DT480:
Seminar-style research project applying Data Science and Analytics to a problem in another discipline. Students will work under the supervision of a Data Science faculty member, with support from a discipline-specific advisor, to produce a deliverable product such as a research manuscript, case study, web page, etc.
Prerequisites
Meeting Times, Location, & Course Delivery Details
This course is delivered in a flexible, project-based format without regular class meetings. Instead, students will work independently or in collaboration with a domain expert on their research projects while adhering to a structured timeline. Key milestones, such as project proposals, progress updates, and final deliverables, will be submitted by specified deadlines to ensure consistent progress. Regular communication with the instructor will be facilitated through optional office hours, one-on-one progress meetings, and online discussion forums for feedback and support. Supplemental materials, including recorded tutorials and resource guides, will be provided to help students acquire the technical and analytical skills needed for their projects. The course culminates in a final presentation, showcasing each student’s work to their peers and relevant stakeholders. This delivery method emphasizes self-directed learning, accountability, and the application of data science to real-world problems.
PLATFORM AND ACCESS: Weekly or biweekly check-ins will be held via Zoom.
Contact Information
This course does not require a specific textbook. However, students are encouraged to utilize the following resources for guidance and support throughout the research process:
- An Introduction to Statistical Learning with Applications in Python by Gareth James (Springer, 2023) (Download Link for Free)
- Python for Data Analysis, 3rd Edition, Wes McKinney, O’reilly.
- Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. 2nd Edition, Aurelien Geron, 2019, O’reilly.
The purpose of this course is to provide students with a hands-on, research-oriented experience in applying data science and analytics to real-world problems across diverse disciplines. By working on a semester-long project under the guidance of a faculty mentor and a domain expert, students will develop the ability to translate complex questions into data-driven solutions. The objective is to empower students to acquire, analyze, and interpret data, while honing skills in communication, collaboration, and ethical decision-making. This course is designed to help students bridge the gap between theoretical knowledge and practical application, preparing them for advanced research or professional roles in data science and related fields.
Tentative Schedule
- Project Proposal (by 01/24/2025) 15%
- Progress Update and report 1 (by 02/14/2025) 10%
- Progress Update and report 2 (by 03/7/2025) 10%
- Progress Update and report 3 (by 03/28/2025) 10%
- Progress Update and draft 1 (by 04/11/2025) 10%
- Progress Update and draft 2 (by 04/25/2025) 10%
- Final draft (by 05/02/2025) 10%
- Final Presentation (05/07/2025) 10%
- Ethical Considerations 15%
Subject to change during the semester. Adequate notice of changes will be given.
This course is designed to mimic the demands of a professional data science research project, requiring significant independent work, collaboration, and time management. Students are expected to dedicate substantial time outside of class to identify research questions, acquire and prepare datasets, perform analyses, and document findings. Regular progress updates and milestone submissions will ensure projects remain on track and align with course objectives. Communication with the instructor and domain experts is vital for refining project goals and resolving challenges. Students are also expected to adhere to ethical standards and maintain thorough documentation of their work. Successful completion of the course will require active engagement, critical thinking, and a commitment to delivering high-quality research outputs by the specified deadlines.
HOMEWORK POLICY:
This course does not include traditional homework assignments. Instead, your focus will be on completing a comprehensive research project, with progress tracked through milestone submissions such as the project proposal, progress reports, documentation, and the final presentation. These milestones serve as checkpoints to ensure you stay on track and receive constructive feedback. While there are no additional assignments, it is essential to manage your time effectively and dedicate consistent effort toward your project to meet deadlines and course expectations.
PROJECTS:
Projects are integral components of this course, providing opportunities to apply data science concepts to real-world scenarios. To ensure a successful and collaborative project experience, the following policies are in place:
- Project Proposal: A project proposal outlining the objectives, scope, methodology, and expected outcomes must be submitted and approved by the instructor before the project commences.
- Progress Updates: Regular progress updates, including milestones achieved and challenges faced, are required. These updates foster transparency, allow for timely feedback, and ensure projects are on track.
- Documentation: Thorough documentation of the project, including data sources, methodologies, and code, is essential. Clear documentation enables understanding, transparency, and reproducibility.
- Final Presentation: Everyone is required to deliver a final presentation showcasing the project's objectives, methodology, findings, and visualizations. This presentation provides an opportunity to communicate results effectively.
- Ethical Considerations: Projects must adhere to ethical standards, respecting privacy, confidentiality, and legal requirements. Any ethical concerns should be addressed promptly.
- Submission Deadline: Projects must be submitted by the specified deadline. Late submissions may be subject to grade deductions.
- A 93-100% A- 90-92% B+ 87-89%
- B 83-86% B- 80-82% C+ 77-79%
- C 73-76% C- 70-72% D+ 67-69%
- D 60-66% F < 60%
Instructor Course Policies
More than three unexcused absences will significantly impact your grade, and excessive absences may lower it naturally. It is essential to communicate in advance about any absence, providing a valid reason and documentation for excused absences.
Upholding academic integrity is paramount in this course, with severe consequences for violations. Plagiarism, cheating, and unauthorized collaboration can lead to failing grades for assignments or exams and referral for judicial review. Additionally, Cell phone use, including texting, is strictly prohibited. Familiarizing yourself with the current Student Handbook is crucial for understanding academic integrity policies, examination procedures, and the attendance policy, especially regarding excused absences, classroom behavior, and the process for handling academic misconduct charges. Adhering to these policies ensures a fair and enriching educational experience for all.
Material To Be Learned
In this course, students will learn how to apply data science methodologies to real-world, interdisciplinary problems through hands-on research projects. They will explore key concepts such as formulating research questions, acquiring and preparing high-quality data, and applying appropriate analytical techniques to address complex challenges. Students will gain practical experience in using programming tools (e.g., Python, R) for data manipulation, visualization, and modeling while adhering to ethical standards in data science. Additionally, they will develop skills in project documentation, reproducibility, and collaboration with domain experts, culminating in a high-quality deliverable such as a research paper, case study, or web-based presentation. This course emphasizes independent learning, critical thinking, and effective communication of data-driven insights.
Learning Outcomes
- Students can evaluate research products in Data Science and Analytics
- Students can formulate high quality, answerable questions about data sets
- Students can choose tools and methods appropriate to a given data set and goal(s)
- Students can identify and obtain high quality data for use in a research product
- Students can produce a high-quality research product in Data Science or Analytics from start to finish in collaboration with a domain expert
- Students can communicate the results of a research study using tools and methods appropriate to their audience and setting
Skills
By the end of this course, students will have developed a diverse set of skills essential for success in data science research and real-world applications. They will gain technical proficiency in data acquisition, cleaning, exploratory data analysis, and modeling using tools such as Python, R, and visualization platforms. Students will learn to critically evaluate datasets, formulate answerable research questions, and apply appropriate analytical techniques. Through collaboration with domain experts, they will integrate data science into interdisciplinary contexts while adhering to ethical standards. Additionally, students will refine their communication skills by presenting their findings clearly to various audiences, using compelling visualizations and storytelling techniques. With a strong emphasis on documentation, reproducibility, and teamwork, this course prepares students to tackle complex, data-driven challenges with professionalism and creativity.
Institutional and Program-Level Policies
All exams will follow the Final Exam Schedule. Students scheduled to take three or more final examinations on one day may request to arrange their examination schedule, so no more than two exams occur on one day.
Requests for early or late exams are considered only under extreme circumstances. Prior to the exam period, the student must file a written request on the Early/Late Exam Form available in the Student One Stop Center, Academic Records, and on the WC portal. The form must be signed by the Instructor and the Academic Dean, approving the alternate exam time. This process must be completed prior to the scheduled exam period.
Undergraduate: SP25 Final Exam Schedule Graduate:
Out-of-class Work Expectation A minimum of 2 hours of out-of-class student work is expected for each hour of in-class time for traditional face-to-face courses. For online and hybrid courses, the combination of face-to-face time and out-of-class work should be equal to 3 hours per credit hour per week. |
Instructional Course Delivery |
Academic Integrity Policy The use of generative AI is prohibited except where expressly allowed in assignment instructions. |
Class Attendance Policy |
Accessibility and Disability Services
Accessibility and Disability Services
Wilmington College provides accommodations and services for student with a variety of disabilities, including chronic illnesses, psychological, physical, medical, learning, and sensory disability amongst others. If you anticipate or experience barriers based on disability and feel you may need a reasonable accommodation to fulfill the essential functions of this course, you are encouraged to contact:
Spencer Izor, Associate Vice President of Compliance - Title IX/ADA Coordinator at spencer.izor@wilmington.edu or 937-481-2365 or Nathan Flack, Academic Resource Manager at 937-481-2208 to learn more about the process and procedures for requesting accommodations, or by visiting College Hall Room 306a or the Robinson Communication Center, Room 103.
Religious Accommodations
Wilmington College strives for an inclusive climate and welcomes students from all backgrounds, faiths, and experiences. If religious observance impedes your ability to participate fully in classroom activities or a principal holiday from your religious tradition occurs during the semester and conflicts with class meetings or activities, please make the professor aware of this immediately to determine if a reasonable accommodation is possible.