Introduction
Measuring the success of General Assembly's Data Science Immersive course requires a comprehensive approach that considers multiple stakeholders and outcomes. To effectively evaluate this educational product, I'll follow a structured framework covering core metrics, supporting indicators, and risk factors while considering all key stakeholders.
I'll follow a simple success metrics framework covering product context, success metrics hierarchy, and strategic initiatives.
Step 1
Product Context
General Assembly's Data Science Immersive course is an intensive, full-time program designed to transform students into job-ready data scientists. Key stakeholders include:
- Students: Seeking career transition or skill enhancement
- Instructors: Delivering high-quality education
- Employers: Hiring skilled data scientists
- General Assembly: Maintaining reputation and profitability
The user flow typically involves:
- Application and admission
- Intensive 12-week curriculum covering statistics, programming, machine learning, etc.
- Project work and portfolio development
- Career support and job search
This course fits into General Assembly's broader strategy of providing practical, in-demand tech skills training. Competitors include other bootcamps like Flatiron School and university-affiliated programs. The product is in a growth stage, with increasing demand for data science skills driving expansion.
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