Updated and effective as of 03-23-20

Course learning objectives

  • Learn to explore, visualize, and analyze data in a reproducible and shareable manner
  • Gain experience in data wrangling and munging, exploratory data analysis, predictive modeling, and data visualization
  • Work on problems and case studies inspired by and based on real-world questions and data
  • Learn to effectively communicate results through written assignments and a final project presentation

Activities and assessments

The following activities and assessments will help you successfully achieve the course learning objectives. By experiencing the course content in different ways, you will not only gain a better understanding of data science, but you will also get experiences that can guide you as you apply what you've learned in future academic and professional projects.


In homework, you will apply what you've learned during lecture and lab to complete data analysis tasks. You may discuss homework assignments with other students; however, homework should be completed and submitted individually. Homework must be typed up using R Markdown and submitted in the appropriate GitHub repository and on Gradescope.

The lowest homework grade will be dropped at the end of the semester.


In labs, you will apply the concepts discussed in lecture to various data analysis scenarios, with a focus on the computation. You will work on lab assignments individually and in teams; all team members are expected to contribute equally to the completion of each assignment. You are expected to use the assigned Git repository on the course's GitHub page as as the central platform for collaboration. Commits to this repository will be used as a metric of each team member's relative contribution for each lab. You will also be asked to evaluate your team members' performance periodically during the semester.

The lowest lab grade will be dropped at the end of the semester.


The exams are an opportunity to assess the knowledge and skills you’ve learned. Both exams will be take-home assignments that you are expected to complete individually. Each exam will include small analysis and computational tasks related to the content discussed in lectures, application exercises, homework assignments, labs, and assigned readings. More details about the content and structure of the exams will be discussed during the semester.

Final project

The purpose of the project is to apply what you’ve learned throughout the semester to analyze an interesting data-based research question. The project will be completed in teams and will be due Wednesday, April 29 at 2:00pm. You must complete all components of the final project to pass the course.

Participation and application exercises

Application exercises give you an opportunity to practice using the statistical concepts and/or code discussed in lecture on short data analyses. Given that students may not be in the same timezone, we are allowing for some flexibility in Application Exercises. These exercises must be attempted within 24 hours of the lecture date in U.S. Eastern Standard Time. For instance, if there is an Application Exercise scheduled for Tuesday, March 24, then it would be "due" by Wednesday, March 25, at 11:59pm U.S. Eastern Standard Time. In general, these assignments are shorter than homework assignments and will be completed individually.


To construct functional and diverse teams, you will be asked to complete a short survey to gauge your previous exposure to programming topics. After completing the survey, you will be assigned to teams of 3-4 students; these teams will stay consistent throughout the semester (barring extraordinary circumstances). You will work in these teams during class, on application exercises, on labs, and on the final project.

Grade calculation

The final grade will be calculated as follows:

Homework 25%
Labs 15%
Exam 1 17.5%
Exam 2 17.5%
Final Project 15%
Participation & Application Exercises 5%
Teamwork 5%

Class attendance in lecture and lab is a firm expectation; frequent absences or tardiness will be considered a legitimate cause for grade reduction. Attendance is no longer required or checked as of March 23, 2020. However, students are still responsible for learning all course material. Pre-recorded lectures will be posted online and made available to students electronically; scheduled class periods will be dedicated to asking questions about the material and working on any assigned application exercises in the presence of the instructor.

If you have a cumulative numerical average of 90 - 100, you are guaranteed at least an A-, 80 - 89 at least a B-, 70 - 79 at least a C-, and so on.


In this course, we will strive to create a learning environment that is welcoming to all students and that is in alignment with Duke’s Commitment to Diversity and Inclusion. If there is any aspect of the class that is not welcoming or accessible to you, please let me know immediately. Additionally, if you are experiencing something outside of class that is affecting your performance in the course, please feel free to talk with me and/or your academic dean.


Duke University is committed to providing equal access to students with documented disabilities. Students with disabilities may contact the Student Disability Access Office (SDAO) to ensure your access to this course and to the program. There you can engage in a confidential conversation about the process for requesting reasonable accommodations both in the classroom and in clinical settings. Students are encouraged to register with the SDAO as soon as they begin the program. Please note that accommodations are not provided retroactively. More information can be found online at access.duke.edu or by contacting SDAO at 919-668-1267, SDAO@duke.edu.

Where to find help

All in-person scheduled meetings and office hours will be switched to Zoom meetings.

  • If you have a question during lecture or lab, feel free to ask it! There are likely other students with the same question, so by asking you will create a learning opportunity for everyone.
  • The teaching team is here to help you be successful in the course. You are encouraged to attend any of the office hours posted on the home page to ask questions as you study the course content and work through assignments. A lot of questions are most effectively answered in-person, so office hours are a valuable resource. Please use them!
  • Outside of class and office hours, any general questions about course content or assignments should be posted on Piazza since there are likely other students with the same questions. The questions you post will be visible to the entire class, so please email the instructor directly with any specific questions about grades or personal matters.

Sometimes you may need help with the class that is beyond what can be provided by the teaching team. In that instance, I encourage you to visit the Academic Resource Center.

The Academic Resource Center (ARC) offers free services to all students during their undergraduate careers at Duke. Services include Learning Consultations, Peer Tutoring and Study Groups, ADHD/LD Coaching, Outreach Workshops, and more. Because learning is a process unique to every individual, they work with each student to discover and develop their own academic strategy for success at Duke. Contact the ARC to schedule an appointment. Undergraduates in any year, studying any discipline can benefit! Contact ARC@duke.edu, 919-684-5917, 211 Academic Advising Center Building, East Campus – behind Marketplace.

Where to find course materials

All assignments and course materials may be found on the course website and GitHub page. There is also an up-to-date course schedule where you can find the lecture notes discussed in each class meeting, assignment deadlines, and reading assignments to help you prepare for each class.

Announcements may also be sent to the class by email, so please check your email regularly.

Make-up Policy

Students who miss a class due to a scheduled varsity trip, religious holiday, or short-term illness should fill out an online NOVAP, Religious Observance Notification, or Incapacitation Form, respectively. These excused absences do not excuse you from assigned homework. It will still be your responsibility to submit relevant assignments in accordance with the deadline.

If you have a personal or family emergency or health condition that affects your ability to participate in class, you should contact your academic dean's office. More information about this procedure may be found on the Personal Emergencies page or provided by your academic dean.

Exam dates cannot be changed and no make-up exams will be given. If you must miss an exam, your absence must be officially excused before the exam due date. If your absence is excused, the missing exam grade will be imputed at the end of the semester based on your performance on other relevant course assignments.

You must complete all components of the final project to pass the course.

Late Work

Homework or lab assignments submitted late but within 24 hours of the deadline may be accepted with a 20% penalty. Homework or lab assignments submitted any later will not be accepted.

Late work will not be accepted for the exams or the final project.

Academic Honesty

By enrolling in this course, you have agreed to abide by and uphold the provisions of the Duke Community Standard as well as the policies specific to this course. Any violations will automatically result in a grade of 0 on the assignment and will be reported to Office of Student Conduct for further action.

  • You may not discuss or otherwise work with others on the exams. Unauthorized collaboration or using unauthorized materials will be considered a violation for all students involved. More details will be given closer to the exam dates.
  • Reusing code: Unless explicitly stated otherwise, you may make use of online resources (e.g. StackOverflow) for coding examples on assignments. If you directly use code from an outside source (or use it as inspiration), you must explicitly cite where you obtained the code. Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism.

On individual assignments, you may not directly share code or write up with other students. On team assignments, you may not directly share code or write up with another team. Unauthorized sharing of the code or write up will be considered a violation for all students involved.

Regrade Requests

Regrade requests should be submitted through the regrade request form on Gradescope. Requests for a regrade must be made within 48 hours of when the assignment is returned. Due to the time consuming nature of regrades, requests submitted later will not be regraded. Requests will only be honored if there is an error in the grade calculation or a correct answer was mistakenly marked as incorrect. Regrade requests made on the basis of grading being too harsh will not be recognized. Please note that by submitting a regrade request, your entire assignment may be regraded and you may potentially lose points. Therefore, you should attend office hours to ask a member of the teaching team about your grading feedback before submitting a regrade request.

No grades will be changed after Wednesday, April 29 at 2:00pm.

Grades can only be changed by the instructor. Teaching Assistants cannot change grades on returned assignments.