Curriculum Overview
General Assembly's data science bootcamp covers the full data pipeline, starting with Python programming, statistical foundations, and data wrangling using pandas. Core modules include exploratory data analysis, machine‑learning algorithms (regression, classification, clustering), and model deployment with Flask or Docker. The program also teaches SQL for database interaction, version control with Git, and cloud basics on AWS or GCP. Throughout, students complete real‑world projects that simulate business problems, building a portfolio that showcases end‑to‑end analytics work.
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Program Format and Schedule
The bootcamp runs in two main formats: a full‑time, 12‑week immersive track and a part‑time, 24‑week evening track. Both use a blended learning model combining live instructor‑led sessions, recorded lectures, and weekly labs. Students receive a dedicated mentor for code reviews and career coaching. The schedule is intensive; full‑time learners commit 40‑50 hours per week, while part‑time students allocate 15‑20 hours, typically evenings and weekends.
Cost and Financial Options
Tuition for the full‑time track is $15,950, and the part‑time track costs $16,950. General Assembly offers several financing routes: a 0% interest loan through Partner Finance, a deferred‑payment plan (pay after securing a job), and a limited number of scholarships for underrepresented groups. Prospective students should compare the total cost of each option, including any interest or fees, before committing.
Career Outcomes and Support
Graduates receive a comprehensive career services package: resume workshops, mock interviews, networking events, and access to a hiring portal with partner companies. Reported outcomes vary by cohort, but many alumni transition into roles such as data analyst, junior data scientist, or business intelligence analyst within three months. Success depends on prior experience, networking effort, and the strength of the final portfolio.
Who Benefits Most
The bootcamp is designed for professionals with a quantitative background—engineers, analysts, or developers—who want to pivot into data science quickly. Beginners with no coding experience may find the pace challenging, though the part‑time option provides extra time for fundamentals. Those seeking a credential for career advancement within their current company also benefit from the program's industry‑focused projects.
Comparison Table
| Attribute | Full‑Time Track | Part‑Time Track |
|---|---|---|
| Duration | 12 weeks | 24 weeks |
| Weekly Time Commitment | 40‑50 hrs | 15‑20 hrs (evenings/weekends) |
| Tuition | $15,950 | $16,950 |
| Typical Outcome Timeline | 3‑4 months post‑graduation | 4‑6 months post‑graduation |
Key Takeaways
- Comprehensive curriculum that spans Python, SQL, ML, and deployment.
- Two scheduling options accommodate both career‑changers and working professionals.
- Cost is high but offset by financing plans and a strong career‑services network.
- Success hinges on prior quantitative skills and active engagement with the alumni network.