Monthly Action Plan

05 Monthly Action Plan

This calendar is designed to move quickly from clarification of your academic profile to a polished application narrative centered on civic data science. Each phase builds toward presenting your technical work clearly and credibly to admissions readers at Berkeley, Carnegie Mellon, and Georgia Tech. Where deeper guidance is needed, this plan references the relevant sections of the overall strategy.

Month Primary Actions & Target Outcomes
Month 1
  • Audit your quantitative coursework. Create a complete list of all math, statistics, and advanced quantitative classes taken at your high school. Include course titles, level (AP/Honors/etc. if applicable), and the sequence relative to what your school offers. The goal is to clearly demonstrate the highest available rigor (see §01 Academic Profile Analysis).
  • Confirm school context. Verify with your counselor what the most advanced math pathway at your school looks like so your application accurately communicates how far you progressed within available offerings.
  • Begin organizing documentation. Collect syllabi, project summaries, or major assignments from your highest-level quantitative classes so they can inform activity descriptions or portfolio links later in the process.
Month 2
  • Finalize your academic rigor narrative. Convert the coursework audit into a short internal summary explaining the quantitative path you pursued. This will help shape activity descriptions and counselor coordination.
  • Revisit the police use‑of‑force dataset project. Define the analytical questions you want the project to answer and outline the statistical modeling approach you plan to implement (see §03 Creative Projects).
  • Establish a reproducible workflow. Set up version‑controlled code, organized datasets, and clear documentation so the project can eventually be shared publicly.
Month 3
  • Deepen the technical analysis. Expand the dataset project beyond descriptive statistics into stronger quantitative methods. Focus on transparent methodology and clearly interpretable outputs.
  • Document every step. Write clear explanations of data sources, assumptions, and modeling choices so readers can understand both the technical work and its civic relevance.
  • Prepare visual outputs. Begin building charts, model summaries, or interactive outputs that make the analysis understandable to non‑technical audiences.
Month 4
  • Finalize the analytical framework. Complete the statistical modeling and validate that the code runs cleanly from raw data to final results (see §03 Creative Projects).
  • Write project documentation. Draft a clear explanation of the research question, dataset construction, and analytical approach so the project can stand on its own if shared publicly.
  • Prepare a publishable repository. Organize code, data documentation, and a readable project overview that can be linked in applications later.
Month 5
  • Publish the project publicly. Host the analysis and documentation in a location that allows admissions readers to view the work easily (for example through a repository or similar platform).
  • Write a plain‑language summary. Create a concise explanation of the project’s findings and civic relevance that could be understood by journalists or community organizations.
  • Prepare outreach materials. Draft short emails introducing the project and its potential public value.
Month 6
  • Begin outreach to civic stakeholders. Share the project with journalists, nonprofits, or civic groups who work on policing transparency or data‑driven policy analysis.
  • Track responses and engagement. Keep records of outreach and any feedback or interest generated; this can help demonstrate real‑world engagement with the project.
  • Start essay brainstorming. Outline how your civic data science work connects to broader themes of public impact and responsible data use (see §06 Essay Strategy).
Month 7
  • Draft your core personal and supplemental essays. Emphasize the intersection between statistical analysis and public accountability in civic systems.
  • Align school‑specific narratives. Begin shaping essays that connect your interests in data and public impact to institutional values at Berkeley, Carnegie Mellon, and Georgia Tech.
  • Integrate the project into your narrative. Ensure your writing clearly explains the intellectual motivation behind the dataset project and what you learned from building it.
Month 8
  • Revise essays strategically. Strengthen clarity and focus, ensuring each essay highlights both technical curiosity and civic purpose (see §06 Essay Strategy).
  • Prepare activity descriptions. Write precise descriptions for the dataset project and any related work, emphasizing the analytical techniques used and the real‑world problem addressed.
  • Confirm technical links. Make sure the published project repository, documentation, and any visual outputs are accessible and clearly organized.
Month 9
  • Finalize the application activity section. Refine descriptions so they communicate technical depth within the limited character space (see §07 Application Execution).
  • Integrate project links thoughtfully. Include documentation links only where the application format allows and where they strengthen the narrative.
  • Conduct a full application review. Check that academic rigor, civic data science work, and quantitative interests are consistently presented across essays and activities.
Month 10
  • Complete final proofreading. Ensure essays, activity entries, and technical references are accurate and error‑free.
  • Verify all materials before submission. Confirm that every section of the application clearly communicates your quantitative preparation and civic data science project.
  • Submit applications with final documentation links included. The goal is a cohesive application that shows both technical skill and a commitment to data‑driven public impact.

By following this timeline, Zara Okonkwo, you ensure that your most distinctive element—the police use‑of‑force data analysis—evolves from an interesting project into a technically rigorous, publicly documented example of civic data science. The later months then focus on translating that work into compelling essays and precise activity descriptions so admissions readers can immediately understand both the technical depth and the public purpose behind it.

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