Archived Syllabus

Homework

Practice problems (mainly from the book) will be assigned (almost) every week on Monday in class and on Moodle; homework will be due via Moodle before 11:55AM of the following Monday. Homework should be completed in RMarkdown; both the RMarkdown and resulting knitted files should be submitted via Moodle. Homework will be graded, and you will be occasionally be asked to present your solution to a homework problem to the class. Late homeworks will have 25% off if submitted within 48 hours of the deadline and no points thereafter. Everyone has one free 48 hour extension that you may take whenever you need it. Please just let me know when you plan to take it.

Group Project

A significant, culminating activity for this class will be to conduct a statistical investigation on a question of interest to you. These projects will be done in groups of three of your choosing. You will use existing data - publicly and freely available data online, or from faculty research you may be involved in. The project will be broken into multiple component parts due throughout the semester, culminating in a 10 minute oral presenation (with slides) in the last week of class and a final written report due the last day of class. We will spend time in class discussing questions of a reasonable scope for this project, reviewing available data sources, and writing a proposal and resulting report.

Take Home, Individual Exams

There will be three, shorter take home exams throughout the semester. More details about the format and content of the projects will be available closer to the projects. I grant extensions only if your Class Dean is involved at least 24 hours before of the assignment deadline and suggests that flexibility with deadlines would be useful; exceptions to this rule for emergencies (i.e., hospitalizations) will be considered on a case by case basis. There will not be a final exam during finals period; your group project report will be due during that time.

Quizzes

There will be brief (\(\leq\) 20 minutes) quizes on Moodle due every week on Monday before 11:55AM (to be precise). You will be able to repeat the quiz as many times as needed before Monday at 11:55AM. So, effectively, the grade is for completion of the quiz, since you can retake it. The goal of the quizzes is to give timely feedback on your understanding of the previous week’s material before the course moves on to new material. In rare cases, a quiz will also include questions about the assigned readings for the week ahead. Quizzes will be easy if you have been keeping up with the reading and the homework. The quiz contents will be discussed in Thursday’s class. There will be no make-up quizzes, but your lowest quiz score will be dropped. The quizes on Moodle will open Friday at noon (12:00pm) and close Monday before 11:55AM; you have 3 days to find 20 minutes to complete the quiz.

Grading Policy

Course grades will be a weighted average of the grades on your group project, take home inidividual projects, homework, and quizzes. A small amount of your grade for participation includes class attendance, participation in class activities and discussions, as well as professional conduct. Professional conduct expectations include: treating classmates, guest speakers, and instructors with respect; ensuring that all communication is done in a respectful and courteous manner - this includes email.

• 25% of your grade will be determined by a semester-long group project, including progress and success on small assignments throughout the semester, as well as a written paper and a poster presentation given at the end of the semester.

• 25% of your grade will be determined by weekly homeworks practicing implementation of concepts in R.

• 10% of your grade will be determined by a first individual, take home exam on simple linear regression.

• 10% of your grade will be determined by a second individual, take home exam on multiple linear regression.

• 10% of your grade will be determined by a third individual, take home exam on logistic regression.

• 10% of your grade will be determined by in-class projects and reading responses.

• 5% of your grade will be determined by weekly Moodle quizzes

• 5% of your grade will be determined by your attendance and participation. Generally, attend class, ask and answer questions in class and/or on Slack.

The purpose of grades is to provide formative feedback that aids your learning. I keep course grades in the online gradebook on Moodle so you can always check them there. But what matters is learning. I will enthusiastically talk to you about your learning anytime, and I encourage discussions in which we go over the work you have completed. These conversations let me hear about your challenges and questions, and provide important learning opportunities. However, my rule is that we shouldn’t talk (and especially haggle) about the points or letter grade assigned unless I have made a clerical error.

Course Website

Course information, including this syllabus, will be available on the course Moodle site. I will update information posted there regularly with class handouts, homework, project assignments, and announcements.

Computing

We will use the R statistical software package extensively and exclusively. R is open source software that is available for free on Mac, Windows, and Linux operating systems. There is an extensive section of the course Moodle site with R resources, which may be espectially useful for those newer to R. There are two ways to access R for this class:

• Server: The Smith College server, which you are strongly encouraged to use. Enrolled students will get an account for the server shortly after the beginning of the class. 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 (note: you 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). Like any shared resource, there are limits on how much data you can store (only really relevant for projects, not homeworks) and how quickly it processes during periods of high use.

• Desktop: You can also download R and RStudio to your computer and run things locally. Note - homework will be submitted via PDF, which requries additional software TeX. It seems like the best option our purposes is TinyTeX which you can install as you would other R packages – see more info on TinyTeX here.

Note that you do not have to choose one or the other; indeed, you can use both at different points in the class. For instance, you may want to get started using the server for homeworks and then transition to working on your project locally to maximize processing power and speed. Keeping your files on a cloud (e.g., Google Drive, Dropbox) in a folder shared among your group members will facilitate collaboration if you’re all working from your individual desktops.

Student Hours

I am available in my office Mondays 10:30AM-12:30pm, Wednesdays 9:30-11:00AM for students to stop by and discuss anything that relates to the course or would be helpful to talk about in person. These hours are a great opportunity to clarify issues that have come up in class and in homeworks (including R), to discuss research opportunities, your academic interests as they relate to SDS, talk about a news story you read that involved statistical modeling, even just to say hi. Some questions are easier to answer in person than via email, and many - myself included! - feel more comfortable asking questions one-on-one than in front of class. Student hours are a great option for both scenarios. Practically, the hours on Monday are a good opportunity to resolve questions that came up in the previous week’s class before the quiz or on the homeworks that are due most Mondays.

Additional Support on Campus

Attendance

Your attendance in class - including being on time - is crucial to your and your classmates’ success. We are all going to learn this material together, so we need to have everyone present and working. I will make accommodations for an unavoidable absence if you notify me in advance. Our Honor Code means that you will be the judge of whether or not an absence was unavoidable. (For instance, staying in bed because you had the flu would be an unavoidable absence, but oversleeping because you stayed up late to write a paper would be an avoidable absence.) One necessary absence during the semester is not unusual; having more than two is uncommon.

Collaboration

Much of this course will operate on a collaborative basis, and you are expected encouraged and to work together with a partner or in small groups to study, complete homework assignments, and prepare for course projects. However, every word that you write must be your own. Copying and pasting sentences, paragraphs, or blocks of R code from another student is not acceptable and will receive no credit. All students are bound by the Smith College Honor Code.

Written and Oral Communication Skills

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 homework and project assignments in this class will place an emphasis on the clarity of your writing. In addition, there were will be many opportunities throughout the class where you will be asked to present your results from in class exercises, homeworks and the final project to your classmates. Consider this course as a way to develop these crucial communication skills and be quick to seek support from peers, in student hours, and from other resources (see above) as needed to do so.

Email and Course Communications

Student Hours and Slack will be the best way to reach me. I am actively on slack during the work day (9-5pm) and sometimes in the evenings. If you have questions about HW, please post them to the Slack channel for that assignment and answer other peoples’ questions if you know the answer! If you do get in touch via email, I will do my best to respond within 24 hours during the week and 48 hours on the weekend. It will be helpful if you include an informative subject line, including the course number (i.e., “SDS 291 HW 3 Question”).

Timeliness, Deadlines, and Extensions

Deadlines matter in this class, both because: a. these parameters help us keep our focus on the course content rather than logistics and b. because they matter in “the real world”. When assignments are due before 11:55am, Moodle counts submissions at 11:55:01am as late. This policy can be very challenging for people who put off their work until the last minute; I encourage you to start homework assignments early. Late work has 25% off if submitted within the first 48 hours after the deadline. I allow for one “free”, no-penalty 48 hour extension – this applies to homeworks. If you need another extension or a longer extension during the semester, I grant extensions only if your Class Dean is involved well in advance of the assignment deadline and suggests that flexibility with deadlines would be useful.

Academic Honor Code

The Smith College honor code articulates general expectations for students’ conduct throughout the course. While some specific applications of the honor code are noted throughout other sections of the syllabus, these examples are illustrative and not exhaustive. In accordance with the honor code statement below, suspected cases of academic dishonesty will be reported to the Academic Honor Board.

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. 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. All submitted work of any kind must be the original work of the student who must cite all the sources used in its preparation.

Again, every word that you write must be your own. Copying and pasting sentences, paragraphs, or blocks of R code from another student is not acceptable, will receive no credit, and warrants notification of the Academic Honor Board. Similarly, Moodle quizes and take home exams should be completed independently and should not be discussed with other students during the open individual take home exam/quiz window. Per College policy, suspected violations will be reported to Academic Honor Board.

With this in mind, I note that many of the materials used in this course build substantially from previous course instructors’ work, namely Ben Baumer, Amelia McNamara, Miles Ott, as well as contributions and insights on this syllabus from Chad Topaz, Jude Higdon, and Steven Miller. I thank them for their work and willigness to share these materials.

Course Accomodations

If you have documented accomodations with Office of Disability Services, please share the letter from their office via email as soon as possible. This includes if changes to exisiting accomodations or any new accomodations emerge through the semester.

Health and Well-Being

Your well-being matters to me personally and as a course instructor vis-a-vis your success in this course. I encourage you to use your support resources (including those on campus, like your Class Dean, RA, and Health Services) early and often. If you have social, health, emotional or financial challenges that significantly affect your performance in the course, please feel free to email me and set up a time to talk.