Policies
When in doubt about anything at all, ask questions!!!
Prerequisites
Students are expected to know all topics covered in the MIDS summer course review and boot camp. These include basic probability and statistical inference, including random variables, probability distributions, central limit theorem, hypothesis testing, confidence intervals, linear regression with one predictor, and exploratory data analysis methods. Students are also expected to be familiar with R/RStudio and are encouraged to have learned LaTeX or a Markdown language by the end of the course. MIDS students automatically satisfy these requirements. If you are not a MIDS student, email the instructor to ascertain that you have taken courses that cover these topics.
Meeting times
Meeting times are designed to be as interactive as possible. My role as instructor is to introduce you new tools and techniques, but it is up to you to take them and make use of them. The statistical techniques we will cover are best learned by practical data analysis, so you will be working on various datasets as much as possible, through a variety of tasks and activities throughout each class. Ask as many questions as possible during and outside classes; there are no stupid questions.
Team Work
Note that this course, as is the case with most core courses within the MIDS program, emphasizes the ability to work in teams so that students can learn team productivity and performance. Each student must therefore be ready to contribute to their team's success. MIDS students will work in the same teams they have been assigned to for the fall semester. If you are not a MIDS student, you will be assigned to a group with other non-MIDS students, and by enrolling in this course, you are agreeing to being held to the same standard as MIDS students. Consequently, you are expected to be fully committed to team excellence, performance, and productivity.
Graded Work
Graded work for the course will consist of participation quizzes, data analysis assignments, team projects, and a final project. Regrade requests for data analysis assignments and team projects must be done via Gradescope AT MOST 24 hours after grades are released! Regrade requests for the final project must be done via Gradescope AT MOST 12 hours after grades are released!
There are no make-ups for any of the graded work except for medical or familial emergencies or for reasons approved by the instructor BEFORE the due date. Contact the instructor in advance of relevant due dates to discuss possible alternatives.
Grades may be curved at the end of the semester. Cumulative averages of 90% - 100% are guaranteed at least an A-, 80% - 89% at least a B-, and 70% - 79% at least a C-, however the exact ranges for letter grades will be determined at the end of the course.
There is no final exam. Students' final grades will be determined as follows:
| Component | Percentage |
|---|---|
| Data Analysis Assignments | 30% |
| Final Project | 25% |
| Team Project 1 | 17.5% |
| Team Project 2 | 17.5% |
| Participation | 10% |
Descriptions of graded work
Data Analysis Assignments
Data analysis assignments will be posted on the course website. The assignments include questions that ask students to apply the statistical modeling skills discussed during the semester, as well as questions on the computational aspects of the methods. Students must turn in these assignments on the due date.
You are encouraged to talk to each other about general concepts, or to the instructor/TAs. However, the write-ups, solutions, and code MUST be entirely your own work. The assignments must be typed up using R Markdown, LaTeX or another word processor, and submitted on Gradescope under "Assignments". Note that you will not be able to make online submissions after the due date, so be sure to submit before or by the Gradescope-specified deadline.
Solutions to the assignments will be curated from student solutions with proper attribution. Every week the TAs will select one or two representative solutions for the assigned problems with each solution being attributed to the student who wrote it. If you would like to OPT OUT of having your solutions used for as a representative solution, let the Instructor and TAs know in advance.
Finally, students may be asked to work in pairs for one or two of the data analysis assignments when possible. When that is the case, each pair need only submit one solution per assignment.
Final Project
For the final project, you will apply the knowledge and skills learned throughout this course to analyze a dataset that interests you, subject to the instructor's approval. The project should be an in-depth statistical analysis of a question that interests you. It is quite common for this final project to be based on your research interests, or topics/questions from one of your other courses. Just about every discipline has questions that are amenable to statistical analyses, including economics, engineering, environmental studies, history, the natural sciences, psychology, and even sports, so there are many options to choose from. The data should comprise several variables amenable to statistical analyses via modeling. Students can bring in their own research data sets, or they can ask the instructor for assistance with identifying appropriate data. You will be expected to present the results of your analysis. Detailed instructions will be made available later.
Team Projects
For the team projects, students will work in teams to analyze data selected by the instructor. Each team will be expected to write a report with their data analysis findings. Students may also be given the opportunity to present their results in class. Detailed instructions will be made available later.
Participation
Each student will be assigned a participation grade based on their level of participation throughout the semester. Participation will be assessed based on performance on PlayPosit and in-class quizzes, engagement during live meeting sessions and breakout rooms, and generally how each students engages with other students on Piazza, especially regarding feedback on the project presentations.
Late Submission Policy
You (or your team when applicable) will lose 50% of the total points on each data analysis assignment, each team project, and the final project, if you submit within the first 24 hours after it is due. You will lose 100% of the total points if you submit later than that.
Academic integrity:
Duke University is a community dedicated to scholarship, leadership, and service and to the principles of honesty, fairness, respect, and accountability. Citizens of this community commit to reflect upon and uphold these principles in all academic and nonacademic endeavors, and to protect and promote a culture of integrity. To uphold the Duke Community Standard:
- I will not lie, cheat, or steal in my academic endeavors;
- I will conduct myself honorably in all my endeavors; and
- I will act if the Standard is compromised.
Cheating or plagiarism on any graded assessments, lying about an illness or absence and other forms of academic dishonesty are a breach of trust with classmates and faculty, violate the Duke Community Standard, and will not be tolerated. Such incidences will result in a 0 grade for all parties involved. Additionally, there may be penalties to your final class grade along with being reported to the Office of Student Conduct. Review the academic dishonesty policies here.
Diversity & Inclusiveness:
This course is designed so that students from all backgrounds and perspectives all feel welcome both in and out of class. Please feel free to talk to me (in person or via email) if you do not feel well-served by any aspect of this class, or if some aspect of class is not welcoming or accessible to you. My goal is for you to succeed in this course, therefore, let me know immediately if you feel you are struggling with any part of the course more than you know how to manage. Doing so will not affect your grades, but it will allow me to provide the resources to help you succeed in the course.
Disability Statement
Students with disabilities who believe that they may need accommodations in the class are encouraged to contact the Student Disabilities Access Office at 919-668-1267 or disabilities@aas.duke.edu as soon as possible to better ensure that such accommodations are implemented in a timely fashion.
Other Information
It can be a lot more pleasant oftentimes to get one-on-one answers and help. Make use of the teaching team's office hours, we're here to help! Do not hesitate to talk to me during office hours or by appointment to discuss a problem set or any aspect of the course. Questions related to course assignments and honesty policy should be directed to me. When the teaching team has announcements for you we will send an email to your Duke email address. Be sure to check your email daily.
If you have any concerns, issues or challenges, let the instructor know as soon as possible. Also, all students are strongly encouraged to rely on Ed Discussion, for interacting among yourself and asking other students questions. You can also ask the instructor or the TAs questions on there and we will try to respond as soon as possible. If you experience any technical issues with joining or using Ed Discussion, let the instructor know.
Professionalism
Try as much as possible to refrain from texting or using your computer for anything other than coursework while watching the lecture videos or while in class. Again, the more engaged you are, the quicker you will be able to get through the materials. You are responsible for everything covered in the lecture videos, lecture notes/slides, and in the assigned readings.