01
Students After 12th
Students from different academic streams can begin with the fundamentals and gradually progress into Python, SQL, machine learning and AI.
Start right after school
Best After 12th 6-Month Data Science Certificate Program in Mohali
Start your data science journey after 12th with a structured six-month programme that takes you from data analysis and Python fundamentals to machine learning, deep learning, generative AI, RAG systems, AI agents and cloud deployment.
Instead of learning disconnected tools, you work through a progressive project-based curriculum where every stage adds another practical skill to your portfolio.
0113
01Where this starts
Course Overview
Month 1 — Build Your Data & Programming Foundation. Begin with Advanced Excel, Power Query, Power BI and DAX to understand business reporting and dashboards. Python is then introduced from the fundamentals using VS Code, virtual environments, object-oriented programming, exception handling, logging, type hints and testing.
You also learn Git, GitHub and modern AI-assisted coding workflows before moving into PostgreSQL, database design, SQL queries, window functions, optimisation, APIs, JSON, FastAPI fundamentals and JWT authentication.
Month 2 — Data Engineering & Machine Learning. Work with Pandas, NumPy, Polars, DuckDB and PyArrow to clean, transform and analyse datasets. You learn exploratory data analysis, feature engineering, statistics, probability and interactive visualisation using Plotly and Streamlit.
The machine-learning section introduces scikit-learn pipelines, preprocessing, cross-validation and model evaluation, followed by practical work with XGBoost, LightGBM and CatBoost and hyperparameter optimisation.
Month 3 — Deep Learning & Computer Vision. Move beyond traditional machine learning into neural networks using PyTorch. Learn tensors, model architecture, CNNs and transfer learning before applying computer vision techniques with OpenCV.
The module also covers YOLO, object detection, OCR, image segmentation, Vision Transformers and Hugging Face, giving you exposure to modern computer-vision workflows.
Month 4 — LLMs & Vector Search. Understand how modern large language models work, including tokenization, embeddings, context windows and attention mechanisms. Learn prompt engineering and structured prompting while working with APIs such as OpenAI, Gemini, Claude and Grok, together with Ollama and LiteLLM.
The module then introduces embeddings and vector search through technologies such as FAISS, ChromaDB, Pinecone, Qdrant and Milvus.
Month 5 — RAG, AI Agents & Applications. Turn your LLM knowledge into usable applications. Learn RAG architecture, document processing, hybrid retrieval, re-ranking, evaluation and guardrails. Work with LangChain, LangGraph, CrewAI and Model Context Protocol (MCP) for tool calling, structured outputs and agent workflows.
Application development includes advanced FastAPI, asynchronous programming, background tasks, WebSockets, Streamlit, Gradio and Chainlit.
Month 6 — Deployment, Security & Capstone. Learn how to move AI applications beyond the development environment. The final module covers Docker, Docker Compose, Linux, Nginx, AWS, Azure AI and Google Vertex AI, together with reverse proxies and cloud deployment approaches.
You also explore AI security topics such as prompt injection, jailbreak risks, secret management and responsible AI, followed by CI/CD using GitHub Actions. The programme concludes with an end-to-end AI application combining FastAPI, PostgreSQL, RAG and AI agents, documented and prepared as a professional portfolio project.
Where it takes you
- Data Analyst
- Junior Data Analyst
- Business Intelligence Analyst
- Python Developer
- Junior Machine Learning Engineer
- Machine Learning Developer
- Data Science Trainee
- AI Developer
- Generative AI Developer
- RAG Application Developer
- AI Automation Developer
- Junior AI Engineer
- Data & AI Intern
- Freelance Data/AI Developer
02Skills you collect
What You'll Learn
The focus is on creating demonstrable work rather than simply completing theoretical lessons.
Business Data Dashboard
Build a Power BI dashboard using Excel, Power Query and DAX. Work with KPIs, transformations and business reporting so your first project already resembles a workplace deliverable.
Machine Learning Pipeline
Prepare a real-world dataset, perform exploratory analysis, engineer features and create a complete scikit-learn pipeline. Compare boosting models and document the evaluation results.
RAG-Based AI Assistant
Create a document-questioning system using embeddings and a vector database. Implement retrieval, re-ranking and response generation while considering evaluation and guardrails.
AI Application Capstone
Bring together backend development, databases, RAG, AI agents and deployment into one substantial application that can become a central part of your portfolio.
03The route
Course Curriculum
The six-month curriculum is divided into progressive stages. Each module connects with the previous one so students gradually move from data fundamentals to production-oriented AI development.
- Excel, Power BI & Data Literacy
- Advanced Excel, Power Query, Power BI, DAX, business dashboards, KPI reporting, AI productivity and data literacy.
- Python Fundamentals & Engineering Practices
- VS Code, uv package manager, virtual environments, Python fundamentals, OOP, exception handling, logging, type hints, pytest, Ruff and Black.
- Git, GitHub & AI Coding Tools
- Git, GitHub, Git Flow, GitHub Copilot, Cursor AI and Windsurf IDE.
- SQL, Database Design & APIs
- PostgreSQL, database design, SQL queries, window functions, query optimisation, APIs, JSON, FastAPI fundamentals, authentication, JWT and Postman.
Build Something at Every Stage
The strongest way to demonstrate technical learning is through completed work. Students can progressively move from a dashboard to a SQL service, a machine-learning pipeline, computer vision, a RAG assistant and an AI SaaS capstone. This gives your portfolio a clear story: you started with data fundamentals and gradually learned how to build complete AI-powered applications.
Outcome
The capstone runs the whole chain in one application — FastAPI and PostgreSQL behind RAG and AI agents, containerised, deployed and documented as a professional portfolio project.
04Your toolkit
Tools You Will Work With
The programme introduces a modern technical toolkit used across data analysis, machine learning and AI development.
ExcelPower QueryPower BIDAXPandasNumPyPolarsDuckDBPyArrowPythonVS CodeGitGitHubPostgreSQLFastAPIPostmanScikit-learnXGBoostLightGBMCatBoostPyTorchOpenCVYOLOOCRHugging FaceExcelPower QueryPower BIDAXPandasNumPyPolarsDuckDBPyArrowPythonVS CodeGitGitHubPostgreSQLFastAPIPostmanScikit-learnXGBoostLightGBMCatBoostPyTorchOpenCVYOLOOCRHugging FaceExcelPower QueryPower BIDAXPandasNumPyPolarsDuckDBPyArrowPythonVS CodeGitGitHubPostgreSQLFastAPIPostmanScikit-learnXGBoostLightGBMCatBoostPyTorchOpenCVYOLOOCRHugging Face
OpenAIGeminiClaudeGrokOllamaLiteLLMFAISSChromaDBPineconeQdrantMilvusLangChainLangGraphCrewAIMCPTool CallingMulti-Agent WorkflowsDockerDocker ComposeLinuxNginxAWSAzure AIGoogle Vertex AIGitHub ActionsOpenAIGeminiClaudeGrokOllamaLiteLLMFAISSChromaDBPineconeQdrantMilvusLangChainLangGraphCrewAIMCPTool CallingMulti-Agent WorkflowsDockerDocker ComposeLinuxNginxAWSAzure AIGoogle Vertex AIGitHub ActionsOpenAIGeminiClaudeGrokOllamaLiteLLMFAISSChromaDBPineconeQdrantMilvusLangChainLangGraphCrewAIMCPTool CallingMulti-Agent WorkflowsDockerDocker ComposeLinuxNginxAWSAzure AIGoogle Vertex AIGitHub Actions
- Excel
- Data & analytics — the first reporting surface.
- Power Query
- Data & analytics — repeatable cleaning and transformation.
- Power BI
- Data & analytics — business dashboards and KPI reporting.
- DAX
- Data & analytics — the measures behind those dashboards.
- Pandas
- Data & analytics — DataFrames, cleaning and analysis.
- NumPy
- Data & analytics — arrays and numeric work.
- Polars
- Data & analytics — fast DataFrames for larger datasets.
- DuckDB
- Data & analytics — analytical SQL straight over files.
- PyArrow
- Data & analytics — the columnar format underneath.
- Python
- Programming & development — the language the whole course runs on.
- VS Code
- Programming & development — the editor and environment.
- Git
- Programming & development — version control and Git Flow.
- GitHub
- Programming & development — where the portfolio lives.
- PostgreSQL
- Programming & development — the relational database.
- FastAPI
- Programming & development — the API layer, basic through advanced.
- Postman
- Programming & development — testing the APIs you build.
- Scikit-learn
- Machine learning — pipelines, training and evaluation.
- XGBoost
- Machine learning — gradient boosting.
- LightGBM
- Machine learning — the fast boosting alternative.
- CatBoost
- Machine learning — boosting with categorical features.
- PyTorch
- Deep learning & vision — tensors, neural networks and CNNs.
- OpenCV
- Deep learning & vision — image processing.
- YOLO
- Deep learning & vision — object detection.
- OCR
- Deep learning & vision — reading text out of images.
- Hugging Face
- Deep learning & vision — Transformers, tokenizers and the Model Hub.
- OpenAI
- Generative AI — the first LLM API you call.
- Gemini
- Generative AI — a second provider to compare against.
- Claude
- Generative AI — long-context reasoning work.
- Grok
- Generative AI — a further model API.
- Ollama
- Generative AI — running models locally.
- LiteLLM
- Generative AI — one interface across providers.
- FAISS
- RAG & vector search — local similarity search.
- ChromaDB
- RAG & vector search — an embedded vector store.
- Pinecone
- RAG & vector search — the hosted vector database.
- Qdrant
- RAG & vector search — filtering alongside vector search.
- Milvus
- RAG & vector search — vector search at scale.
- LangChain
- RAG & vector search — chains, prompts and memory.
- LangGraph
- RAG & vector search — stateful graph workflows.
- CrewAI
- AI agents — multi-agent workflows.
- MCP
- AI agents — Model Context Protocol for tools and resources.
- Tool Calling
- AI agents — how a model reaches your functions.
- Multi-Agent Workflows
- AI agents — autonomous, enterprise-oriented processes.
- Docker
- Deployment — containerising the application.
- Docker Compose
- Deployment — running the whole stack together.
- Linux
- Deployment — the server the application runs on.
- Nginx
- Deployment — reverse proxy in front of the app.
- AWS
- Deployment — cloud hosting and services.
- Azure AI
- Deployment — Microsoft's AI platform.
- Google Vertex AI
- Deployment — Google's managed AI platform.
- GitHub Actions
- Deployment — CI/CD pipelines.
05Is this you?
Who Can Join This Course?
02
Students Pursuing a Degree
Students studying BCA, B.Sc, BBA, B.Com or related programmes can use the course to add practical technology skills alongside their academic education.
03
Commerce & Arts Students
You do not need to begin as an advanced programmer. The curriculum introduces programming, statistics and machine learning progressively.
04
Students Looking for a Technical Skill
If you want a structured technology programme after 12th instead of learning isolated tools from different sources, the six-month format provides a clear learning path.
05
Career Switchers
Learners from non-technical backgrounds can build their foundation step by step before moving into advanced AI application development.
06
Self-Learners
If you have learned Python or AI through online tutorials but struggled to complete projects, the project-based structure can help you turn individual lessons into finished portfolio work.
06Why this one
Why This Programme Is Worth Your Time
01
Start With Employable Data Skills
Excel, Power Query, Power BI, DAX and SQL provide a practical foundation for working with business data before you move into advanced AI.
02
Learn Python for Real Development
Go beyond basic syntax with virtual environments, OOP, testing, logging, Git and development practices.
03
Cover the Complete Machine Learning Journey
Learn data preparation, EDA, feature engineering, model building, evaluation, cross-validation and boosting algorithms.
04
Move Into Modern AI
Explore LLMs, embeddings, prompt engineering, vector databases, RAG and AI agents instead of stopping at traditional data science.
05
Learn to Build AI Applications
FastAPI, Streamlit, Gradio, Chainlit, databases and API integrations help connect models to usable applications.
06
Understand Deployment & Security
Docker, cloud platforms, CI/CD and AI security topics prepare you to think beyond a local notebook.
07
Finish With a Major Capstone
The final project brings multiple technologies together into one portfolio-ready application.
07Why now
Data Science Is Expanding Into AI Engineering
Modern data roles increasingly overlap with machine learning, automation and generative AI.
A strong foundation therefore needs more than spreadsheets or isolated Python exercises. Students benefit from understanding the complete journey.
This programme is designed around that progression so you can build knowledge in stages rather than trying to learn every advanced AI concept on day one.
The complete journey, stage by stage
- DataMonth 1 — Excel, Power Query, Power BI and DAX.
- PythonMonth 1 — fundamentals, OOP, testing, logging and type hints.
- SQLMonth 1 — PostgreSQL, window functions and query optimisation.
- Machine LearningMonth 2 — scikit-learn pipelines and gradient boosting.
- Deep LearningMonth 3 — PyTorch, CNNs, transfer learning and computer vision.
- LLMsMonth 4 — tokenization, embeddings, prompting and model APIs.
- RAGMonth 5 — retrieval, hybrid search, re-ranking and guardrails.
- AI AgentsMonth 5 — LangGraph, CrewAI, MCP and multi-agent workflows.
- DeploymentMonth 6 — Docker, cloud platforms, AI security and CI/CD.
Talk to a Course Advisor
Ten minutes with the techcadd team settles eligibility, session timings, fees and where this leads — before you commit six months to it.
08Certification
Certification & Career Documentation
Students completing the programme can build a collection of project work and course documentation according to the current programme terms.
Course Completion DocumentationDocumentation confirming successful completion of the training programme, subject to techcadd's current certification policy.
Project DocumentationStructured project records that explain the work completed during the programme.
Portfolio ProjectsA collection of dashboards, data projects, machine-learning work and AI applications that can be presented during interviews.
Placement SupportCareer-oriented assistance such as resume preparation, portfolio guidance, interview practice and information about relevant opportunities, subject to current placement-support policies.
Capstone Defense
Fullscreen
Project Completion Certificate
ISO 9001:2015 Accredited
Fullscreen
Course Completion Certificate
Two documents on completion — the course certificate, and a separate certificate for the capstone you build and defend. Click any card to inspect in full screen.
09Where it leads
Where This Course Can Take You
The skills covered in the programme can support entry-level pathways across data, analytics, machine learning and AI development. Actual opportunities depend on your skills, portfolio, interview performance, experience and the requirements of individual employers.
Potential Job Roles
Clean, query and analyse business data, then report what it shows.
10What you build
Portfolio Projects
01
Business KPI Dashboard
Create a business reporting dashboard using Power BI, Power Query and DAX. Present important KPIs through a clean, interactive reporting interface. Technologies: Power BI · DAX · Power Query
02
SQL Data Service With FastAPI
Design a PostgreSQL database, write optimised queries and expose selected data through a FastAPI service with authentication. Technologies: PostgreSQL · FastAPI · JWT · Postman
03
End-to-End Machine Learning Pipeline
Take a raw dataset through cleaning, feature engineering, visual analysis and model development. Compare multiple algorithms and document the final evaluation. Technologies: Pandas · Polars · Scikit-learn · XGBoost
04
Computer Vision Application
Build a computer-vision solution using deep learning and image-processing techniques, with exposure to object detection and OCR. Technologies: PyTorch · OpenCV · YOLO
05
RAG Assistant
Create an AI assistant that can retrieve information from documents using embeddings and vector search before generating contextual responses. Technologies: LangChain · Vector Database · LLM APIs
06
Industry-Style AI SaaS Capstone
Develop a complete AI application combining backend APIs, PostgreSQL, RAG, AI agents, containerisation and deployment. Technologies: FastAPI · PostgreSQL · RAG · AI Agents · Docker · Cloud
11How it works
Learn It. Build It. Present It.
Every major project follows a practical three-stage workflow.
Understand
Study the requirement, identify the problem and select an appropriate technology stack.
Business KPI DashboardBuild
Develop the project step by step with practical implementation and trainer feedback.
SQL Data Service With FastAPIPresent
Explain your architecture, methodology, decisions and results so the project becomes something you can confidently discuss in an interview.
End-to-End Machine Learning Pipeline12Why techcadd
Why Choose techcadd for Data Science Training in Mohali?
A curriculum that builds in order, a project at every stage, and a stack that runs from a first spreadsheet to a deployed AI application.
01
Structured Learning Path
The curriculum progresses from beginner-level data concepts to advanced AI development rather than introducing complex tools without context.
02
Project-Focused Training
Projects are integrated throughout the programme so learners have opportunities to apply concepts immediately.
03
Beginner-Friendly Foundation
Students starting after 12th can first develop their programming and data fundamentals before moving into machine learning and AI.
04
Modern Technology Stack
The syllabus covers current technologies across Python, machine learning, deep learning, LLMs, RAG, AI agents, APIs, containers and cloud platforms.
05
Portfolio Development
Instead of finishing with only notes, students work toward dashboards, machine-learning applications and AI projects that can be organised into a portfolio.
06
Career Preparation
The programme can include resume, portfolio and interview preparation alongside technical learning and placement-support activities according to current techcadd policies.
13Who has walked it
Students who started where you are
Alumni of this route, on what changed once they were sitting in interviews.
4.6
280 reviews
577%
414%
35%
22%
12%
Running real ad budgets during the course was the difference. I walked into my first job already knowing how to read a campaign report and fix what was underperforming.
The cyber security lab setup let me break things safely and learn how attacks really work. Placement cell arranged three interviews within a month of finishing.
AutoCAD and SolidWorks were taught with actual production drawings, not textbook exercises. My employer noticed that my drawing sets followed proper standards from day one.
I joined the 6-month MERN track straight after B.Tech with almost no practical experience. The live project work is what changed things — I had real code to talk about in interviews instead of just a syllabus.
14Before you go
Frequently Asked Questions
Still unsure? A ten-minute call with a counsellor usually settles it faster than any brochure.
The programme is structured as a six-month learning path covering data analysis, Python, SQL, machine learning, deep learning, LLMs, RAG, AI agents and deployment. Current class schedules, session timings and total instructional hours should be confirmed with the Mohali centre.
15Final stage
Ask About Data Science Certificate Program in Mohali
Have questions about the syllabus, session timings, fees, projects or eligibility?
Speak with the course counselling team to understand whether this six-month Data Science programme matches your academic background and career goals.
Location: Mohali, Punjab. Counselling hours: Monday to Saturday, 9:00 AM to 7:00 PM.
+91 98881 22442Call the admissions desk+91 98881 22442WhatsApp for a quick replyinfo@techcadd.comEmail us your questions
Your details are used only to contact you about this enquiry — never sold, never added to a marketing list.
Other routes
Popular Courses
Explore other career-focused programmes available across technology and digital skills.
Still deciding
Not Sure If Data Science Is the Right Choice After 12th?
A counselling session can help you understand the difference between Data Science, Data Analytics, Artificial Intelligence and other technology tracks.
Explore the curriculum, ask about the current Mohali session and choose a learning path based on your interests and career plans.
Get Started Today
- 6 months, 12th pass, classroom and 1-on-1
- Excel, Power BI, DAX and SQL before any machine learning
- Python taught as engineering — OOP, testing, logging and Git
- scikit-learn, XGBoost, LightGBM, CatBoost and PyTorch
- LLMs, embeddings, vector databases, RAG and AI agents
- Docker, AWS, Azure AI, Vertex AI, AI security and CI/CD
- 6 portfolio projects closing on an AI SaaS capstone