Best After 12th 4-Month Data Science Program in Jalandhar
A four-month fast-track path from data fundamentals to production-ready AI systems — Python and data engineering, machine learning and deep learning, LLMs, RAG, AI agents and cloud deployment, across 16 modules and one industry capstone.
Data Science Program Course in Jalandhar
A 4-month fast-track AI-integrated Data Science programme for school leavers at techcadd Jalandhar (4.9★, 556+ reviews): Excel and Power BI, Python, SQL, Pandas and Polars, machine learning with scikit-learn and gradient boosting, PyTorch and computer vision, LLMs, vector databases, RAG, AI agents, FastAPI and cloud deployment — 16 modules ending in an industry AI SaaS capstone.
Key Highlights :
- Duration: 4 Months
- Mode: Classroom & 1-on-1
- Eligibility: 12th Pass
- Includes: Placement Support
Course Overview
This is the Data Science Mastery Program on its four-month fast track, written for someone starting straight after 12th who wants working AI skills quickly rather than the longest possible syllabus. Sixteen modules across four months, ending in a complete industry-level AI SaaS application. What makes it different from an older data science course is that the classical pipeline and the LLM stack are taught as one job: you finish able to clean data and train a model, and also to put a RAG assistant or an AI agent in front of a business.
Month one is data and programming foundations — advanced Excel, Power Query, Power BI, DAX, business dashboards, KPI reporting and data literacy; then modern Python with VS Code, the uv package manager, OOP, exception handling, type hinting, pytest, Ruff and Black; Git, GitHub, Git Flow and GitHub Copilot alongside SQL on PostgreSQL with database design, window functions and query optimisation; and finally APIs, JSON, FastAPI basics with JWT and Postman, plus Pandas 2.x, NumPy, Polars, DuckDB and PyArrow.
Month two is data science, machine learning and deep learning: data cleaning, feature engineering, exploratory analysis, interactive visualisation with Plotly and Streamlit, statistics, probability and preprocessing; scikit-learn with pipelines and cross validation; gradient boosting with XGBoost, LightGBM and CatBoost, model evaluation and hyperparameter optimisation; then deep learning fundamentals with PyTorch, tensor operations and neural networks. Month three moves into computer vision and the LLM stack — CNNs, transfer learning and OpenCV, transformers, Hugging Face and tokenizers; LLM fundamentals covering tokenization, embeddings, the attention mechanism, prompt engineering and structured prompting; the OpenAI, Gemini, Claude and Grok APIs alongside Ollama and LiteLLM; and embeddings with FAISS, ChromaDB, Pinecone, Qdrant and Milvus for semantic search.
Month four turns all of it into applications: RAG architecture with hybrid search and guardrails, LangChain, LangGraph, CrewAI and the Model Context Protocol with tool calling, AI agents and multi-agent systems; AI application development with FastAPI advanced, async programming, WebSockets, Streamlit, Gradio and Chainlit; cloud deployment and AI security through Docker, Docker Compose, AWS, Azure AI, Google Vertex AI, prompt injection defence, responsible AI and CI/CD. It closes with the industry capstone — a complete AI SaaS application integrating FastAPI, PostgreSQL, RAG pipelines, AI agents and Docker containerisation, deployed to the cloud and delivered with documentation and a professional GitHub portfolio.
What You'll Learn
Every module ends in something you have built and a trainer has reviewed, so the list below is work you will have done rather than topics you will have heard about.
- 01
A business dashboard in month one
Advanced Excel, Power Query, DAX and Power BI producing a real KPI dashboard — the piece that makes you useful to a Jalandhar employer before the Python work has even finished.
- 02
A tuned, evaluated model
Not a fitted notebook: a scikit-learn pipeline with cross validation, then XGBoost, LightGBM and CatBoost compared and hyperparameter-tuned, with an evaluation you can defend.
- 03
A working RAG assistant
Documents embedded into a vector database, retrieved with hybrid search, answered by an LLM behind guardrails, and wrapped in a Streamlit, Gradio or Chainlit interface.
- 04
An industry AI SaaS capstone
FastAPI and PostgreSQL, RAG pipelines and AI agents, containerised with Docker, deployed to the cloud with CI/CD, documented and pushed to a professional GitHub portfolio.
Course Curriculum
The syllabus is 16 modules across four months, ending in one industry capstone. Month 1 builds the data and programming foundations, month 2 covers data science, machine learning and deep learning, month 3 moves into computer vision, LLMs and vector search, and month 4 turns it into applications with RAG, agents, cloud deployment and the capstone.
Month 1 — Data & Programming Foundations
- 1
Module 01 · Excel, Power BI & Data Literacy — Excel Advanced, Power Query, Power BI, DAX, business dashboards, KPI reporting, AI productivity, data literacy
- 2
Module 02 · Python Fundamentals & Engineering Practices — Python fundamentals, VS Code, uv package manager, OOP, exception handling, type hinting, pytest, Ruff, Black
- 3
Module 03 · Git, GitHub & SQL Foundations — Git, GitHub, Git Flow, GitHub Copilot, SQL (PostgreSQL), database design, window functions, query optimisation
- 4
Module 04 · APIs & Data Engineering — APIs, JSON, FastAPI basics, JWT, Postman, Pandas 2.x, NumPy, Polars, DuckDB, PyArrow
Tools you will actually work in
Everything here is installed on the lab machines and used on live client work, not shown once in a slide and forgotten.
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Who can do this course
Students with a term to spare
Four months fits a long vacation or the gap between school and college, and you finish with a deployed AI SaaS capstone rather than an unfinished playlist.
Commerce and arts students
Nothing here needs school physics or higher mathematics. Statistics and probability are taught at the depth the modelling work actually needs, and the Excel and Power BI work is directly employable on its own.
Degree students who want a head start
If you are entering a BCA, BBA or B.Sc, arriving already able to build a dashboard, train a model and ship a RAG assistant changes what your first two years look like.
Anyone testing whether data suits them
Four months is a real commitment but a bounded one. If it clicks, the six-month certificate programme goes deeper into deep learning, computer vision and a longer capstone month.
Self-taught learners
If free tutorials left you with half-finished notebooks, what changes here is a trainer reading your code each week and a capstone with a deadline attached.
Why this programme is worth your year
Excel, Power BI and data literacy
Advanced Excel, Power Query, DAX, business dashboards and KPI reporting — the part of the syllabus that is employable before the rest of it finishes.
Python and SQL, properly
Modern Python with uv, OOP, type hinting, pytest, Ruff and Black, plus PostgreSQL with database design, window functions and query optimisation.
Data engineering and machine learning
Pandas 2.x, NumPy, Polars, DuckDB and PyArrow; scikit-learn pipelines and cross validation; XGBoost, LightGBM and CatBoost with hyperparameter optimisation.
Deep learning and computer vision
PyTorch from tensors up — neural networks, CNNs, transfer learning and OpenCV, then transformers, Hugging Face and tokenizers.
LLMs, RAG and AI agents
Tokenization, embeddings and attention; the OpenAI, Gemini, Claude and Grok APIs; five vector databases; RAG with hybrid search and guardrails; LangChain, LangGraph, CrewAI and MCP.
Cloud deployment and one capstone
Docker, AWS, Azure AI, Google Vertex AI and CI/CD with prompt injection defence and responsible AI — then a complete AI SaaS application on FastAPI and PostgreSQL.
One Fast Track From Spreadsheets to Shipped AI
- 16 modules from Excel and SQL through machine learning, deep learning, RAG and AI agents — ending in a deployed industry AI SaaS capstone.
- Fresher Data Analyst and AI roles in Punjab start around ₹16,000 – ₹28,000 a month for someone with a portfolio an employer can open.
Get certified in Data Science Program
Complete the course with a portfolio of live projects and receive an industry-recognised certificate, plus a documented internship letter accepted by Punjab universities.
Computer Education · JalandharCertificateof Project ExcellenceThis is to certify thatStudent Namehas designed, built and deployed a live capstone project in Data Science Program, reviewed and graded under industry mentorship.
Computer Education · JalandharCertificateof Course CompletionThis is to certify thatStudent Namehas successfully completed the professional training programme in Data Science Program with a grade of A+.
Two certificates on completion — the course certificate and a separate capstone project certificate.
Where this course takes you
The roles this opens, what they pay in Punjab and beyond, and who is hiring for them — the same figures our free Salary Estimator publishes, not a brochure number.
Build complete data pipelines from Excel and SQL through Python-based data engineering; train, tune and evaluate machine learning and deep learning models for real tasks; design and query vector databases for semantic search and RAG systems; build production LLM applications using LangChain, LangGraph, CrewAI and MCP; deploy AI applications with FastAPI, Docker, CI/CD and the major cloud AI platforms; apply AI security and responsible AI practices in production systems; and ship a full industry-level AI SaaS capstone with a professional GitHub portfolio.
Data Analyst, Data Scientist, Machine Learning Engineer, Deep Learning Engineer, LLM / AI Engineer, AI Agent Developer, AI Application Developer, Backend / API Developer and freelance AI consultant. Straight after 12th on a fast track, Data Analyst and AI Application Developer are the realistic first steps; the rest open as the portfolio grows.
A fresher with a deployed portfolio typically starts around ₹16,000 – ₹28,000 a month in the Jalandhar market. That rises quickly with experience, and candidates who can demonstrably ship a RAG system or an agent workflow move well beyond it, because far fewer applicants can show one.
Take this fast track if you want working AI skills inside a single term and a bit, are testing whether data suits you, or have a fixed window. Take the six-month certificate if you want more room on deep learning and computer vision — it adds object detection, OCR and image segmentation — and a whole month given to the capstone rather than a closing module.
No. Statistics and probability are taught at the level the work actually needs — distributions, evaluation metrics and what a result does and does not prove. The programme starts at Excel and Python fundamentals, so commerce and arts students sit in the same batch and finish the same capstone.
Yes, and this syllabus suits it unusually well. A dashboard, a forecast or a RAG assistant over a client's own documents are all billable independent work, and a Jalandhar address costs you nothing on a remote brief. You finish able to scope it, secure it, deploy it and document it.
Hands-on projects you will ship
Business KPI Dashboard
Month one's build: a real business dashboard in Power BI with Power Query transformations and DAX measures, reporting the KPIs a manager actually asks for.
SQL Data Service with FastAPI
A designed PostgreSQL schema with window functions and optimised queries, exposed through a JWT-authenticated FastAPI endpoint and tested in Postman.
End-to-End ML Pipeline
A messy real dataset cleaned and engineered in Pandas and Polars, explored with Plotly, then modelled through a scikit-learn pipeline and beaten with XGBoost, LightGBM and CatBoost.
Computer Vision Build
A PyTorch CNN with transfer learning and OpenCV, extended into transformers and Hugging Face — the project that makes deep learning concrete rather than theoretical.
RAG Assistant over Real Documents
Embeddings in a vector database with hybrid search and guardrails, answered by an LLM API, orchestrated with LangChain or LangGraph and wrapped in a Streamlit or Chainlit interface.
Industry AI SaaS Capstone
One complete application: FastAPI and PostgreSQL, RAG pipelines and AI agents, containerised with Docker, secured against prompt injection, deployed to the cloud with CI/CD and documented for review. This is the one interviewers ask about.
Learn it. Build it. Make it yours.
Every project moves through the same loop: understand the brief, build with guidance, then explain the decisions behind your work.
Understand
Break a real requirement into a clear plan and the right tools.
Business KPI DashboardBuild
Work hands-on with trainer feedback while the decisions are still easy to change.
SQL Data Service with FastAPIPresent
Turn the finished work into a portfolio story you can defend in an interview.
End-to-End ML PipelineWhy students choose techcadd
There are many places to learn data science in Jalandhar and the brochure syllabus looks similar at all of them. What differs is who teaches, whether the tooling is current, whether the LLM modules are more than a demo video, and whether anyone picks up the phone after you have paid. techcadd has trained students across Punjab since 2007 on the same model: small batches, working practitioners as trainers, live client work as coursework.
Trainers who still do the work
Your trainer is not a full-time lecturer. They deliver data and AI work for techcadd's services arm, so the examples in class are current rather than a case study from five years ago.
Written for a school leaver
Month one begins at Excel and Python fundamentals. Nothing is assumed, and nothing is skipped on the assumption that a degree will fill the gap later.
Classical and LLM in one programme
Gradient boosting and vector databases, scikit-learn pipelines and LangGraph agents, taught by the same trainer — because that is how the job is now advertised.
Current tooling, not legacy habits
uv, Ruff, Black and pytest from month one; Polars and DuckDB alongside Pandas; PyTorch and Hugging Face for deep learning. You learn the stack a 2026 team actually runs.
AI on your own API keys
OpenAI, Gemini, Claude and Grok through real API calls, plus Ollama and LiteLLM for local and routed models — with cost, context limits and failure handling met head on.
Honest about what a fast track buys
We will tell you plainly whether the four-month or the six-month track fits your goal. A student sold the wrong length is a student who does not finish.
What our students say
Real experiences from learners across Jalandhar and the districts around it.
“I did this in the gap before college. Having a deployed AI app on GitHub before my first semester started changed how everyone treated me.”
ABAyush BansalAfter 12th Student · Jalandhar “The Power BI work paid off before the course even ended — I was doing reporting part-time by the time we reached machine learning.”
MKMeher KaurJunior Data Analyst · Phagwara “Month four is what my interview was about. Nobody else applying could explain hybrid search or why guardrails matter.”
STShivam ThakurAI Application Developer · Ludhiana “I did this in the gap before college. Having a deployed AI app on GitHub before my first semester started changed how everyone treated me.”
ABAyush BansalAfter 12th Student · Jalandhar “The Power BI work paid off before the course even ended — I was doing reporting part-time by the time we reached machine learning.”
MKMeher KaurJunior Data Analyst · Phagwara “Month four is what my interview was about. Nobody else applying could explain hybrid search or why guardrails matter.”
STShivam ThakurAI Application Developer · Ludhiana
“I came from commerce and was sure the maths would stop me. It never did — statistics came in only as much as the models actually needed.”
NANandini AroraB.Sc Student · Kapurthala “A client wanted a chatbot over their own PDFs. I had already built exactly that, with a vector database and a Streamlit front end.”
KSKabir SethiFreelancer · Hoshiarpur “They were straight that a fast track would not make me a senior data scientist. That honesty is why I trusted the rest of what they said.”
PMPrerna MalhotraAfter 12th Student · Nakodar “I came from commerce and was sure the maths would stop me. It never did — statistics came in only as much as the models actually needed.”
NANandini AroraB.Sc Student · Kapurthala “A client wanted a chatbot over their own PDFs. I had already built exactly that, with a vector database and a Streamlit front end.”
KSKabir SethiFreelancer · Hoshiarpur “They were straight that a fast track would not make me a senior data scientist. That honesty is why I trusted the rest of what they said.”
PMPrerna MalhotraAfter 12th Student · Nakodar
Frequently asked questions
Find answers to the questions students ask before enrolling.
Four months, covering 16 modules: data and programming foundations; data science, machine learning and deep learning; deep learning, LLMs and vector search; then RAG, agents, deployment and the industry capstone. Weekday, evening and weekend batches cover the same syllabus, and 1-on-1 training is available. Every class runs for 2 hours.
Yes — that is who it is written for. Month one begins with Excel, Power Query and Power BI, then teaches Python from the ground up including the engineering practice most self-taught learners miss: virtual environments with uv, type hinting, pytest, Ruff and Black. The pace is brisk, so consistent attendance matters more here than on the six-month track.
No. Nothing in the syllabus requires school physics or higher mathematics. Statistics and probability are covered at the depth the modelling needs, and students from all three streams sit in the same batch.
Both. Months one and two are the classical pipeline — Excel and Power BI, Python, SQL, data engineering, machine learning, gradient boosting and deep learning. Months three and four are the LLM stack: tokenization and embeddings, prompt engineering, the OpenAI, Gemini, Claude and Grok APIs, vector databases, RAG architecture, LangChain, LangGraph, CrewAI, MCP, AI agents, FastAPI applications and cloud deployment.
Both cover the classical pipeline and the LLM stack, but the six-month certificate has more room: object detection, OCR, image segmentation and Vision Transformers in its deep learning month, a fuller treatment of production data tooling, and a whole month dedicated to the capstone rather than a closing module. This fast track compresses to the essentials and reaches the capstone sooner.
FAISS, ChromaDB, Pinecone, Qdrant and Milvus for vector search. GitHub Copilot as a coding assistant from month one. The OpenAI, Gemini, Claude and Grok APIs with Ollama for local models and LiteLLM for routing, then LangChain, LangGraph, CrewAI and the Model Context Protocol for orchestration. Real keys, real costs, real rate limits.
Every module produces something a trainer reviews, and six are substantial portfolio pieces: a Power BI KPI dashboard, a JWT-secured FastAPI data service over PostgreSQL, an end-to-end ML pipeline with tuned gradient boosting, a PyTorch computer vision build, a RAG assistant over real documents, and the industry AI SaaS capstone.
A complete industry-level AI SaaS application integrating FastAPI, PostgreSQL, RAG pipelines, AI agents and Docker containerisation, with full cloud deployment on AWS, Azure AI or Google Vertex AI — delivered with documentation and a professional GitHub portfolio.
Yes, in module 15. Student LLM apps are exactly where prompt injection and leaked API keys show up, so prompt injection defence, responsible AI practice and CI/CD are covered before the capstone is deployed.
In Jalandhar, shorter fast-track programmes typically cost less than the six-month certificate tracks, which run in the ₹18,000 to ₹40,000 range. techcadd counsellors share the current fee sheet and EMI options on request, and a demo class is free.
No institute can honestly guarantee a job, and you should be cautious of any in Jalandhar that does. techcadd guarantees placement support: resume and portfolio guidance, CV reviews, mock interviews and repeated drives with hiring partners across Jalandhar and Ludhiana, continued after a rejection rather than abandoned.
Yes — a course completion certificate, plus a documented internship letter based on live work. Alongside them you leave with a professional GitHub portfolio and a deployed capstone application, which is what an interviewer actually opens.
Yes. techcadd Jalandhar runs weekday, evening and weekend batches in parallel so college students and working people can both attend, and 1-on-1 training is available for a fully personal schedule. Every class runs for 2 hours; book a free demo class to see the lab and meet the trainer before enrolling.
Ask about Data Science Program
Send your question and a counsellor will call you back about batch timings, fees, EMI options, placement record, or whether this course fits your degree.
- info@techcadd.com
- Phone
- +91 98881 22254
- Address
- 2nd Floor, Crystal Plaza, SCS 78, Opposite PIMS Hospital, Jalandhar, Punjab 144001
- Counselling hours
- Monday – Saturday, 9:00 AM – 7:00 PM
Not sure if Data Science Program is the right fit?
One call with a counsellor is usually enough to find out. Book a free demo class and see the lab before you decide.