Best Data Science Certificate Program in Jalandhar
A six-month, project-driven path from data fundamentals to production-ready AI systems — Python, data engineering, machine learning, deep learning, LLMs, RAG, AI agents and full cloud deployment, across 24 modules and one industry capstone.
- Live client projects
- Practitioner trainers
- Placement support
- Certificate + internship
- Duration
- 6 Months
- Syllabus
- 24 Modules, 2026 Edition
- Mode
- Classroom, Weekend & 1-on-1
- Includes
- Internship Letter
- Students trained
- 25,000+Students trainedsince 2007
- Google rating
- 4.9★Google rating556+ reviews
- Practical training
- 100%Practical traininglive client work
Course overview
techcadd's Data Science Mastery Program in Jalandhar is a six-month, project-driven path from data fundamentals to production-ready AI systems. It is the 2026 edition of the syllabus, which means the classical data science pipeline and the LLM stack are taught as one job rather than two courses: Excel and Power BI, Python, SQL and data engineering, machine learning and deep learning, then LLMs, vector search, RAG, AI agents and cloud deployment.
Month one is data and programming foundations — advanced Excel, Power Query, Power BI and DAX; modern Python with uv, virtual environments, OOP, logging, type hinting, pytest, Ruff and Black; Git, GitHub and AI coding tools; then SQL on PostgreSQL with window functions, query optimisation, APIs, JSON and FastAPI basics with JWT authentication. Month two is data engineering and machine learning: Pandas 2.x, NumPy, Polars, DuckDB and PyArrow, then cleaning, feature engineering, EDA, interactive visualisation with Plotly and Streamlit, statistics and probability, scikit-learn pipelines and cross validation, and gradient boosting with XGBoost, LightGBM and CatBoost.
Month three moves into deep learning and computer vision with PyTorch, tensors, neural networks, CNNs and transfer learning, OpenCV, YOLO, OCR, image segmentation, Vision Transformers and Hugging Face. Month four opens the LLM stack: tokenization, embeddings, context windows and attention; 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 five is where those pieces become applications — RAG architecture with hybrid search, re-ranking, evaluation and guardrails; LangChain, LangGraph, CrewAI and the Model Context Protocol with tool calling and structured outputs; multi-agent systems and enterprise agent design; and FastAPI advanced, async, background tasks, WebSockets, Streamlit, Gradio and Chainlit. Month six is deployment and the capstone: Docker and Docker Compose, Linux, Nginx, AWS, Azure AI and Google Vertex AI, AI security including prompt injection and jailbreak defence, secret management, responsible AI and CI/CD with GitHub Actions — and then a complete industry-level AI SaaS application built with FastAPI, PostgreSQL, RAG pipelines, AI agents, containerisation and full cloud deployment, delivered with documentation and a professional GitHub repository.
Industry-Ready Training in Data Science
- 100% practical, project-based learning
- AI tools integrated into every module
- Live client projects under trainer supervision
- Internship letter and placement support
- Small batches with daily doubt clearing
Who can dothis course
The programme is built for people at six different starting points, and the batch is deliberately mixed. Month one starts at Excel and Python fundamentals with nothing assumed — but this is six months of steady building, and what decides your outcome is finishing what each module asks for.
Students after 12th
Join from any stream. Month one teaches Excel, Power BI and Python from the ground up, and most students run the programme alongside a degree at a Jalandhar college using the weekday or weekend batch.
Graduates and final-year students
If you are finishing a BCA, B.Sc, B.Com, BBA or B.Tech, this is the shortest route from degree to a data role. You enter placement season with a capstone AI SaaS app on GitHub rather than a blank CV.
Working professionals
The weekend batch exists for people already earning. Analysts and MIS executives who already live in Excel usually find month one familiar and move quickly into the Python, ML and LLM work from month two onward.
Business owners and freelancers
Owners take this to stop outsourcing analysis they cannot judge. Freelancers take it for the month four to six stack — RAG assistants, AI agents and deployed FastAPI apps are the highest-paid brief an independent developer can take right now.
Career restarters
A gap on the CV counts for less than work someone can open. You leave with a documented internship letter, a professional GitHub portfolio and a deployed AI application to walk an interviewer through.
Self-taught learners
If scattered tutorials left you with notebooks but nothing shipped, what changes here is a six-month calendar, a trainer who reviews your code each week, and a capstone with a deadline attached to it.
Why this programmeis worth your year
Every mid-size company in Punjab now sits on data it cannot read, which is exactly the gap this fills. That gap is the whole argument for the classical half of this programme: there is local demand, there are budgets, and there are very few trained people to hand the work to.
The second half exists because the job has moved. A data scientist in 2026 is expected to know embeddings and vector search as well as regression and gradient boosting, and to be able to put a RAG assistant or an agent workflow in front of a business rather than hand over a notebook. That is why months four to six are given entirely to LLM fundamentals, vector databases, RAG architecture, LangChain, LangGraph, CrewAI, MCP, FastAPI applications and cloud deployment — the pieces most data science programmes in Jalandhar still do not carry.
The programme is deliberately built on current tooling rather than what was standard five years ago: uv and Ruff rather than bare pip and lint-as-an-afterthought, Polars and DuckDB alongside Pandas, PyTorch and Hugging Face for deep learning, and GitHub Copilot and Cursor treated as instruments you learn to use with judgement. A syllabus that has not moved since 2020 is easy to spot — ask what a programme teaches for vector search and see what answer you get.
What separates this from a playlist of tutorials is supervision on real work. Every module produces something a trainer reads: a dashboard, a cleaned pipeline, a tuned model with an evaluation report, a working RAG endpoint. From month five you are building applications rather than notebooks, and by month six the whole batch is shipping to a cloud environment. No employer in Jalandhar will take your word for it without work they can open.
Be realistic about the money. A fresher who finishes with a deployed portfolio typically starts around ₹20,000 – ₹38,000 a month locally, and moves up quickly with experience. The roles this programme is written against are 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. The ceiling is high, but it is earned — nobody pays a beginner well for an enrolment alone.
Students reach the Jalandhar centre from Model Town, Urban Estate, Adarsh Nagar, Basti Bawa Khel and Rama Mandi, with weekend students travelling in from Phagwara, Kapurthala, Nakodar, Hoshiarpur and Adampur. Whether you have just finished 12th, are completing a degree at a local college, or are switching from a non-technical job, month one starts at zero, which is why weekday, evening, weekend and 1-on-1 timings all exist rather than a single fixed slot, with every class running two hours.
AI-Integrated Data Science Is Powering the Next Generation of Industry Leaders
- One programme covering the whole job — Excel and SQL through machine learning, deep learning, LLMs, RAG, agents and cloud deployment.
- Data Scientist and AI Engineer roles in Punjab start around ₹20,000 – ₹38,000 a month for a fresher with a deployed portfolio.
Reviewed by mentors. Built for interviews.What you will
actually build
The syllabus is a six-month calendar of 24 topics, four per month, and every topic produces something a trainer reads rather than a set of notes. Months 1–2 build the data and machine learning foundation, month 3 covers deep learning and computer vision, months 4–5 open the LLM, RAG and agent stack and turn it into applications, and month 6 is deployment, AI security and the industry capstone. Topics run in the order a real project runs: get the data, model it, make it intelligent, then ship it and defend it.
Data & Programming Foundations
- Module 01 · Excel, Power BI & Data Literacy — Excel Advanced, Power Query, DAX, business dashboards, KPI reporting, AI productivity
- Module 02 · Python Fundamentals & Engineering Practices — VS Code, uv, virtual environments, OOP, exception handling, logging, type hinting, pytest, Ruff, Black
- Module 03 · Git, GitHub & AI Coding Tools — Git Flow, GitHub Copilot, Cursor AI, Windsurf IDE
- Module 04 · SQL, Database Design & APIs — PostgreSQL, database design, window functions, query optimisation, APIs, JSON, FastAPI basics, JWT, Postman
One course.A mesh of real tools.
Everything below is installed on the lab machines and used on live client work, not shown once in a slide and forgotten.
Git & GitHubGet Certified in
Data Science
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, 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 with a grade of A+.
Two certificates on completion — the course certificate and a separate capstone project certificate.
Where this coursetakes 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.
What will I actually be able to do at the end?
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; build production LLM applications with 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 practice in production; and ship a full industry-level AI SaaS capstone with a professional GitHub portfolio.
What job roles open up after this programme?
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. The first three are the volume roles in Punjab today; the LLM and agent titles are where the shortage — and the premium — currently sits.
What can I earn, and how fast does it grow?
A fresher with a deployed portfolio starts around ₹20,000 – ₹38,000 a month in the Jalandhar market. With two years of delivery experience that typically doubles, and candidates who can demonstrably ship a RAG system or an agent workflow move well beyond it, because far fewer applicants can show one.
Do I need a maths or statistics degree?
No. Statistics and probability are taught in month two at the level the work actually needs — distributions, sampling, evaluation metrics and what a result does and does not prove. Month one starts at Excel and Python fundamentals, so an arts or commerce background is no obstacle; consistency through the six months is.
Can I freelance or work remotely with this skill?
Yes, and this syllabus is unusually well suited to it. A Jalandhar address costs nothing on a remote brief, and a deployed RAG assistant or agent workflow is the single most requested independent build right now. You finish able to scope it, secure it, deploy it and document it.
Which industries hire for this in Punjab?
Beyond IT and analytics firms, the export houses, sports goods and hand tool manufacturers, hospitals, immigration consultancies, schools and real estate groups across Jalandhar and Ludhiana now hire directly for reporting, forecasting and — increasingly — for internal AI assistants built on their own documents.
Hands-on projectsyou 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 normalised 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 — with the evaluation to prove it.
Computer Vision Build
A PyTorch CNN with transfer learning, extended into object detection and OCR with YOLO and OpenCV — the project that makes deep learning concrete rather than theoretical.
RAG Assistant over Real Documents
Embeddings in a vector database with hybrid search, re-ranking and guardrails, answered by an LLM API and evaluated for hallucination — then wrapped in a Streamlit or Chainlit interface.
Industry AI SaaS Capstone
The whole of month six on one application: FastAPI and PostgreSQL, RAG pipelines and AI agents, containerised with Docker, secured against prompt injection, deployed to the cloud with GitHub Actions 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 choosetechcadd
There are many places to learn this 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.
Classical and LLM in one programme
Gradient boosting and vector databases, scikit-learn pipelines and LangGraph agents, taught in the same six months 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, DuckDB and PyArrow 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 rather than skipped.
A capstone worth a month
Month six is one industry-level AI SaaS build: FastAPI, PostgreSQL, RAG pipelines, agents, Docker and cloud deployment, delivered with documentation, code review and a managed GitHub repository.
A placement cell that persists
Resume and portfolio guidance built into the programme, mock interviews and CV reviews, and drives with hiring partners across Jalandhar and Ludhiana — repeated after a rejection, not abandoned.
How techcaddcompares
The module headings look similar everywhere. These are the rows where a data science programme in Jalandhar is actually decided.
| Feature | techcadd | Other institutes |
|---|---|---|
| LLMs and RAG | Three of the six months: tokenization, embeddings and attention, prompt engineering, the OpenAI, Gemini, Claude and Grok APIs, five vector databases, RAG with hybrid search, re-ranking, evaluation and guardrails. | A closing 'Introduction to Generative AI' session, usually one demo of ChatGPT. |
| AI agents | LangChain, LangGraph, CrewAI and MCP with tool calling, structured outputs, multi-agent systems and enterprise agent design. | Not covered — most syllabuses stop at a single-prompt chatbot. |
| Data engineering | Pandas 2.x with Polars, DuckDB and PyArrow, plus SQL window functions and query optimisation on PostgreSQL. | Pandas alone, on clean CSV files that never resemble production data. |
| Deep learning | PyTorch from tensors up: CNNs, transfer learning, OpenCV, YOLO, OCR, segmentation, Vision Transformers and Hugging Face. | A theory week on neural networks, with no model anyone actually trained. |
| Engineering practice | uv, virtual environments, type hinting, logging, pytest, Ruff and Black from month one, with Git Flow and code review standards. | Notebooks only — no tests, no version control, nothing another developer could run. |
| Deployment | FastAPI with Docker and Docker Compose behind Nginx, deployed to AWS, Azure AI or Google Vertex AI, with GitHub Actions CI/CD. | A model saved to disk, and a promise that deployment is 'a devops job'. |
| AI security | Prompt injection and jailbreak defence, secret management and responsible AI, taught as its own module before the capstone ships. | Absent entirely, which is why so many student LLM apps leak their API keys. |
| Capstone | A full month on one industry AI SaaS application, delivered with documentation, code review, a managed GitHub repository and a live cloud deployment. | A final-week notebook assembled from the trainer's own template. |
Rows describe what is commonly on offer in the market, not any one named institute. Ask any institute — including this one — to show you a student's deployed capstone and GitHub repository before you pay.
What our studentsin Jalandhar say
- Google
I was switching careers and worried I would be behind. Half the Data Science batch were doing the same thing, and nobody made me feel slow.
Harpreet KaurSoftware Trainee · Jalandhar GoogleThe Power BI dashboard from month one got me a part-time reporting job before I had even reached the machine learning modules.
Rohit SharmaJunior Analyst · Phagwara GoogleMonth five changed what I could charge. A client wanted a chatbot over their own PDFs and I had already built exactly that with a vector database and re-ranking.
Simranjeet SinghFreelancer · Kapurthala GoogleI was switching careers and worried I would be behind. Half the Data Science batch were doing the same thing, and nobody made me feel slow.
Harpreet KaurSoftware Trainee · Jalandhar GoogleThe Power BI dashboard from month one got me a part-time reporting job before I had even reached the machine learning modules.
Rohit SharmaJunior Analyst · Phagwara GoogleMonth five changed what I could charge. A client wanted a chatbot over their own PDFs and I had already built exactly that with a vector database and re-ranking.
Simranjeet SinghFreelancer · Kapurthala
- Google
The Data Science programme got me interview-ready faster than I expected. My interviewer asked to see my project and that was the whole conversation.
Anjali VermaCareer Switcher · Jalandhar Cantt GoogleNobody else in my placement batch could explain what an embedding actually is, let alone show a deployed FastAPI app that used one.
Karan MehtaB.Tech Student · Jalandhar Googletechcadd's placement cell kept calling me for drives until I was placed. That persistence mattered more than anything else for Data Science.
Sandeep KaurPlaced Fresher · Phillaur GoogleThe Data Science programme got me interview-ready faster than I expected. My interviewer asked to see my project and that was the whole conversation.
Anjali VermaCareer Switcher · Jalandhar Cantt GoogleNobody else in my placement batch could explain what an embedding actually is, let alone show a deployed FastAPI app that used one.
Karan MehtaB.Tech Student · Jalandhar Googletechcadd's placement cell kept calling me for drives until I was placed. That persistence mattered more than anything else for Data Science.
Sandeep KaurPlaced Fresher · Phillaur
Choose howyou want to learn
Every mode covers the same syllabus, projects and placement support. Pick the schedule that fits your life.
Weekday Classroom
Two hours a day at the Jalandhar centre, on lab machines with your trainer reading your notebooks and code in the room.
Learn moreWeekend Batch
For people already working. Same six months, same 24 modules, same capstone — delivered across Saturday and Sunday sessions.
Learn moreLive Online
Live classes, not recordings, with the same weekly review of your repository and deployed endpoints. For students outside Jalandhar.
Learn more1-on-1 Training
Your own pace through the same syllabus — useful if you already work in analytics and want to start at the machine learning or LLM months.
Learn moreFrequently Asked Questions
The Data Science Mastery Program runs for 6 months across 24 modules — data and programming foundations, data engineering and machine learning, deep learning and computer vision, LLM fundamentals and vector search, RAG and AI agents, then deployment and the industry capstone. Weekday, evening and weekend batches cover the same syllabus, and 1-on-1 training is available if you would rather set your own pace. Every class runs for 2 hours.
Both, and that is the point of the 2026 edition. Months 1 to 3 are the classical pipeline — Excel and Power BI, Python, SQL, data engineering, machine learning, gradient boosting, deep learning and computer vision. Months 4 to 6 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.
In Jalandhar, comprehensive 6-month programmes with live projects, an internship and placement support typically run in the ₹18,000 to ₹40,000 range, with AI-integrated tracks at the upper end. techcadd counsellors share the current fee sheet and EMI options on request, and a demo class is free.
Students after 12th, graduates and final-year students, working professionals switching into data roles, and business owners all join this programme. Month one starts at Excel and Python fundamentals, so a technical background helps but is not required.
No. Python is taught from the ground up in month one, including the engineering practices most self-taught learners miss — virtual environments with uv, type hinting, logging, pytest, Ruff and Black. Statistics and probability are covered in month two at the depth the modelling work needs.
GitHub Copilot, Cursor AI and Windsurf as coding assistants from month one. Then the OpenAI, Gemini, Claude and Grok APIs, with Ollama for local models and LiteLLM for routing, in month four. LangChain, LangGraph, CrewAI and the Model Context Protocol in month five. Real keys, real costs, real rate limits — not a recorded demonstration.
FAISS, ChromaDB, Pinecone, Qdrant and Milvus, alongside the embedding and semantic search theory in module 16 — then applied in month five through RAG architecture with hybrid search, re-ranking, evaluation and guardrails.
Every module produces something a trainer reviews, and six of them 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 with YOLO and OCR, a RAG assistant over real documents, and the month-six industry AI SaaS capstone.
A complete industry-level AI SaaS application built over the final month — FastAPI and PostgreSQL, RAG pipelines, AI agents, Docker containerisation and full cloud deployment on AWS, Azure AI or Google Vertex AI. Module 24 covers the delivery standard: project documentation, code review practice, GitHub repository management and industry best practice.
It is module 22, and it is there because student LLM apps are exactly where prompt injection, leaked API keys and jailbreaks show up. You cover prompt injection and jailbreak defence, secret management, responsible AI practice and CI/CD with GitHub Actions before the capstone is deployed.
No training provider can honestly guarantee a job, and you should be cautious of anyone in Jalandhar who claims one. techcadd guarantees placement support: resume and portfolio guidance built into the programme, CV reviews, mock interviews and repeated drives with hiring partners across Jalandhar and Ludhiana.
Yes. Every student receives a programme completion certificate plus a documented internship letter based on live work. The internship satisfies the industrial training requirement at most Punjab universities, and your GitHub portfolio and deployed capstone are what you show alongside it.
Yes. techcadd Jalandhar runs weekday, evening and weekend batches in parallel so working professionals and college students can both attend, and 1-on-1 training is available for a fully personal schedule. Every class (batch or 1-on-1) runs for 2 hours; book a free demo class to see the lab and meet the trainer before enrolling.
How the programme is staged
One programme, taught in stages. Each stage begins with practical foundations and the next continues where it ends, so nothing is repeated and nothing is skipped.
Months 1 – 2
Opens with Module 01 · Excel, Power BI & Data Literacy through Module 08 · Gradient Boosting & Model Tuning. 8 capabilities, taught, practised in the lab and assessed on work you keep.
Months 3 – 4
Adds Module 09 · Deep Learning Fundamentals through Module 16 · Embeddings & Vector Databases, on top of everything stage 1 already covers.
Months 5 – 6
Adds Module 17 · RAG Architecture through Module 24 · Capstone Delivery & Standards, on top of everything stage 2 already covers.
Every module,stage by stage
The Data Science syllabus runs as 3 stages inside one enrolment. A tick shows the stage each capability first appears in — the ladder is cumulative, so a later stage builds on the earlier ones instead of replacing them.
Months 1 – 2
8 capabilities
Opens with Module 01 · Excel, Power BI & Data Literacy through Module 08 · Gradient Boosting & Model Tuning. 8 capabilities, taught, practised in the lab and assessed on work you keep.
Months 3 – 4
+8 capabilities
Adds Module 09 · Deep Learning Fundamentals through Module 16 · Embeddings & Vector Databases, on top of everything stage 1 already covers.
Months 5 – 6
+8 capabilities
Adds Module 17 · RAG Architecture through Module 24 · Capstone Delivery & Standards, on top of everything stage 2 already covers.
| Module | Stage 1 | Stage 2 | Stage 3 |
|---|---|---|---|
| 01Module 01 · Excel, Power BI & Data LiteracyExcel Advanced, Power Query, DAX, business dashboards, KPI reporting, AI productivity. | Included | Included | Included |
| 02Module 02 · Python Fundamentals & Engineering PracticesVS Code, uv, virtual environments, OOP, exception handling, logging, type hinting, pytest, Ruff, Black. | Included | Included | Included |
| 03Module 03 · Git, GitHub & AI Coding ToolsGit Flow, GitHub Copilot, Cursor AI, Windsurf IDE. | Included | Included | Included |
| 04Module 04 · SQL, Database Design & APIsPostgreSQL, database design, window functions, query optimisation, APIs, JSON, FastAPI basics, JWT, Postman. | Included | Included | Included |
| 05Module 05 · Data Engineering FundamentalsPandas 2.x, NumPy, Polars, DuckDB, PyArrow. | Included | Included | Included |
| 06Module 06 · EDA, Visualization & Statisticsdata cleaning, feature engineering, Plotly and Streamlit, statistics, probability, feature selection, preprocessing. | Included | Included | Included |
| 07Module 07 · Machine Learning Foundationsscikit-learn, pipelines, cross validation. | Included | Included | Included |
| 08Module 08 · Gradient Boosting & Model TuningXGBoost, LightGBM, CatBoost, model evaluation, hyperparameter optimisation. | Included | Included | Included |
| 09Module 09 · Deep Learning FundamentalsPyTorch, tensor operations, neural networks. | Not included | Included | Included |
| 10Module 10 · CNNs & Transfer LearningCNNs, transfer learning, computer vision, OpenCV. | Not included | Included | Included |
| 11Module 11 · Object Detection & OCRYOLO, OCR, object detection, image segmentation, Vision Transformers. | Not included | Included | Included |
| 12Module 12 · Transformers & Hugging Facetransformers, tokenizers, the Model Hub. | Not included | Included | Included |
| 13Module 13 · LLM Fundamentalstokenization, embeddings, context windows, attention mechanism. | Not included | Included | Included |
| 14Module 14 · Prompt Engineeringprompt optimisation, system prompts, structured prompting. | Not included | Included | Included |
| 15Module 15 · LLM APIs & Model AccessOpenAI, Gemini, Claude and Grok APIs, Ollama, LiteLLM. | Not included | Included | Included |
| 16Module 16 · Embeddings & Vector DatabasesFAISS, ChromaDB, Pinecone, Qdrant, Milvus, semantic search. | Not included | Included | Included |
| 17Module 17 · RAG Architecturehybrid search, re-ranking, evaluation, guardrails. | Not included | Not included | Included |
| 18Module 18 · LangChain, MCP & Tool CallingLangGraph, prompt templates, chains, memory, CrewAI, Model Context Protocol, function calling, structured outputs. | Not included | Not included | Included |
| 19Module 19 · AI Agents & Multi-Agent Systemsautonomous workflows, enterprise agent design. | Not included | Not included | Included |
| 20Module 20 · AI Application DevelopmentFastAPI advanced, async, background tasks, WebSockets, Streamlit, Gradio, Chainlit. | Not included | Not included | Included |
| 21Module 21 · Containerization & Cloud DeploymentDocker, Docker Compose, Linux, Nginx, reverse proxy, AWS, Azure AI, Google Vertex AI, serverless AI. | Not included | Not included | Included |
| 22Module 22 · AI Security & CI/CDprompt injection, jailbreak defence, secret management, responsible AI, GitHub Actions, automated deployment. | Not included | Not included | Included |
| 23Module 23 · Industry Capstone Buildan end-to-end AI SaaS with FastAPI, PostgreSQL, RAG, agents, Docker and cloud deployment. | Not included | Not included | Included |
| 24Module 24 · Capstone Delivery & Standardsproject documentation, code review, GitHub repository management, industry standards and best practices. | Not included | Not included | Included |
Every stage sits inside the one programme: nothing is dropped as you move up, and nothing is charged for twice. If you are not sure which stage matters most for the role you are aiming at, a counsellor will map it against the jobs actually hiring in Punjab right now.
Not sure if Data Science 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.
Ask aboutData Science
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.
- Free counselling and demo class
- Weekday, evening, weekend or 1-on-1, all 2-hour classes
- Internship letter and placement support