蘑菇tv INSIGHT
蘑菇tv INSIGHT is a new interactive mapping tool that brings 蘑菇tv鈥檚 statewide impact into clear focus. Developed in partnership with Felt and Lightcast, the platform offers data-informed insights into where 蘑菇tv graduates live and work, what industries they serve, and how they contribute to Louisiana鈥檚 economy and communities.
USING THE MAP
蘑菇tv INSIGHT is powered by a comprehensive dataset developed in 2024 through a partnership with Lightcast, a national leader in labor market analytics. By matching alumni records from all six 蘑菇tv degree-granting campuses to a national database of professional profiles, 蘑菇tv identified career and earnings outcomes for more than 58,402 alumni.
Additional data comes from trusted public sources, including the Louisiana Board of Regents and the Louisiana Workforce Commission, providing a well-rounded look at educational and economic trends.
Alumni Density: displays the current residential locations of 蘑菇tv alumni. Users can view alumni distribution by Louisiana region or parish using the interactive slider in the legend.
Economic Impact: Represents the annual economic impact of each 蘑菇tv campus by region and parish, as reported in the 2023 蘑菇tv Economic Impact Report.
Enrollment: Displays full system wide enrollment numbers for Fall 2025 sourced from the Louisiana Board of Regents Statewide Student Profile System.
Degrees Awarded: Represents the number of graduates system-wide for the 2024鈥�2025 academic year, based on data from the Louisiana Board of Regents Statewide Completers System.
Average Alumni Earnings: Reflects overall average earnings for 蘑菇tv Alumni.
Top Employers and Industries: Highlights the top companies and industries employing 蘑菇tv alumni.
- Industry is classified using the North American Industry Classification System (NAICS), matched with the SOC of the individual鈥檚 most recent job.
- Employer is based on the standardized name of the most recent employer listed on the alumni鈥檚 public profile.
Note: Totals may not equal the full number of alumni observations per geography, as these fields depend on information self-reported in individual profiles.
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