Data Science Capstone
Spring 2026
- Important Course Documents
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Class Materials
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Links used in class will be posted here:
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Proposal Writing
Key Components of a Data Science Proposal
Below you will find some key components of a data science proposal. Your proposal should address all of these areas. In some cases the sections are combined, but all the information should be there. Your proposal should be a typed .pdf document with clearly marked sections and titles. Your audience is fellow data scientists who have a good idea of the basics of data cleaning, analysis, and modeling. You may need to do some work to give your reader the background into your area of application. You should hand in something that you are proud of! Here is a nice article about writing a data proposal: Click Here!Title and Abstract / Summary: A concise, descriptive title and a brief summary (just a few sentences) that outlines the problem, motivation, hypothesis, and proposed methods.
Introduction / Problem Statement: An overview of the topic that provides necessary background and clearly defines the specific problem or knowledge gap your project addresses, including its relevance and significance. You could also review literature or cite reference that you will be relying on.
Objectives and Hypotheses: Clearly stated objectives that are specific, measurable, achievable, relevant, and time-bound (SMART). If you have a hypothesis that you plan to test state it here.
Methodology and Research Design: A detailed description of the technical approach, including data sources, analytical tools, and evaluation strategies.
Data: Description of datasets, including sources (with URLs if applicable), size, features, and required cleaning or preprocessing steps.
Tools and Techniques: Software, libraries, statistical methods, and machine learning algorithms to be used in the project.
Plan: A step-by-step outline of data collection, analysis, modeling, and evaluation. Visual aids such as flowcharts may be included where appropriate.
Expected Results and Impact: A discussion of anticipated outcomes and their potential contributions, such as new insights, improved practices, or policy implications. Talk also about any ethical considerations.
Timeline and Resources: A realistic project timeline with milestones and deadlines, along with required resources such as software, computing power, or budget considerations.
References / Bibliography: A complete and properly formatted list of all cited sources to establish academic credibility.
Best Practices
Be Clear and Concise: Use plain language and avoid unnecessary jargon.
Use Visuals: Include charts, graphs, and tables to clarify complex ideas.
Tell a Story: Present analytics within a coherent narrative that guides the reader from problem to solution.
Tailor to Your Audience: Adapt the tone and technical depth to the needs of your reviewers.
Proofread Thoroughly: Errors can undermine confidence in your work.
It is okay if your plans change during the semester. Sometimes as you start exploring your data and trying new methods, your project will change. However, it is good to have a clear plan at all times, so when your plan changes, update your timeline!
"ER MER GERD! WER SENIORS!!!"
Image thanks to: weruletheinternet.com -
Proposal Writing