01
Students Straight Out of 12th
Students from different academic streams can begin with the fundamentals and gradually move into Python, analytics and AI development.
Start right after school
Best After 12th 4-Month Data Science Program in Mohali
A four-month fast-track journey from data fundamentals to practical AI development — learn Python, SQL, machine learning, deep learning, LLMs, RAG, AI agents and cloud deployment, and finish with an industry-focused AI SaaS capstone.
0113
01Where this starts
Course Overview
This four-month fast-track Data Science programme is designed for students who want to build practical data and AI skills after 12th without spending years before creating real projects.
The course begins with Excel, Power BI, Python and SQL before moving into data science, machine learning and deep learning. You then progress into computer vision, LLMs, embeddings, vector databases, RAG and AI agents.
The final month focuses on application development, cloud deployment, AI security and an industry-style AI SaaS capstone.
Month 1 — Data & Programming Foundations: Start with advanced Excel, Power Query, Power BI, DAX, KPI reporting and data literacy. Move into Python fundamentals with VS Code, object-oriented programming, exception handling, type hints, testing and coding practices. You will also learn Git, GitHub, SQL with PostgreSQL, database design, window functions and query optimisation. The month concludes with APIs, JSON, FastAPI basics, JWT, Postman, Pandas, NumPy, Polars, DuckDB and PyArrow.
Month 2 — Data Science, Machine Learning & Deep Learning: Learn how to clean and understand datasets through exploratory data analysis, feature engineering, statistics and visualisation. Build machine learning workflows with scikit-learn, pipelines and cross-validation. Explore advanced gradient-boosting models including XGBoost, LightGBM and CatBoost, followed by model evaluation and hyperparameter optimisation. The month also introduces PyTorch, tensors, neural networks and deep learning fundamentals.
Month 3 — Computer Vision, LLMs & Vector Search: Move into computer vision with CNNs, transfer learning and OpenCV. Then explore transformers, Hugging Face and tokenizers. Learn the foundations of large language models, including tokenisation, embeddings, attention mechanisms and prompt engineering. Work with major AI APIs and local model tools, followed by vector embeddings and databases such as FAISS, ChromaDB, Pinecone, Qdrant and Milvus for semantic search.
Month 4 — RAG, AI Agents, Deployment & Capstone: Learn how modern AI applications are built using RAG architecture, hybrid search, guardrails, LangChain, LangGraph, CrewAI, MCP, tool calling and multi-agent systems. Develop AI applications using FastAPI, asynchronous programming, WebSockets, Streamlit, Gradio and Chainlit. The final stage covers Docker, cloud deployment, AWS, Azure AI, Google Vertex AI, CI/CD, prompt-injection defence and responsible AI. You then bring everything together in an end-to-end AI SaaS capstone using FastAPI, PostgreSQL, RAG, AI agents and Docker.
Where it takes you
- Data Analyst
- Data Scientist
- Machine Learning Engineer
- Business Intelligence Analyst
- Big Data Engineer
02Skills you collect
What You'll Learn
Four practical outcomes, one per stage of the programme — a dashboard, a model, a retrieval assistant and a deployed application.
Build a Business Intelligence Dashboard
Use advanced Excel, Power Query, Power BI and DAX to create an interactive KPI dashboard and understand how businesses use data for reporting and decision-making.
Build and Evaluate Machine Learning Models
Create complete machine learning pipelines using scikit-learn and compare advanced models such as XGBoost, LightGBM and CatBoost.
Build a Working RAG Assistant
Learn how documents are converted into embeddings, stored in vector databases and retrieved to create an AI-powered question-answering system.
Develop an AI SaaS Application
Combine FastAPI, PostgreSQL, RAG pipelines, AI agents and Docker into a complete application that can be documented, deployed and presented as a portfolio project.
03The route
Course Curriculum
The syllabus is structured across four months, progressing from data fundamentals to advanced AI application development.
- Excel, Power BI & Data Literacy
- Advanced Excel
- Power Query
- Power BI
- DAX
- Business dashboards
- KPI reporting
- AI productivity
- Data literacy
- Python Fundamentals & Engineering Practices
- Python fundamentals
- VS Code
- Package management
- Object-oriented programming
- Exception handling
- Type hints
- pytest
- Ruff
- Black
- Git, GitHub & SQL Foundations
- Git
- GitHub
- Git Flow
- GitHub Copilot
- PostgreSQL
- Database design
- SQL queries
- Window functions
- Query optimisation
- APIs & Data Engineering
- APIs
- JSON
- FastAPI basics
- JWT
- Postman
- Pandas
- NumPy
- Polars
- DuckDB
- PyArrow
Practical, Project-Based Training
Every month ends in build work rather than revision — a Power BI dashboard and a SQL service in month one, an ML pipeline in month two, a vision build and a RAG assistant in month three, and the deployed AI SaaS capstone in month four.
Outcome
By completing the curriculum, students will have moved from Excel and SQL fundamentals through machine learning, deep learning, LLMs, RAG and agents to a deployed application, with six projects and a professional GitHub portfolio.
04Your toolkit
Tools You Will Actually Work With
Microsoft ExcelPower QueryPower BIPythonVS CodePostgreSQLGit & GitHubGitHub CopilotPandasNumPyPolarsDuckDBPyArrowscikit-learnXGBoostLightGBMCatBoostPyTorchOpenCVHugging FaceOpenAIMicrosoft ExcelPower QueryPower BIPythonVS CodePostgreSQLGit & GitHubGitHub CopilotPandasNumPyPolarsDuckDBPyArrowscikit-learnXGBoostLightGBMCatBoostPyTorchOpenCVHugging FaceOpenAIMicrosoft ExcelPower QueryPower BIPythonVS CodePostgreSQLGit & GitHubGitHub CopilotPandasNumPyPolarsDuckDBPyArrowscikit-learnXGBoostLightGBMCatBoostPyTorchOpenCVHugging FaceOpenAI
GeminiClaudeGrokOllamaLiteLLMFAISSChromaDBPineconeQdrantMilvusFastAPIStreamlitGradioChainlitLangChainLangGraphCrewAIDockerAWSAzure AIGoogle Vertex AIGeminiClaudeGrokOllamaLiteLLMFAISSChromaDBPineconeQdrantMilvusFastAPIStreamlitGradioChainlitLangChainLangGraphCrewAIDockerAWSAzure AIGoogle Vertex AIGeminiClaudeGrokOllamaLiteLLMFAISSChromaDBPineconeQdrantMilvusFastAPIStreamlitGradioChainlitLangChainLangGraphCrewAIDockerAWSAzure AIGoogle Vertex AI
- Microsoft Excel
- Business reporting and the first dashboards.
- Power Query
- Clean and reshape data before it reaches a report.
- Power BI
- Data models, DAX measures and KPI dashboards.
- Python
- The language the rest of the programme is built in.
- VS Code
- Where the code is written, run and debugged.
- PostgreSQL
- Relational database design and querying.
- Git & GitHub
- Version control and the portfolio employers read.
- GitHub Copilot
- AI assistance inside the editor.
- Pandas
- Load, clean and reshape tabular data.
- NumPy
- Numerical arrays underneath the analysis.
- Polars
- Fast dataframes for larger datasets.
- DuckDB
- Analytical SQL directly over local files.
- PyArrow
- Columnar data interchange between tools.
- scikit-learn
- Pipelines, cross-validation and model evaluation.
- XGBoost
- Gradient boosting for tabular problems.
- LightGBM
- A faster boosting alternative to compare against.
- CatBoost
- Boosting that handles categorical features directly.
- PyTorch
- Tensors, neural networks and deep learning.
- OpenCV
- Image processing behind the vision project.
- Hugging Face
- Transformers, tokenizers and pretrained models.
- OpenAI
- One of the model APIs behind the AI applications.
- Gemini
- A second provider in the same patterns.
- Claude
- A third provider, for comparison and structured work.
- Grok
- A fourth API surface to work against.
- Ollama
- Run local models on your own machine.
- LiteLLM
- One interface across several model providers.
- FAISS
- Similarity search over embeddings at speed.
- ChromaDB
- A lightweight vector store for local RAG work.
- Pinecone
- A hosted vector database for semantic search.
- Qdrant
- Vector search with filtering and payloads.
- Milvus
- Vector storage at larger scale.
- FastAPI
- Serve models and RAG pipelines behind an API.
- Streamlit
- Turn an analysis into an interactive app.
- Gradio
- Quick interfaces for model demos.
- Chainlit
- Chat interfaces for LLM applications.
- LangChain
- Chains, tools and the glue around model calls.
- LangGraph
- Agent graphs, state and conditional routing.
- CrewAI
- Multi-agent orchestration and task delegation.
- Docker
- Package the finished application for deployment.
- AWS
- Cloud hosting for the deployed capstone.
- Azure AI
- A second cloud AI platform to compare.
- Google Vertex AI
- Managed model training and serving.
05Is this you?
Who Can Do This Course
02
Students With a Gap Before College
Use the months after school to build practical technical skills, complete projects and create a portfolio before starting your degree.
03
Commerce & Arts Students
You do not need to already be an advanced programmer. The programme starts with fundamentals and introduces statistics and programming progressively.
04
Degree Students Who Want a Head Start
Students entering BCA, BBA, B.Sc or related programmes can use the course to develop practical data and AI skills alongside their academic studies.
05
Students Exploring Data & AI
If you are unsure whether Data Science is the right career path, a four-month structured programme gives you an opportunity to experience analytics, machine learning and AI development through practical work.
06
Self-Taught Learners
If you have learned from scattered tutorials but lack complete projects, structured training and trainer feedback can help you turn individual skills into a portfolio.
06Why this one
Why This Programme Is Worth Four Months
01
Excel, Power BI & Data Literacy
Start with practical business reporting using Excel, Power Query, Power BI, DAX and KPI dashboards.
02
Python & SQL
Develop strong programming and database fundamentals that support analytics, machine learning and application development.
03
Data Engineering & Machine Learning
Work with Pandas, NumPy, Polars, DuckDB and PyArrow before building machine learning pipelines with scikit-learn and gradient-boosting models.
04
Deep Learning & Computer Vision
Understand neural networks, PyTorch, CNNs, transfer learning and OpenCV through practical projects.
05
LLMs, RAG & AI Agents
Go beyond basic AI prompts and learn embeddings, vector databases, RAG architecture, AI agents and modern orchestration frameworks.
06
Cloud Deployment & Capstone
Learn Docker, cloud deployment, CI/CD and AI security before completing an end-to-end AI SaaS application.
07Why now
One Fast Track From Data to AI Applications
In four focused months, move from Excel and SQL fundamentals to machine learning, deep learning, LLMs, RAG, AI agents and cloud deployment.
Instead of finishing with only certificates and notes, the programme is designed around practical projects that can become part of your portfolio.
The fast track, month by month
- Month 1 — FoundationsExcel, Power BI, Python, Git, SQL and the data engineering stack.
- Month 2 — ModelsEDA, statistics, scikit-learn, gradient boosting and PyTorch.
- Month 3 — Vision & LLMsCNNs, transformers, embeddings and vector databases.
- Month 4 — ApplicationsRAG, agents, FastAPI, Docker, cloud and the AI SaaS capstone.
Talk to a Course Advisor
Ten minutes with a course counsellor settles eligibility, session timings, fees and where this leads — before you commit four months to it.
08Certification
Get Certified in Data Science
Complete the programme with practical projects and receive a course completion certificate. Your project work can also be organised into a professional portfolio for interviews, internships and future applications.
Industry CertificateDemonstrate completion of structured Data Science and AI training.
Project PortfolioBuild multiple practical projects that can be presented during interviews.
Capstone ProjectComplete an end-to-end AI application as the final project.
Placement SupportGet support with CV preparation, interview practice and career guidance.
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
What Job Roles Can You Explore?
Depending on your skills, portfolio and further experience, potential career paths include:
Roles this programme prepares you for
Clean, query and report on business datasets.
10What you build
Hands-On Projects You Will Ship
01
Business KPI Dashboard
Create an interactive Power BI dashboard using Power Query transformations and DAX measures to present important business KPIs. Month 1 · Power BI · DAX
02
SQL Data Service With FastAPI
Design a PostgreSQL database, write advanced SQL queries and expose selected functionality through a FastAPI application. Month 1 · PostgreSQL · FastAPI
03
End-to-End Machine Learning Pipeline
Clean and analyse a real-world dataset using Pandas and Polars, then build and evaluate machine learning models using scikit-learn and advanced boosting techniques. Month 2 · scikit-learn · XGBoost
04
Computer Vision Build
Develop a computer vision project using PyTorch, CNNs, transfer learning and OpenCV. Month 3 · PyTorch · OpenCV
05
RAG Assistant Over Documents
Create an AI assistant that processes documents, generates embeddings, retrieves relevant information from a vector database and produces responses through an LLM. Months 3–4 · LangChain · Vector Database
06
Industry AI SaaS Capstone
Develop a complete AI application combining FastAPI, PostgreSQL, RAG, AI agents and Docker, with deployment documentation and a professional GitHub presentation. Month 4 · AI SaaS · Capstone
11How it works
Learn It. Build It. Make It Yours.
Every project follows a simple learning cycle.
Understand
Understand the requirement, break it into smaller tasks and choose the right tools.
Business KPI DashboardBuild
Develop the project hands-on with guidance and trainer feedback.
SQL Data Service With FastAPIPresent
Explain your approach, demonstrate the final project and turn your work into a portfolio story.
End-to-End Machine Learning Pipeline12Why techcadd
Why Students Choose techcadd
Practical, beginner-friendly training on a current stack, structured so every stage ends in portfolio work.
01
Trainers Focused on Practical Learning
Learn through practical examples, exercises and projects rather than relying only on theoretical lessons.
02
Designed for Beginners
The programme starts with fundamentals so students coming directly after 12th can gradually build their technical confidence.
03
Classical Data Science + Modern AI
Learn traditional machine learning alongside LLMs, RAG, vector databases and AI agents.
04
Current Technology Stack
Work with modern tools used across data analytics, machine learning and AI application development.
05
Real API-Based AI Learning
Understand how AI applications work with model APIs, local models, embeddings, vector databases and application frameworks.
06
Portfolio-Focused Training
The programme is structured around projects so that you can finish with practical work to demonstrate during internships and interviews.
13Who has walked it
Students who started where you are
Alumni of this route, on what changed once they were sitting in interviews.
4.7
194 reviews
582%
411%
34%
22%
11%
I did my B.Tech but never got hands-on data science exposure in college. Techcadd's course changed that completely — real projects, real datasets, not just theory. The capstone project became the centerpiece of my resume.
Was doing B.Com and wanted a technical pivot. Honestly intimidating at first, but the phase-wise structure — starting with Python before jumping into ML — made it manageable. Got a Data Analyst interview call soon after finishing.
Commute daily from Chandigarh and the structured curriculum was the biggest plus. Machine learning modules were tough but the real-world case studies made concepts stick far better than any online course I'd tried before.
Working in a non-IT job and wanted a genuine career switch. This course gave me practical Python, SQL and ML skills, not just certificates. The Tableau and Power BI modules were outstanding — we worked with real business data.
Best decision after graduation — chose this over just adding another degree. Learned the entire pipeline from Python to visualization. Feels like a genuine head start compared to peers with just theoretical knowledge.
Already had programming background but statistics and ML were new territory. Trainers connected everything to real business scenarios, which made the learning curve much less overwhelming than self-study would have been.
Working in software testing and wanted to pivot into data science. The evening sessions let me train without leaving my job. The advanced ML module with XGBoost and ensemble methods was genuinely challenging but rewarding.
Travelled from Derabassi and it was completely worth it. Trainers don't rush through algorithms — they explain the business reasoning behind each technique. The capstone project gave me something real to show in interviews.
Solid, comprehensive course for someone serious about data science. I'm a B.Sc graduate and this gave me the practical Python and SQL skills my degree never covered. The structured phases made a huge difference.
Was intimidated by machine learning before this course. Now I'm comfortable building models, evaluating performance and visualizing results. Genuinely one of the better structured programs I researched in Mohali.
Took this as a serious investment toward a data science career. The phase-wise approach — Python, stats, ML, SQL, visualization, big data — meant nothing felt rushed. 1:1 sessions meant real attention when stuck.
Practical, project-heavy training. As an MBA student, the hands-on capstone project gave me far more real exposure than my college case studies ever did. Would recommend to anyone serious about a data career.
14Before you go
Frequently Asked Questions
Still unsure? A ten-minute call with a counsellor usually settles it faster than any brochure.
The fast-track programme is designed to be completed in four months, covering data fundamentals, Python, SQL, machine learning, deep learning, LLMs, RAG, AI agents, deployment and a final capstone.
15Final stage
Ask About Data Science Program in Mohali
Have questions about the course, curriculum, fees, session timings or career options? Speak with a course counsellor to understand whether this four-month Data Science programme matches your goals.
Location: Mohali, Punjab. Counselling hours: Monday – Saturday, 9:00 AM – 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.
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Still deciding
Not Sure If Data Science Program Is the Right Fit?
A quick conversation with a course advisor can help you understand the curriculum, learning path, course duration and career options before you enrol.
By the end of the programme, you can develop a broader understanding of the modern data and AI workflow — from collecting and cleaning data to training models and deploying AI applications.
You will work with
- Python-based data analysis
- SQL and databases
- Data engineering tools
- Machine learning
- Deep learning
- Computer vision
- LLM applications
- Vector databases
- RAG systems
- AI agents
- FastAPI
- Docker
- Cloud AI platforms
- AI security
- GitHub portfolio development