Texas A&M Institute of Data Science
Higher Education • 
College Station, Texas, United States • 11-20 Employees
Company overview
| Headquarters | College Station, Texas, United States |
| Websites | |
| NAICS | 6112 |
| Keywords | Data Science |
| Employees | 11-20 |
| Socials |
Key Contacts at Texas A&M Institute of Data Science
Jennifer Cutler Scotti
Associate Director, Industry Engagement
Drew Casey
Associate Director For Program Engagement
Texas A&M Institute of Data Science Email Formats
Texas A&M Institute of Data Science uses 2 email formats. The most common is {first initial}{last name} (e.g., jdoe@tamu.edu), used 50% of the time.
| Format | Example | Percentage |
|---|---|---|
{first initial}{last name} | jdoe@tamu.edu | 50% |
{last name}{first initial} | doej@tamu.edu | 50% |
About Texas A&M Institute of Data Science
The Texas A&M Institute of Data Science (TAMIDS) serves and fosters collaborations across the university and its affiliated agencies. TAMIDS is a joint undertaking of Texas A&M University with the Texas A&M Engineering Experiment Station (TEES) and Texas A&M AgriLife Research. TAMIDS is an inclusive umbrella organization for data science and will facilitate interactions between researchers in diverse application areas and those with expertise in core methodologies, promote education in data science across the university, and pursue outreach to commercial and governmental organizations in the wider data science ecosystem.
Texas A&M Institute of Data Science revenue & valuation
| Annual revenue | $1,454,435 |
| Revenue per employee | $86,000 |
| Estimated valuation?This valuation is estimated based on industry average for the Higher Education industry and current estimated revenues | $4,700,000 |
| Total funding | No funding |
Employees by Management Level
Total employees: 11-20
Seniority
Employees
Employees by Department
Texas A&M Institute of Data Science has 4 employees across 3 departments.
Departments
Number of employees
Funding Data
Texas A&M Institute of Data Science has never raised funding before.
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