Detroit K-12 Education Data Landscape Scan
I conducted a Landscape Scan of the Detroit K-12 Education data ecosystem to identify and map key actors. This scan was used to help guide the development of The Skillman Foundation data strategy as well, as the base for a cross sector convening of actors within and adjacent to the ecosystem for collaboration, in an effort to enhance and grow the capacity for continued growth in and for the Detroit education ecosystem. You can read more about the process and findings at The Skillman Foundation blog.
Michigan School District: Federal Funding Analysis
I conducted an analysis on Michigan school districts and the amount of funding they receive from the Federal government in the 2023-2024 school year. All the data used came from MI School Data, and any missing data was not included in the report used. The dashboard visualizes district level federal funding, demographic, and other enrollment data. The dashboard can be filtered or sorted by district, locality (city, rural, suburb, & town), and district type (ISD, LEA, & PSA). The dashboard can be viewed on Tableau Public and the code used to prepare the data for visualization was conducted in R Studio and can be viewed on GitHub.
Telling Your Story: Where Communications and Data Meet
I presented a session at the Council of Michigan Foundations Annual Conference along with Tina Bennett the Communications Fellow that I work with at The Skillman Foundation. We presented on the connectivity between Data and Communications teams within organizations and how and why it is important to work together cross functionally. We also highlighted key tools that can be used across a wide range of organizations and helped provide some guidance to help attendees understand when and where each tool or platform would be best utilized. You can view the presentation deck below.
Multi-Layer Perceptron Models
I created three different MLP (Multi-layer Perceptron) models. These models were trained and tested on data sets for predicting the presence of breast cancer, identifying the actions of an individual using data collected by sensors in cell phones, and predicting the win/loss outcome of football games. The Python codes and outcomes can be viewed on GitHub.
