Full Stack with Data Science
Do the analysis, build the model, then ship the application around it.
Most data scientists hand over a notebook. This programme makes you the person who takes an idea from raw data through modelling to a working, deployed application that people actually use — a combination that is rare and very well paid.
- Duration
- 24 weeks
- Weekly effort
- 12–15 hrs/week
- Level
- Beginner friendly
- Format
- Live cohort + project reviews
- Mode
- Classroom in Marathahalli & Live online
- Typical range
- ₹7–26 LPA
Outcomes
What you will be able to do
Not topics covered — capabilities you can demonstrate in an interview and on day one of the job.
- Write production-quality Python for data work, not just notebook scratch code
- Run the full statistical toolkit — EDA, inference, experiment design, causal thinking
- Build, tune and validate ML models, and explain them to a non-technical stakeholder
- Wrap models in APIs and web applications and deploy them to the cloud
- Communicate findings that actually change a business decision
Curriculum
5 modules over 24 weeks
Every module ends in working code that is reviewed by a mentor before you move on.
01Python & SQL for Data
Weeks 1–4
Python & SQL for Data
Weeks 1–4
- Python fundamentals through to clean, testable, modular code
- pandas and Polars for real-world data wrangling at scale
- SQL from joins to window functions and query optimisation
- Data cleaning, missing data strategy and reproducible pipelines
02Statistics & Analysis
Weeks 5–8
Statistics & Analysis
Weeks 5–8
- Descriptive statistics, distributions and exploratory data analysis
- Hypothesis testing, confidence intervals and common statistical traps
- A/B testing and experiment design end to end
- Introduction to causal inference — correlation is still not causation
- Visualisation and storytelling with data
03Machine Learning
Weeks 9–14
Machine Learning
Weeks 9–14
- Regression, classification, trees and gradient boosting with XGBoost
- Feature engineering, leakage, cross-validation and honest evaluation
- Unsupervised learning: clustering, dimensionality reduction, anomaly detection
- Time series forecasting and recommendation systems
- Model interpretability with SHAP, and fairness considerations
04Deep Learning & Modern AI
Weeks 15–18
Deep Learning & Modern AI
Weeks 15–18
- Neural networks and PyTorch fundamentals
- CNNs for vision and transformers for text
- Transfer learning and using pretrained models effectively
- Practical LLM use for data science: extraction, classification, synthetic data
05The Full Stack Half
Weeks 19–24
The Full Stack Half
Weeks 19–24
- Serving models with FastAPI; Streamlit for rapid internal tools
- React and Next.js essentials for building a real front end
- Databases, caching and application architecture for data products
- Docker, cloud deployment, MLflow tracking and monitoring
- Capstone build, business presentation and interview preparation
Capstone
What you walk out with
Portfolio work substantial enough to carry a technical interview on its own.
An end-to-end data product: raw data through modelling to a deployed web application with a live dashboard
A business case study presented to a panel — analysis, recommendation and the numbers behind it
Tools & stack
Roles this leads to
- Data Scientist
- ML Engineer
- Full Stack Data Scientist
- Analytics Engineer
- Decision Scientist
Who it is for
- Graduates in engineering, statistics, maths or commerce
- Analysts who want to move from dashboards into modelling and products
- Developers adding data science depth
Prerequisites
- Comfort with school-level mathematics
- Basic programming exposure helps; we start Python from the ground up
Enrol
Get the full syllabus for Full Stack with Data Science
Request the detailed module-by-module syllabus, current batch dates and fee structure. A counsellor will also check whether this is the right programme for your background.
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