Launch your
career in Data & AI
Transform rows of data into rows of opportunity. Master the tools and techniques of the Modern Data Stack — build scalable pipelines, analyze insights with AI, and launch your career in Data Engineering and Analytics.
| cohort.id | C04 |
| duration | 16 weeks |
| seats.total | 24 |
| seats.open | 7 |
| format | remote · live |
| tracks | analytics · eng · ai/ml |
| next.start | TBD |
Studio, not classroom
Built by practicing Data & AI engineers who've delivered real-world pipelines, analytics systems, and AI products. You'll learn by doing — from foundations to deployment.
Concept-first, tool-agnostic
Principles before products. You'll swap vendors confidently because you understand what's underneath.
Hands-on labs, end-to-end
Build and deploy real pipelines, real RAG systems, real agents — against production-scale data.
Azure-first + cross-cloud
Deep Azure Synapse and AI Foundry. Every service mapped to its AWS equivalent so you stay portable.
LLMs, vectors & agents
Modern AI taught as engineering, not magic — embeddings, retrieval, MCP, observability.
Agentic AI, prompt engineering, Databricks & Bedrock
Nine AI-era modules threaded through the back half of the program — agents with MCP, LLMOps and evals, fine-tuning, governance, AI-assisted engineering. Azure-first, AWS-fluent.
Three tracks, one studio
Everyone runs the 16-week core. In weeks 9–16 you specialize — pick the track that matches the career you're actually trying to build.
Analytics Track
Decision-shaping analyst the business actually listens to. Excel mastery to statistical rigor to boardroom-ready dashboards.
- Advanced SQL, dimensional modeling & Excel power functions
- Python (pandas) & R (dplyr) for data manipulation
- Data collection: APIs, web scraping, CSV pipelines
- Data cleanup: missing values, outliers, transformations
- Statistical analysis: hypothesis, correlation, regression
- BI & viz: Tableau, Power BI, matplotlib, seaborn
Engineering Track
Full data engineering lifecycle — generation, storage, ingestion, serving. Our deepest track, built from production work shipped weekly.
- Modern ELT at scale: Airflow · dbt · Prefect
- Lakehouse architecture: Delta Lake · Databricks · Unity Catalog
- Databases: relational, document, columnar, KV, graph
- Streaming & messaging: Kafka · Event Hubs · SQS
- Containers, CI/CD & IaC: Docker · K8s · Terraform
- Data governance: quality, lineage, GDPR & privacy
AI / ML Track
Deploy AI systems that hold up under audit. LLMs, retrieval, agents, multimodal — taught as engineering, with safety in every lab.
- Prompt engineering: structured outputs, evals, routing
- RAG: chunking, embeddings, hybrid retrieval, rerankers
- Vector stores: AI Search · Bedrock KBs · Pinecone · Qdrant
- Agentic AI: LangGraph · Foundry · Bedrock Agents · MCP
- Multimodal AI: vision, Whisper, image generation
- AI safety: prompt injection, moderation, adversarial
From rows of data, rows of opportunity
See how our graduates transformed their careers and doubled their salaries. Representative outcomes; references available on interview.
Senior Data Scientist · $125K
Data Engineer · $135K
Senior Analytics Manager · $95K
Ready to transform your career?
Join thousands of professionals who have successfully transitioned into high-paying data careers. Cohort 04 start date: TBD — applications reviewed on a rolling basis.