Overview
This course offers a well-rounded exploration of data science, covering both methodologies and practical applications. It emphasizes a hands-on, collaborative approach, where students work in teams to analyze data and derive valuable insights using statistical and machine learning tools, while fostering an intuitive understanding of underlying theories and algorithms. The course also focuses on developing skills in critical thinking, model evaluation, and the communication of data-driven insights. By the end, students will be equipped to apply computational and inferential thinking to solve data science problems.
Class meetings: WeFr 3:05 – 4:20 pm in Biological Sciences 130
Canvas: Instructors and TAs will regularly share important announcements on Canvas. Therefore, it’s crucial to check the Canvas page frequently throughout the term.
Prerequisites:
- Introductory courses in data structures, algorithms, discrete mathematics, and statistics
- Proficiency in Python, R, or Java
Grading
The following weights are used:
- 20% for homework
- 50% for team project
- 30% for exam
Homework
There will be four homework assignments designed to foster critical thinking and also provide a preview of the assessment materials you can expect on exams. Points will be awarded based on correctness. All assignments are due by 11:59 pm ET on the due date. Late submissions will not be accepted, and no extensions will be accommodated.
Project
For team project, you will apply data science techniques to uncover insights that enhance understanding and decision-making, and craft a compelling narrative to communicate these insights in an engaging and understandable way. The focus of your project should be on extracting meaningful interpretations from data, rather than developing complex algorithms or deploying advanced tools.
Projects are completed in groups of 3-5 students, and teams are formed by the students themselves.
Throughout the semester, there will be four deliverables:
- Proposal
- Milestone Report + Lightning Presentation
- Team Project Presentation
- Final Report + Reproducible Code
In the week leading up to each deliverable (excluding the final report and reproducible code), you will need to meet with the TAs to discuss your progress. All team members should attend these meetings, as attendance and participation will be factored into your team project grade.
All four deliverables should be submitted via Gradescope. For each deliverable, submit as a group: one submission per project group and tag all team members.
Collaboration and Ethics
Homework assignments are to be completed individually. While you are encouraged to discuss the assignments with your peers, the final submission must be written up by you alone. Please acknowledge any collaborators and sources you consulted in your submission, adhering to standard academic practices. Team projects are collaborative efforts, and submissions should reflect the collective understanding of all team members. As with the homework assignments, be sure to acknowledge any external collaborators and sources you consulted in your team project submission.
For both individual assignments and team projects, all code and written responses must be original. Please write the code yourself and be able to explain or reproduce all results you submit. If you have any questions about collaboration, the instructor is available to provide clarification.
Exam
During the semester, there will be one scheduled exam. This exam will be both written and in-person. You are required to complete the exam on your own, without collaboration or the use of external resources. No makeup exams will be accommodated.
Regrade Policy
Any questions about submission grading must be directed to the TAs within 72 hours of receiving feedback. Regrading requests for cases where we misapplied the rubric in an individual instance are more likely to succeed compared to regrades that argue about relative point values within the rubric, as the rubric is applied uniformly to the entire class.
Optional Textbooks
- An Introduction to Statistical Learning with Applications in Python. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor. Springer, 2023.
- Deep Learning. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. MIT Press, 2016.
Academic Integrity
Upon enrollment, each student commits to maintaining the high academic standards of Duke University. Academic misconduct refers to behavior that adversely affects the institution’s integrity. This encompasses activities such as cheating, fabrication, plagiarism, unauthorized collaboration, and assisting others in these actions. Engaging in misconduct may lead to disciplinary measures, including but not limited to failure on the assignment/exam/course, written reprimand, disciplinary probation, suspension, or expulsion.
