Course Policies
Course objectives
By the end of the semester you will be able to...
- explore, visualize, and analyze data in a reproducible manner
- investigate patterns, model outcomes, and make predictions
- gain experience in data wrangling and munging, exploratory data analysis, predictive modeling, and data visualization
- work on problems and case studies inspired by real-world questions and data
- effectively communicate results
Lecture Structure
For every Tuesday and Thursday lecture, a repository on GitHub will be created for you. This repository will contain an R Markdown file with an outline of the day's topics, code examples, questions, and spaces for you to take notes and work independently. The repository will also contain any necessary datasets.
During lecture, take notes and work on code in this R Markdown file. Course notes are graded based on a good-faith effort towards completion of all parts and contribute to your participation grade. They are due one week following the lecture date.
Computing
We will use the R programming language for data exploration, visualization, and analysis. To interact with R we will use RStudio, an integrated development environment. You have access to RStudio through any web browser at https://vm-manage.oit.duke.edu/containers/rstudio or at the link at the top of this page.
Activities and assessments
The following activities and assessments will help you successfully achieve the course learning objectives.
Homework (25%)
There are five individual homework assignments in this course. You will apply what you have learned during lecture and lab to complete data analysis tasks. You may discuss homework with other students, but assignments must be completed and submitted individually and you must note on your assignment submission who you worked with. The lowest homework grade will be dropped at the end of the semester.
Homework is assigned Thursday and due the following Thursday at 11:59 PM. There is a 24 hour grace period after the due date of homework assignments when they can be submitted with no penalty. Please use this policy as little as possible. After the grace period, there is a 20% penalty for each day the assignment is late.
In order to receive credit, homework must
- be typed up using R Markdown
- correspond to an appropriate GitHub repository
- be submitted as a pdf file to Gradescope
Labs (15%)
There are nine labs. During lab, you will apply lecture concepts to data analysis problems. Most lab assignments will be completed in teams, with all team members expected to contribute equally. You will use your team's repository on the course GitHub page as the central platform for collaboration. Commits to this repository are used as a metric of each team member's contribution to the labs, and you will also be asked to evaluate your team members throughout the semester. The lowest lab grade will be dropped at the end of the semester.
Labs are assigned on Friday and due the following Sunday night at 11:59 PM, but are designed to be completed and submitted during the scheduled lab time. There is a 24 hour grace period after the due date of lab assignments when they can be submitted with no penalty. Again, please use this policy as little as possible. After the grace period, there is a 20% penalty for each day the assignment is late.
In order to receive credit, labs must
- be typed up using R Markdown
- correspond to an appropriate GitHub repository
- be submitted as a pdf file to Gradescope
Exams (40%)
There are two individual, take-home, open-note exams worth 20% each. Each exam will include analysis and computing tasks related to the content covered in lectures, homework assignments, and labs. Details about the content and structure of the exams will be discussed later in the semester.
Exam dates cannot be changed and no make-up exams will be given. If no exam is submitted to Gradescope prior to the deadline, the most recent Github commit submitted prior to the deadline will be graded. If you must miss an exam, the absence must be officially excused by your academic dean prior to the due date. For officially excused absences, the missing exam grade will be imputed based on your performance on the other exams. Late work will not be accepted for the exams.
Final Project (15%)
The purpose of the final project is to use the data science tools you have developed to analyze an interesting data-based research question. The project will be completed with your lab teams, and each team will present their work in writing. More information about the project will be provided later in the semester. Late work will not be accepted for the final project.
Participation and Teamwork (2.5%)
Your participation grade is based primarily on completion of course notes. Notes for each lecture period will be made available in a GitHub repository. Course notes for each lecture will be graded based on a good-faith effort towards completion of all parts and are due one week following the lecture date (lecture notes for a Tuesday lecture are due the following Tuesday at 11:59 PM).
Periodic team feedback and small discussion assignments will also contribute to your participation grade.
Statistical Experience (2.5%)
The statistical experience is an opportunity for you to relate data science principles to your life and society. This assignment is a chance for you to be creative and reflect on your experiences in class. The exact format will be announced later, but may include course discussions, outside readings, or writing short blog posts.
Grade Calculation
The final grade will be calculated as follows:
| Homework | 25% |
| Labs | 15% |
| Exam 1 | 20% |
| Exam 2 | 20% |
| Final Project | 15% |
| Participation | 2.5% |
| Statistical Experiences | 2.5% |
A letter grade will be assigned as follows:
| 93 | ≤ | A | ≤ | 100 |
| 90 | ≤ | A- | < | 93 |
| 87 | ≤ | B+ | < | 90 |
| 83 | ≤ | B | < | 87 |
| 80 | ≤ | B- | < | 83 |
| 77 | ≤ | C+ | < | 80 |
| 73 | ≤ | C | < | 77 |
| 70 | ≤ | C- | < | 73 |
| 67 | ≤ | D+ | < | 70 |
| 63 | ≤ | D | < | 67 |
| 60 | ≤ | D- | < | 63 |
| 0 | ≤ | F | < | 60 |
Regrade requests
Regrade requests should be submitted through the regrade request form on Gradescope. Requests for a regrade must be made within a week of when the assignment is returned; requests submitted later will not be considered. You should only submit a regrade request if there is an error in the grade calculation or a correct answer was mistakenly marked as incorrect. You should not submit a regrade to dispute the number of points deducted for an incorrect response. Please note that by submitting a regrade request, your entire assignment may be regraded and you may potentially lose points.
Due to the time consuming nature of responding to regrade requests, you must attend office hours and ask a member of the teaching team about the feedback before submitting the request. When you submit a request, indicate which member of the teaching team you spoke with. Grades can only be changed by the instructor. Teaching Assistants cannot change grades on returned assignments.
Make-up policy and late work
The late work policy is designed to provide flexibility and also help you stay on top of the course work. If there are extreme extenuating circumstances that prevent you from completing assignments or keeping up with the material, please contact the instructor and/or your academic dean for further discussion.
Students who miss a class due to a scheduled varsity trip, religious holiday, or short-term illness should fill out the appropriate form. These excused absences do not excuse you from work; 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.
Course communication
The course website contains the day-to-day schedule, policies, readings, slides, and assignments. Course announcements will be sent via email through Sakai. Lecture videos and links to the live lecture sessions and office hours are available on Sakai.
Academic Honesty
Academic honesty is of paramount importance in this class, and all work must be done in accordance with the Duke Community Standard, reproduced as follows:
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.
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, be reported to the Office of Student Conduct for further action, and potentially a failing (F) course grade depending on the magnitude of the offense.
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 an honor code violation for all students involved.
Inclusion
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. Please let me know ways to improve the effectiveness of the course for you personally, or for other students or student groups.
Accessibility
Remote learning is a challenge. If any aspect of the course is not accessible to you due to challenges with technology, format, lack of access to quiet study spaces, time zones, etc, please let me know.
If you feel like your performance in class is being impacted by your experiences outside of class, please don't hesitate to come talk with me. If you prefer to speak with someone outside of the course, your academic dean is an excellent resource.
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. Note that accommodations are not provided retroactively.
Where to find help
Office hours
Many questions are most effectively answered in-person, so office hours are a valuable resource. Please make use of them! A list of instructor and TA office hours can be found on the course website. Office hours are accessed through Zoom links available on Sakai.
Piazza
Use Piazza for asking questions and course discussion. Please do not email technical questions to the instructor or TAs. You can post anonymously if desired but note that faculty and TAs can see the poster's identity. Feel free also to answer posted questions and ask follow-up questions.
Other Resources
Academic Resource Center
There are times you may need help with the class that is beyond what can be provided by the teaching team. In those instances, I encourage you to visit the Academic Resource Center (ARC). The 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.
CAPS
Duke Counseling & Psychological Services (CAPS) helps Duke Students enhance strengths and develop abilities to successfully live, grow and learn in their personal and academic lives. CAPS recognizes that we are living in unprecedented times and that the changes, challenges and stressors brought on by the COVID-19 pandemic have impacted everyone, often in ways that are tax our well-being. CAPS offers many services to Duke undergraduate students, including brief individual and group counseling, couples counseling and more. CAPS staff also provides outreach to student groups, particularly programs supportive of at-risk populations, on a wide range of issues impacting them in various aspects of campus life. CAPS provides services to students via Telehealth. To initiate services, you can contact their front desk at (919) 660-1000.
DuWell
DuWell is designed to provide students an understanding of what wellness is and how it applies to their lives. Moments of Mindfulness programs teach practical steps that students can use, in order to facilitate the growth of their personal wellness. Available at (919) 681-8421 or duwell@studentaffairs.duke.edu
WellTrack
WellTrack offers a suite of online tools and courses that help you identify, understand and address issues that you are having. Using the variety of tracking and assessment tools and practicing mindfulness can be essential in maintaining your mental health. Available at https://app.welltrack.com/.
DukeReach
DukeReach provides comprehensive outreach services to identify and support students in managing all aspects of their wellbeing. If you have concerns about a student's behavior or health visit the website above for resources and assistance. Available at http://studentaffairs.duke.edu/dukereach.
Blue Devils Care
Blue Devils Care is a convenient and cost-effective way for Duke students to receive 24/7 mental health support through TalkNow. Available at http://bluedevilscare.duke.edu.