Skip to Main Content

Data Science Ph.D Program

A website for the Data Science students in the Doctorate Program

Dissertation Courses - Research Focused Course Work

Guide to the DIS 9000 Series: Your Doctoral Data Science Journey

1. DIS 9100 (9901): Proposal and Literature Review

Course Description for Data Science students
DIS 9100 is the keystone course in which you lay the intellectual groundwork for your doctoral research. Over the next term, you will transition from a nascent research idea to a fully articulated dissertation proposal, complete with clear research questions, a solid theoretical framework, and a comprehensive review of the literature. This course ensures that you identify knowledge gaps, justify your methodological choices, and secure ethical approval, setting the stage for the detailed planning and implementation phases that follow.

To do:

  • Craft Chapter 1 (Introduction & Research Design) and Chapter 2 (Background & Methodological Rationale)

  • Identify your dataset and get approval.

  • Define research questions and select an appropriate theoretical framework

  • Conduct a thematic literature review of 30–50 high-impact studies

  • Obtain IRB exemption or approval and document ethical considerations

  • Receive Chair and SME feedback to finalize your proposal draft

2. DIS 9200 (9902): Planning Final Steps

Course Description for Data Science students
DIS 9200 transforms the abstract concepts of your proposal into a concrete, executable study plan. Building on the foundations of DIS 9100, you will design your data preprocessing workflows, exploratory analyses, and modeling strategy in meticulous detail. By the end of this course, you will submit a complete draft prospectus—integrating your methodological roadmap, preliminary data work, and a schedule of deliverables—and secure committee approval to proceed to full implementation.

To Do:

  • Produce Chapter 3 (Study Plan & Preliminary Work) with a detailed Visio/Lucid Diagram workflow

  • Implement initial data cleaning, integration, and exploratory data analysis

  • Complete feature engineering

  • Outline the algorithms' selection and validation strategies

  • Draft and defend your Prospectus in the Doctoral Record (DR)

  • Complete and unofficial Presentation to your Chair and SME

  • Incorporate SME and Chair feedback via a formal Change Matrix

3. DIS 9300 (9903): Coding & Modeling Milestones

Course Description for Data Science students
DIS 9300 is the hands-on course where theory meets practice through rigorous implementation of your study design. Having secured committee approval for your prospectus, you will now focus on coding, model development, and validation. This course challenges you to execute every step of your analytical pipeline—from data cleaning and feature engineering to algorithm selection and hyperparameter tuning—while maintaining transparent documentation and version control.

To Do:

  • Develop and share a fully documented GitHub/Colab repository with data, code scripts, and a README

  • Perform end-to-end implementation: data preprocessing, feature engineering, model training, and tuning

  • Generate preliminary results and interpret them using confusion matrices, ROC/AUC, or equivalent metrics

  • Draft Chapter 4 (Implementation & Results) detailing your technical workflow and initial findings

  • Obtain Chair and SME approval on all coding milestones

4. DIS 9400 (9904): Implementation Wrap-Up & Defense

Course Description for Data Science students
DIS 9400 completes your doctoral journey, guiding you to synthesize implementation efforts into conclusive insights and prepare for your oral defense. You will refine your Chapter 4 results, craft Chapter 5’s discussion and recommendations, and develop a polished defense presentation highlighting the originality, rigor, and implications of your work. This capstone course supports you through final approvals, reproducibility publication, and the orchestration of your defense event.

To Do:

  • Finalize Chapter 4 with complete model evaluation and linkage to research questions.

  • Compose Chapter 5 (Discussion & Conclusions) with theoretical synthesis, limitations, and future directions.

  • Prepare and submit your defense slides, including an IRB/ethics overview

  • Coordinate and execute your oral defense with the Chair, SME, and Academic Reader

  • Publicly release your code and data repository for reproducibility, then receive final committee sign-off