by steps of the data analysis workflow:

Question formulation:

  • Translate a research problem into a set of questions that can be answered with data.
  • Formulate data questions as measurable statements about parameters in a model.

Data acquisition:

  • Determine the necessary data to conduct analyses.
  • Reflect on how design structures impact potential conclusions.
  • Identify potential ethical concerns surrounding data collection and data privacy.

Data wrangling:

  • Articulate what makes a tidy dataset and identify whether or not a dataset is tidy.
  • Apply basis data wrangling verbs.

Exploration and Visualization:

  • Construct graphics.
  • Describe what graphics do and do not reveal.
  • Compute summary statistics.
  • Recognize good and bad graphing and design techniques.
  • Display, summarize, and interpret complex, multivariate relationships.

Modeling and Inference:

  • Understand and be able to explain key probabilistic and inferential concepts, such as, sampling, variability, random variables, distributions, confidence, and significance.
  • Determine the correct model for a given problem and set of data.
  • Appropriately apply and draw inferences from a statistical model.
  • Conduct model diagnostics.
  • Consider the ethical implications of various modeling practices.

Communicating Findings:

  • Develop a reproducible workflow using R Markdown documents.
  • Interpret and communicate results of statistical analyses effectively for both a statistical and non-statistical audience.
  • Reflect on the variables involved and show a curiosity for other ways of examining and thinking about the data.

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 and lab sessions, you should not use your computer for any activity that is not directly related to class.


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 us and the course assistants during office hours. Make sure to check out the office hours schedule to see what times are good for you!
  • Form study groups with your lab members or other students in the course.

Course Components

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

  • Homework: Each week you will receive R Markdown lab assignments.
    • You will receive your assignments during lab and they are due before your next lab meeting.
    • Don’t wait until the night before the labs are due to start them! They are designed to encourage consistent application and practice and are not structured to be completed in a single evening.
    • No late homework is accepted.
    • The lowest homework grade will be dropped.
  • Exams: We will have a mid-term exam and a final exam.
    • Both exams will include a take-home component and an oral component.
    • The mid-term exam will take place during Oct 14th - 16th.
    • The final exam will take place during Dec 10th - 15th.
  • Quizzes: We will periodically have short quizzes. These will be based on the material covered in recent class periods.
  • Project: You will complete a group data analysis project. The components of the project include:
    • Project Assignment 1: Wrangle and visualize the data
    • Project Assignment 2: Construct a data biography
    • Project Assignment 3: Model the data
    • Final Project Assignment: A 2-3 page paper and 10 minute video summarizing the project findings
  • Participation:
    • You are expected to attend class and participate in class activities. If you are unable to attend class, you should watch the recording before the next class period.
    • You are expected to attend and participate in your lab session.
    • In our class Slack workspace, you must submit at least 1 content related post each week. These posts could be questions about the material, answers to questions, and/or links to useful resources.
    • Within the first 4 weeks of class, you must go to at least 2 office hours.


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 make an appointment to discuss your accommodation needs with one of us.

Course Climate

We expect everyone in this class to strive to foster a learning environment that is equitable, inclusive, and welcoming. If you experience any barriers to learning, please come to us or a college administrator with your concerns.

Code of Conduct

We expect all members of Math 141 to make participation a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.

We expect everyone to act and interact in ways that contribute to an open, welcoming diverse, inclusive, and healthy community of learners. Examples of unacceptable behavior include: using sexualized language or imagery, making insulting or derogatory comments, harassing someone publicly or privately, or other unprofessional conduct.

Instead you can contribute to a positive learning environment by demonstrating empathy and kindness, being respectful of differing viewpoints and experiences, and giving and gracefully accepting constructive feedback.

This Code of Conduct is adapted from the Contributor Covenant, version 2.0.

Academic Honesty

We 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.