About DataScienceSF

You can use data science to address 5 basic types of civic problems. Ask yourself if you or people in your department need to:

Find the needle in the haystack

Data science can help identify people, geographic areas, or categories to target.

Prioritize your backlog

Stop missing priority cases. Data science can help identify high, medium, and low priority cases with existing data.

Flag "stuff" early

We can better address situations if we catch them early, even before they come to you. Data science identifies candidates for early intervention and engagement.

Conduct A/B testing

Data science can identify and test various approaches to find the most successful outreach methods.

Optimize your resources

Data science uses existing data to optimize distribution of services (people, resources, equipment) to cut response time and maximize throughput.

DataScienceSF will bring 3 key tool sets to bear on your service change questions: 

Analytical methods

DataScienceSF will help identify the right method for your problem. Our methods include sentiment analysis, machine learning, regression, data mining, classification, clustering, imputation, AB testing, forecasting and more.

Tools

We use a variety of languages, libraries, data engineering, and visualization tools. 

Languages include python, R, Javascript, NodeJS and SQL with a variety of libraries (SciPy, Pandas, etc). 

Data engineering tools include profiling, ETL, noticing, APIs and optimized data pipelines and storage/access. 

Visualization tools include D3.js, Gephi, Leaflet, PowerBI, ggplot2 and Shiny.

User experience research

We use data-driven tools to assess and help design the right implementation of a service change. This includes journey and process mapping, service blueprinting, ethnographic research and ride-alongs, iterative prototyping and usability testing.

We create an actionable data insight to improve your work with these tools.

We have a general set of expectations for department partners. And for each project, we refine and clarify roles and responsibilities via a project charter.

Commit to a service change

We expect departments to be open to and commit to a service change if that's where the data leads us.

Assign a Department Champion

We will need to name a Department Champion to be our liaison. They will help refine the problem statement and identify paths to service change. The Department Champion will dedicate about 5 hours per week to this project. This will taper off after the beginning.

Commit to working within our timeframe

Engagements last between 1 to 4 months. 

The engagement will be an iterative process. We will refine the original problem statement into something answerable. We will use the data available and implementable within any service change constraints. 

You can join a future cohort if now is not a good time.

Provide access to staff and business processes

We expect to do user research as part of the engagement. It may include shadowing employees, interviews, and other research methods. This will help identify relevant implementation factors to incorporate into the statistical model.

Provide timely access to data

Data science projects rely on timely data access. Plan to prepare and work with your technology or database team to provide data. As appropriate, we can sign data sharing MOUs (we have templates). Some projects may need ongoing data access to implement the model in real time.

Present and disseminate our findings. 

Part of our goal is to document and communicate what we learn and achieve. And we want other departments and jurisdictions to benefit too. We will need input, feedback, and participation from departments in this process.