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 an in-person, project-based format with regularly scheduled class meetings. Students will work independently or in collaboration with a domain expert on their research projects while adhering to a structured timeline. Key milestones—including project proposals, progress updates, and final deliverables—will be submitted by specified deadlines to ensure consistent progress. Class sessions will be used for guided work time, progress discussions, and targeted instruction. Regular communication with the instructor will occur through office hours, scheduled one-on-one progress meetings, and in-class feedback sessions. Supplemental materials, including tutorials and resource guides, will be provided to support the development of the technical and analytical skills required for the projects. The course culminates in a final presentation, during which students will showcase their work to peers and relevant stakeholders. This delivery method emphasizes active engagement, accountability, and the application of data science to real-world problems.
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.
Project Technical Requirements and Analytical Expectations
Student projects are expected to demonstrate a comprehensive and methodical data science workflow. At a minimum, each project must incorporate at least two distinct datasets, which should be appropriately joined or integrated using sound data engineering practices. Students are required to perform thorough data cleaning and wrangling, including handling missing values, inconsistencies, and outliers, and clearly document all preprocessing steps. Projects must include exploratory data analysis (EDA) to identify patterns, relationships, and potential issues within the data, supported by appropriate summary statistics and visualizations.
From a modeling perspective, students must begin with a clearly defined baseline model and then develop multiple alternative models using suitable algorithms. Cross-validation (CV) techniques should be applied for model tuning and performance optimization. A systematic comparison of models is required, using appropriate evaluation metrics and justified selection criteria. Effective data visualizations should be used throughout the project to communicate insights, model performance, and results clearly and professionally.
Tentative Schedule and Evaluation Breakdown
| Component | Due Date | Weight |
|---|---|---|
| Project Proposal (problem definition, data sources, feasibility) | by 01/20/2026 | 10% |
| Proposal Presentation | 01/22/2026 | 5% |
| Progress Update and Report 1 (data acquisition, initial joins, cleaning plan) | by 02/03/2026 | 5% |
| Progress 1 Presentation | 02/05/2026 | 5% |
| Progress Update and Report 2 (data wrangling, EDA, preliminary insights) | by 02/17/2026 | 5% |
| Progress 2 Presentation | 02/19/2026 | 5% |
| Project Draft 1 (EDA results, baseline model, documentation) | by 03/05/2026 | 5% |
| Progress Update and Report 3 (multiple models, feature engineering, CV setup) | by 03/17/2026 | 5% |
| Progress 3 Presentation | 03/19/2026 | 5% |
| Project Draft 2 (model tuning, comparison, visualizations) | by 04/02/2026 | 5% |
| Progress Update and Report 4 (final model selection, validation results) | by 04/14/2026 | 5% |
| Progress 4 Presentation | 04/16/2026 | 5% |
| Final Draft (complete analysis, interpretation, reproducibility) | by 04/30/2026 | 12.5% |
| Final Presentation | 05/05/2026 (1:00–3:00 PM) | 12.5% |
| Ethical Considerations (data use, privacy, bias, transparency) | Ongoing | 10% |
This schedule is subject to change during the semester. Adequate notice will be provided for any adjustments.
Presentation Expectations
Students will complete five presentations during the semester: four progress presentations aligned with major project milestones and one final presentation at the conclusion of the course. Each progress presentation must be prepared for a minimum of 10 minutes and should clearly address technical progress, including data integration, cleaning, exploratory analysis, modeling decisions, challenges encountered, and next steps.
The final presentation must be prepared for at least 20 minutes and should provide a comprehensive overview of the completed project, including datasets used, modeling approach, cross-validation and tuning strategy, model comparison, results, visualizations, and conclusions. Students are expected to follow the presentation schedule closely, as timely progress updates are critical for meaningful feedback and successful project outcomes. Failure to present as scheduled may impact evaluation unless prior approval is obtained.
This course is designed to mirror the demands of a professional data science research project and requires a high level of independent work, collaboration, and effective time management. Students are expected to dedicate substantial time outside of class to defining research questions, acquiring and preparing datasets, conducting analyses, and documenting results. Regular progress updates, project draft submissions, and in-class progress presentations are incorporated to ensure projects remain on track and aligned with course objectives. Ongoing communication with the instructor and, when applicable, domain experts is essential for refining project goals and addressing challenges. Students are required to adhere to ethical standards and maintain thorough, well-organized documentation throughout the project lifecycle. Successful completion of the course demands active engagement, critical thinking, and a sustained commitment to producing high-quality research deliverables by the established deadlines.
Homework Policy
This course does not include traditional homework assignments. Instead, students will focus on completing a comprehensive research project, with progress evaluated through structured milestone submissions such as the project proposal, multiple progress reports, project drafts, and presentations. These milestones function as formal checkpoints to support steady progress and provide constructive feedback. Although there are no additional assignments, students are expected to manage their time effectively and devote consistent effort to meet all deadlines and course expectations.
Projects
Projects are a central component of this course and provide students with the opportunity to apply data science concepts to real-world problems. To ensure a productive and well-managed project experience, the following policies apply:
Project Proposal: Students must submit a project proposal outlining objectives, scope, methodology, and expected outcomes. The proposal must be approved by the instructor before substantive project work begins.
Progress Updates and Reports: Students are required to submit regular written progress updates documenting milestones achieved, challenges encountered, and planned next steps.
Progress Presentations: Students will deliver multiple in-class progress presentations throughout the semester. These presentations are intended to communicate project status, receive feedback, and demonstrate ongoing development.
Project Drafts: Students must submit multiple project drafts at designated points in the semester. Drafts will be reviewed to assess structure, methodology, analysis quality, and clarity of documentation, and feedback will be provided for revision.
Documentation: Comprehensive documentation—including data sources, data preparation procedures, methodologies, and code—is required. Clear and thorough documentation is essential for transparency, understanding, and reproducibility.
Final Presentation: All students must deliver a final presentation that clearly communicates the project’s objectives, methodology, results, and visualizations. This presentation serves as a formal demonstration of the completed work.
Ethical Considerations: All projects must comply with ethical standards, including respect for privacy, confidentiality, and applicable legal requirements. Any ethical concerns must be identified and addressed promptly.
Submission Deadlines: All project components must be submitted by the specified deadlines. Late submissions may result in grade penalties unless prior approval is granted.
- A 94-100% A- 90-93% 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.