This syllabus contains some experimental elements that are worth trying. However, experiments are allowed to fail and therefore the syllabus is subject to change based on how the class is learning.
Forms
Homework extension – To continue the philosophy of having your grade reflect what you know, not what you know + what you get done on time. If you did not submit the homework at all, you are allowed one homework extension request, no questions asked. This is on all parts of the homework. When we grant your extension, you will have until the next day at 11:59 pm to do a single submission per Sakai quiz.
Covid Precautions
Per the Duke Requirements, you must wear a mask if you are in-person in class and in office hours.
If you test positive, file a short term illness form.
Group Logistics
You will work in three groups over the course of the semester. Your group is a learning pod where you work together to master the material. The first two groups will be randomly assigned. You will be able to choose your last group. Between each round of groups, everyone will give feedback to each other and reflect on their own performance as a group member.
Course Activities
Modules
The semester is broken up into modules. Each module focuses on a set of topics and has the following pieces:
- Videos – These are videos from the edx.org course sequence Data8 and should be watched before class.
- Homework – To check your understanding of the videos, we will have homework in the form of a quiz on Sakai. This will be due 24+ hours before the first class we discuss the material (usually Tuesday, so it will be due Sunday 11:59 pm). You will have three chances on the quiz and we will take your best score. A score of 80% on a homework will result in full credit.
- In-class group worksheet – On the first in-class day of the module, your groups will go over a worksheet that focuses on the topics everyone scored the lowest on in the homework (the 24+ hours is to give us time to make this). You will be graded on completion.
- Lab – After the group worksheet, the rest of the class time for the module will focus on Jupyter Notebooks that you fill out individually, but you work on together with your group in class.
Projects
There are three projects, just like there will be three groups. Each time you form a group, you will focus on a project. For the first two projects, everyone will work on the same project. These projects focus on spanning across all of the modules during that third of the semester.
The final project will be of your group’s choosing, just like you will be able to choose your own groups. The project will be from data found, cleaned, and analyzed by your group.
Exams
We will have 3 evenly spaced exams with the last exam on the last day of class. This is to lower the stress and stakes of any one exam. Exams will be done individually. We will run a mock Exam 1 in class before the first exam so everyone has some experience in what to expect. See the schedule for these exams.
Grading
The final course grade will be calculated with the below weights.
As of 10/30/21: Exam 1’s weight went from 15 to 12 and Exam 3’s weight went from 15 to 18. This is to reflect the fact that each subsequent exam includes some material from the prior exams due to the cumulative nature of the class material.
Course Activity | Weight |
---|---|
Homework | 10 |
Group worksheets | 3 |
Group feedback | 3 |
Labs | 15 |
Project 1 | 6 |
Project 2 | 8 |
Project 3 | 10 |
Exam 1 | |
Exam 2 | 15 |
Exam 3 |
Regrades
Students wishing to have a grade changed must make a written request through the regrade request feature on Gradescope no later than one week from the day the assignment is returned. Regrade requests should explain specifically why the student believes a different grade is more appropriate, not just ask for more partial credit without any reason. Please note that grade changes (apart from clear grader errors) are rare.
Course Policies
Collaboration
You must adhere to the Duke Community Standard in all the work you do in this class. Please make sure to read the standard carefully.
You are encouraged to collaborate on all group work and all activities for learning the material.
You may not collaborate on your exams.
Late Work and Extensions
Homeworks can be submitted 1 day late. However, Sakai will only allow one submission (assuming you have not already submitted) during the late period. Labs can be submitted late for 2 days, usually Sunday when the next module’s homework is due. We will not take points away for submitting late, your grade should reflect your learning not whether you can get things in on time.
This policy is to provide flexibility in case life gets in the way of your learning. However, keep in mind relying on this late due date is not advisable given the module cadence.
If you need an extension beyond the late period, email Prof. Stephens-Martinez.
Diversity
This course is committed to Duke’s Commitment to Diversity and Inclusion. Moreover, beyond the diversity listed in this statement, we believe this class should be inclusive to students with no prior background in computer science such that they feel the class is a secure and supportive learning environment.
Disabilities
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 their 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.
Acknowledgments
This class is an adaptation of Data8 from the University of California, Berkeley. Therefore, much of the material in this class is being used with permission from Data8 and was created by Ani Adhikari, John DeNero, David Wagner, and a lot of staff at UC Berkeley. This course was also designed with input from Jeff Forbes and Andrea Novicki.