Provided in the challenge forum is an artificial dataset detailing death counts by geography, cause of death, and demographic variables. NCIPC is interested in a solution for analyzing the data and:
- Detecting upticks and downticks (time series spikes, drops, other anomalies, upward- and downward trends) in the data. It should support different statistical methods, and queries (e.g. identification of trends by deviation from historical baseline; changing the window of historic baseline; selection of state/city for the analysis; comparison between states and cities, etc.).
- Generating statistical summary reports (e.g. the largest identified deviation of death counts from historic baseline; suicide deaths for ages 10-24 by state; etc.).
- Customizing specific analysis/query options mentioned in the previous two points.
- Providing an interactive graphic user interface (GUI) to inspect discovered trends, create summaries, and control all relevant parameters.
- Supporting exports of trends and summaries as static documents (Word or PDF).
- The core data analysis is performed remotely (on a cloud server, presumably deployed into AWS in production use); the core analysis is handled by Python or R code.
- The system uses a web interface for end users.
- The solution relies on software licensed under permissive free open-source licenses (e.g. Apache, MIT, etc.)
- These may not be hard requirements. If you have a different approach / technology / software with different licensing and additional benefits, you should consult with the copilot first, either via the challenge forum, or via email firstname.lastname@example.org (in case the question is too specific and may give away your ideas to other members)
Your submissions will be judged subjectively, based on how they fulfil the expectations explained above. Detailed feedback on the review and challenge outcomes will be shared to competitors.
The winning solutions will be presented to NCIPC. Assuming they are approved, we expect to run several follow-up challenges to create a production-ready solution, based on their feedback.