Tech IT SolutionData Science + AI
Master end-to-end Data Science + Artificial Intelligence: Python, Statistics, Machine Learning, Deep Learning, GenAI, MLOps & Real-World Projects for top data & AI roles in 2026
Why join this course
A complete, end-to-end path into data science and AI: from Python and statistics to machine learning, deep learning, generative AI and deployment. You finish with capstone projects that prove you can take a model from idea to production.
End-to-end coverage
Python, ML, deep learning, GenAI and MLOps in one structured program.
Storytelling with data
Visualize and explain results to non-technical stakeholders.
Modern AI included
Transformers, LLMs, prompt engineering and RAG, not just classic ML.
Deploy your models
Serve models with FastAPI, Streamlit and Docker, and track with MLflow.
By the end you will be able to
- Python for Data Science & Statistics Fundamentals
- Data Wrangling, EDA & Visualization (Pandas, NumPy, Matplotlib, Seaborn)
- Machine Learning Algorithms & Model Building (Scikit-learn)
- Deep Learning & Neural Networks (TensorFlow, PyTorch)
- Generative AI, LLMs, Prompt Engineering & RAG
- MLOps, Model Deployment & Cloud AI (AWS, Azure, GCP basics)
- 100% Hands-on + End-to-End Projects + Industry Certificate
Who is this course for?
Add a job-ready skill next to your degree or diploma.
Upskill for a promotion, a better role or a career switch.
Use these skills to deliver client work or run your own business better.
Curriculum overview
6 modules, explained in detail. Open a module to see exactly what is inside.
1Python & Data Science Foundations6 topics
Build the base every data scientist needs: Python, statistics, linear algebra basics and fluent data handling with Pandas and NumPy.
What is inside
- Python Programming (Data Types, Functions, OOP)
- Statistics & Probability
- Linear Algebra Basics
- Exploratory Data Analysis (EDA)
- Pandas & NumPy Mastery
- Data Cleaning & Preprocessing
2Data Visualization & Storytelling7 topics
Turn numbers into stories. Learn visualization libraries and dashboards that make findings obvious to any audience.
What is inside
- Matplotlib
- Seaborn
- Plotly
- Interactive Dashboards
- Tableau / Power BI Basics
- Data Storytelling Principles
- Dashboard Design Best Practices
3Machine Learning Core5 topics
The heart of data science: supervised and unsupervised learning, evaluation, validation and tuning.
What is inside
- Supervised Learning (Regression, Classification)
- Unsupervised Learning (Clustering, Dimensionality Reduction)
- Model Evaluation & Validation
- Hyperparameter Tuning
- Ensemble Methods (Random Forest, XGBoost, LightGBM)
4Deep Learning & Neural Networks6 topics
Go deeper with neural networks for vision, sequences and transfer learning.
What is inside
- Neural Networks Basics
- CNNs for Computer Vision
- RNNs/LSTMs/Transformers
- TensorFlow / PyTorch Frameworks
- Transfer Learning
- Model Optimization & Regularization
5Generative AI & Large Language Models7 topics
Understand transformers and large language models, then build with them using prompts, fine-tuning and RAG.
What is inside
- Transformers & Attention Mechanisms
- Prompt Engineering
- Fine-tuning LLMs (PEFT, LoRA)
- RAG Systems
- LangChain / LlamaIndex
- Generative Models (GANs, Diffusion Models)
- Ethical AI & Bias Mitigation
6MLOps, Deployment & Capstone Projects7 topics
Ship your work. Deploy, track and automate models, then complete your capstone.
What is inside
- Model Deployment (FastAPI, Flask, Streamlit)
- Docker & Kubernetes Basics
- MLflow
- DVC
- CI/CD for ML
- Cloud Platforms (AWS SageMaker, Azure ML, GCP Vertex AI)
- 4–5 End-to-End Projects (Customer Churn Prediction, Image Classification, Sentiment Analysis, Sales Forecasting, GenAI Chatbot / RAG System)
What you will build
Real work for your portfolio, not just exercises.
Predictive ML model
Clean a real dataset, train, tune and evaluate a model with clear business impact.
Deep learning classifier
Build an image or text classifier with TensorFlow or PyTorch.
GenAI RAG assistant
Create an LLM app grounded in your own documents.
Deployed ML app
Package and deploy a model as a live web app or API.
Tools you will use
How you will learn
Career outcomes
Roles this course prepares you for:
- Resume & LinkedIn profile building
- Portfolio and project review
- Mock interviews with IT leads
- Hiring-partner drives and referrals
Certificate, batches & details
Data Science & AI Professional Certification
Issued after you pass the final evaluation of the course.
Flexible batches
Morning, afternoon, evening and weekend batches. Classroom at GTB Nagar or live online.
Course at a glance
| Duration | 7–10 months (Fast-track: 5–6 months) |
|---|---|
| Level | Beginner to Advanced |
| Mode | Classroom (GTB Nagar, Delhi) & live online |
| Teaching format | Live Sessions + Capstone Projects |
| Prerequisites | No prior experience needed. We start from the basics and build up step by step. |
| Batches | Weekday morning / afternoon / evening & weekend |
| Certificate | Data Science & AI Professional Certification |
| Fees | Call or WhatsApp for the current fee · EMI options available |
Frequently asked questions
Who can join Data Science + AI?
No prior experience needed. We start from the basics and build up step by step. Students, graduates, job seekers and working professionals are all welcome.
How long is the course and what are the batch timings?
The course runs for 7–10 months (Fast-track: 5–6 months). We run weekday morning, afternoon and evening batches plus weekend batches for college students and working professionals.
Are classes online or in the classroom?
Both. Attend live in our GTB Nagar (Delhi) classroom or join live instructor-led online classes from anywhere in India. Sessions are recorded, and you can switch between modes.
What certificate will I receive?
Data Science & AI Professional Certification, awarded after you pass the final evaluation.
Will I work on real projects?
Yes. Most of the training is hands-on labs and guided projects, and you finish with work you can show in your portfolio.
What jobs can I apply for after this course?
Typical roles include Data Scientist, Machine Learning Engineer, AI Analyst. Your outcome depends on your skills, portfolio and interview performance, and our placement team supports you at every step.
Do you help with placement?
Yes. We help with resume and LinkedIn profiles, portfolio review, mock interviews and hiring-partner drives. This is placement assistance, not a job guarantee.
What is the fee, and is EMI available?
Fees depend on the batch and mode. Call or WhatsApp us for the current fee and flexible EMI / installment options.
Can I attend a free demo class first?
Yes. Book a free demo class to meet the mentors and see how the training works before you enrol.
Related courses
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