If you start with contract data, know this:

Contract Opportunities is a government-wide list of notices of proposed contract actions expected to exceed $25,000. Notices include solicitations, pre-solicitations, sole source justifications, awards and other notices related to the acquisition of supplies and services.

This project assumes that you have some libraries installed

• tidyverse is required
• dlookr is used for a quick snapshot of data

• /data: directory will hold data locally - dir should be empty on github unless we generate some local data or get some small data files that are easier to keep (do not commit large files here, .gitignore should prevent this)
• /analysis: directory will hold analysis scripts
• /helpers: directory holds helper scripts
• /outputs: directory holds outputs
• /discuss: directory for discussion of topics and ideas - most intermediate products should probably go here

• 12 slides or fewer (PDF or PPT)
• describes project:
  • Question(s) you are trying to answer
  • Methods, techniques, & approaches used
  • Tool(s) & framework(s) used along with lessons learned
  • Results (visualization and explanations/insights)

• only if a finalist

This criteria considers how novel your approach is:

• Does your approach use a single dataset or integrate across multiple datasets?
• Does your approach use existing/mature methods for data analysis or does it extend beyond current typical practices?
• How far does your aproach extend beyond the common Nxxx uses for this data?
• What technical hurdles did you have to overcome to complete your effort?

This criteria considers the equality of you approach and how well you accurately depict that approach in your submitted presentation.

This is an evaluation of how useful the resultant analytical observations and/or dashboards are for executives. This also consideres the repeatability of the analytics and the TRL for using it (tool/model/dashboard) in the future in support of such executives.

This is an evaluation of how useful the ... are for everyday data science technical professionals. ...