DataScienceSF is a way for you to harness the power of advanced analytics and applied statistics to implement a tool that helps improve your work.
This new service from DataSF aims to help departments achieve more with their existing resources and processes.
Through a 4 month engagement, DataSF’s Data Science team and your department will refine a problem, identify statistical methods to address it, and develop and institute a service change tool to improve your work. Projects are chosen through a bi-annual selection process. The final product of DataScienceSF isn't a recommendation or a report but a service change.
DataScienceSF will bring 3 key tool sets to bear on your data science questions:
Collectively, these tools allow us to create an actionable data insight to improve your work.
There are five basic types of civic problems data science can help address. Ask yourself if you or people in your department would want to:
Do you have trouble identifying targets in a larger population? If you find it difficult to identify people, geographic areas, or categories to target, data science can help.
Do you have a backlog that you tackle via first in, first out and are worried that you’re missing priority cases? Data science can help identify high, medium, and low priority cases by analysing existing data.
Do you find it hard to predict future conditions leading to reactive services? Many situations - good and bad - could be addressed more efficiently if caught early, even before they come to you. Data science identifies candidates for early intervention and engagement.
Do you use costly outreach methods (like mailers, text messages, forms, surveys) that aren’t tested before implementation? Data science can help identify and test various approaches to identify those that would be most successful.
Do you find it difficult to identify where to place or distribute resources to be more effective? Data science uses existing data to optimize distribution of services (people, resources, equipment) to minimize response time and maximize throughput.
You can learn more about the different types and see examples of civic data science projects from other cities in our PowerPoint deck.
Below are some general expectations we have for the project. We will refine and clarify roles and responsibilities via a project charter.
Projects submitted before April 3rd have the option to be considered for expedited review.
Expedited review is designed to help DataSF and Departments with 'shovel ready' projects shortcut the longer evaluation process. The department gains quicker access to DataScienceSF services as well as the competitive advantage of being evaluated in a smaller pool of submissions.
Departments wanting to be considered for Expedited review need to be willing to start their project in April or early May if they are selected.
Only 1-2 projects will be selected. If you apply for expedited review and are not selected, you will still be evaluated with the full submissions and have another opportunity to be selected.
DataScienceSF Deck (PPT), includes
To be announced