House of Data/Curriculum
What you actually learn
The whole syllabus, in the open.
No "advanced modules" you only hear about after you pay. Six modules in the order they become useful, eight levels from your first line of Python to your first offer, and the full list of tools — the same names sitting in the job descriptions you are about to apply to.
The syllabus
Six modules, in the order they're useful.
Beginner to advanced, then specialisation, then production, then the job. No module starts before the one under it is solid.
Fundamentals of Data Analysis
Excel · Beginner Python · Tableau
Analytical Proficiency & Business Insights
SQL · Product analytics
Foundations of Machine Learning & Deep Learning
Advanced Python & libraries · Probability, statistics, calculus, linear algebra · Intro to neural networks
Specialisations
Machine Learning — supervised, unsupervised, recommenders · and / or Deep Learning — neural networks, computer vision, NLP, Generative AI, RAG and AI agents
ML Pipeline Development & Deployment
MLOps · Advanced data structures & algorithms (optional)
Get placed at top product companies
Build a strong profile · Apply the right way · Ace the interview
The build sequence
Eight levels. Foundation to roof.
Nothing here is optional and nothing is left to you. Each level is scheduled, taught and checked before the next one starts.
Enrol
Register, pick your batch and get a profile analysis — graduation year, gaps, prior experience — with a realistic package report before you spend a rupee on the course.
Live training starts
Online, offline, weekday or weekend. Fifteen students maximum, so you can interrupt and ask.
Real-time projects
Two machine learning and two Gen AI projects built the way they're built at work — versioned, deployed to Azure, GCP or AWS, with MLOps and LLMOps around them.
One-to-one support
Stuck at 11pm on a model that won't converge? That's what the mentor line is for. Any time, through the program.
Corporate readiness
Interview vocabulary, corporate etiquette, team outings. The part most institutes skip and every interview panel notices.
Certification
Complete the program and get certified by House of Data, with your project portfolio attached.
Profile marketing
We build the resume ourselves and push your profile through Naukri, LinkedIn and our hiring partners, tuned to whatever the market is asking for that month.
Placed
Multiple offers, your choice of city and company type — product, service, hybrid or startup. If it doesn't happen here, you move to the Advanced Placement Plan.
The stack
The tools on the job descriptions.
Not a survey of everything that exists. This is what Indian hiring managers are asking for in 2026, and it's what you'll have your hands on during the program. Gold means it's showing up in almost every Gen AI job description right now.
Languages & data
- Python
- SQL
- R
- NoSQL
- Pandas
- NumPy
- FastAPI
- Power BI
- Tableau
- Advanced Excel
ML & deep learning
- scikit-learn
- PyTorch
- TensorFlow
- Transformers
- CNNs
- RNNs
- XGBoost
- OpenCV
Gen AI & LLMs
- Hugging Face
- OpenAI API
- Anthropic API
- Gemini API
- Prompt engineering
- Fine-tuning
- Open-weight models
Agents & orchestration
- LangChain
- LangGraph
- CrewAI
- MCP
- Tool calling
- Multi-agent systems
- AI workflows
Retrieval & vector data
- RAG
- Chunking & embeddings
- Pinecone
- Qdrant
- FAISS
- ChromaDB
- Knowledge graphs
Deployment & cloud
- Docker
- Kubernetes
- Azure
- AWS
- GCP
- MLOps
- LLMOps
- CI/CD
Evaluation & governance
- LLM evaluation
- Benchmarking
- Guardrails
- Red teaming
- Responsible AI
- AI security
Analytics & BI
- Power Query
- Excel Macros
- Product analytics
- A/B testing
- Business analytics
- Automation
Ways of working
- Git & GitHub
- Agile delivery
- Code review
- System design
- Interview vocabulary
Two lengths, one syllabus.
Everything above is taught on both programmes — the Fast Track compresses it into 2 + 1 months, the Flagship takes six and goes deeper on specialisation, LLMOps and your own project.