AI & Data Engineering Bootcamp
From data to real products in 2–3 days. Students go from an idea/prompt to a working mini product using modern AI + engineering workflows.
Ideal for
Schools, colleges and coaching centers who want students to build real projects with an industry-ready roadmap.
Why this matters
73% of employers say graduates lack practical coding skills.
4.2M new developer jobs expected globally by 2027.
85% of AI jobs require LLM/prompt engineering skills.
Deliverables
PPT deck, sample datasets, hands-on notebooks, GitHub code, exercises and completion certificate template.
Outcome
Students finish with a working mini-project they can demo in interviews and add to their portfolio.
Industry context first
We connect fundamentals to real job roles: data analyst, data engineer, big data engineer, ML engineer and backend developer.
Hands-on learning
Students work in Jupyter Notebooks and ship small milestones throughout the bootcamp.
Institution-friendly delivery
Batch-wise delivery, lab setup checklist, trainer-led sessions, and optional evaluation rubric.
What you’ll learn (4 tracks)
A clear path from fundamentals to full stack, big data engineering, and real AI applications.
Programming Fundamentals
Python & JavaScript, data types & functions, OOP, file handling, API integration basics, and data ingestion using Apache Kafka.
Full Stack Development
HTML/CSS, React basics, Node.js + Express, REST API design, MongoDB/PostgreSQL, mini full stack project.
Big Data & Data Engineering
Hadoop ecosystem overview, Apache Spark basics, data pipelines (ETL/ELT), data lake vs data warehouse concepts, batch vs stream processing.
AI / LLM Applications
Prompt engineering, LLM API integration, RAG concepts, vector DB overview, AI-powered web apps.
What students walk away with
Clear deliverables that help in placements, internships and real project credits.
Completion certificate
Digital + printable certificate for each participant.
GitHub portfolio
2–3 small projects and a structured repository students can showcase.
AI/LLM experience
Hands-on exposure to prompt engineering and LLM app workflows.
Big Data exposure
Understanding of Hadoop, Spark, and modern data pipeline architectures used in industry.
Capstone demo
Team-built mini AI app demo with a clear presentation checklist.
Alumni community
Optional Discord/Slack group for continued support and updates.
Career guidance
Resume tips, interview prep pointers, and roadmap for next steps.
What students build
A mini product flow: ingest dataset, clean and transform using data pipelines, store in a database, expose insights via API, and show outputs in a dashboard — with big data tools in the mix.
Why institutions choose this
It is not generic theory. Students see the end-to-end path from data to a usable product artifact. Great for placements and project-based credits.