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Domain 03 . Data & AI

AI Data Science

300–400
Hours of teaching . 6–9 months
All levels
Level
17
Modules
Classroom . Online . Hybrid
Mode
Course overview

A full master programme covering the data science and artificial intelligence stack from Python to deployed models. The programme runs from programming and mathematics through data science practice, machine learning, deep learning, computer vision and natural language processing, then into generative AI, prompt engineering, agents, retrieval-augmented generation, MLOps, analytics and cloud AI services.

Module durations are fixed in hours and every module carries practical work. The final 50 hours are given to industry projects selected from the capstone list.

Who it is for
  • Graduates targeting data science and AI roles
  • Working software and IT professionals switching tracks
  • Analysts moving from reporting into modelling
  • Engineering and MCA students in final year
Prerequisites
Basic computer knowledge and programming fundamentals. Python recommended; revised in Module 2.
How it runs
Learn → Practise → Build → Experience → Demonstrate, ending in a capstone. Delivered by practitioners from the engineering bench, in Madurai, Coimbatore and online.

Programme sheet

The printed sheet carries the full module breakdown, labs, project work and certification path. Fees, dates and formats for the next intake are confirmed by the education team on enquiry.

Download the programme sheet →Enquire or apply →

Learning outcomes

01
Write production Python for data work with NumPy, Pandas and Plotly.
02
Run the full data science cycle from collection to feature engineering.
03
Build deep learning models in TensorFlow, Keras and PyTorch.
04
Build NLP pipelines for classification, summarisation and chatbots.
05
Deploy and monitor models with Docker, MLflow, FastAPI and cloud APIs.
06
Apply linear algebra, probability and optimisation to model behaviour.
07
Train, tune and evaluate supervised and unsupervised models.
08
Deliver computer vision systems for detection, recognition and OCR.
09
Apply generative AI, prompt engineering, agents and RAG to business cases.
10
Present findings in Power BI and Tableau dashboards.

Module structure

17 modules
Module 01

Introduction to Artificial Intelligence

Introduction to AI . AI vs Machine Learning vs Deep Learning vs Generative AI . Types of AI . Future of AI . PRACTICAL . History and Evolution of AI . AI Applications Across Industries . AI Ethics and Responsible AI
Module 02

Python Programming for AI

Python Basics . Variables & Data Types . Operators . Loops . Functions . Modules & Packages . Object-Oriented Programming . File Handling . Exception Handling . Virtual Environments
Libraries — NumPy, pandas, Matplotlib, Plotly, Seaborn
Lab
Data Cleaning . Data Manipulation . Data Visualization
Module 03

Mathematics for AI

Linear Algebra . Vectors . Matrices . Eigenvalues . Probability . Statistics . Bayes Theorem . Optimization . Gradient Descent
Lab
Matrix Operations Using NumPy . Statistical Analysis
Module 04

Data Science with Python

Data Collection . Data Wrangling . Feature Engineering . Data Visualization . Data Pipelines
Mini Project — Sales Data Dashboard
Lab
Kaggle Datasets . Real-World Data Analysis
Module 05

Machine Learning

Supervised Learning . Unsupervised Learning . Reinforcement Learning . Regression Algorithms . Decision Trees . Random Forest . K-Means Clustering . Model Evaluation . Hyperparameter Tuning
Libraries — scikit-learn
Lab
Customer Churn Prediction . House Price Prediction . Spam Detection
Module 06

Deep Learning

Backpropagation . RNN . GRU . Attention Mechanism . PRACTICAL . Activation Functions . LSTM . Transformers
Frameworks — TensorFlow, Keras, PyTorch, n8n, Image Classification, Digit Recognition, Time-Series Prediction
Module 07

Computer Vision

Image Processing . Image Classification . Object Detection . Object Tracking . Face Detection . Face Recognition . OCR . Image Segmentation . YOLO . OpenCV
Lab
Face Recognition System . Vehicle Detection . Number Plate Recognition
Module 08

Natural Language Processing (NLP)

Text Processing . Tokenization . Stop Words . Lemmatization . Stemming . TF-IDF . Word Embeddings . Word2Vec . FastText . BERT . Named Entity Recognition . Sentiment Analysis . Text Summarization . Machine Translation
Libraries — NLTK, spaCy, Hugging Face
Lab
Chatbot Development . Sentiment Analysis . Resume Classifier
Module 09

Generative AI

GPT Architecture . Transformer Models . Tokens & Embeddings . Open-Source vs Closed Models . Multimodal AI . Responsible AI . os
Popular Models — GPT, Claude, Gemini, Llama, DeepSeek, Mistral, Qwen
Lab
AI Content Generator . AI Research Assistant . AI Code Assistant
Module 10

Prompt Engineering

Zero-Shot Prompting . One-Shot Prompting . Few-Shot Prompting . Tree-of-Thought Prompting . System Prompts . Prompt Chaining . Prompt Optimization . Structured Outputs
Lab
Prompt Library Creation . Business AI Assistant
Module 11

AI Agents & Automation

AI Agent Architecture . Memory . Planning . Tool Calling . Function Calling . Multi-Agent Systems . Workflow Automation
Frameworks — LangChain, LangGraph, CrewAI, AutoGen, OpenAI Agents SDK
Lab
Customer Support Agent . Research Agent . HR Assistant . Email Automation Agent
Module 12

Retrieval-Augmented Generation (RAG)

Document Processing . Embeddings . Semantic Search
Vector Databases — Hybrid Search, RAG Architecture, FAISS, ChromaDB, Pinecone, Weaviate
Lab
PDF Chatbot . Enterprise Knowledge Assistant
Module 13

MLOps & AI Deployment

Model Versioning . Experiment Tracking . Data Versioning . Docker . Kubernetes . MLflow . Weights & Biases
Monitoring & Logging — Model Deployment, API Development (FastAPI), Streamlit, Gradio
Lab
Deploy ML Models . Deploy LLM Applications . Dockerize AI Projects
Module 14

Data Analytics

REPORTS
Power BI — Data Import, Data Cleaning, Data Modeling, DAX, Power Query, Dashboards
Tableau — Data Connections, Charts, Dashboards, Storytelling
Lab
Business Dashboard . Sales Analytics Dashboard
Module 15

Cloud AI Services

OpenAI API . Azure OpenAI . Google Vertex AI . AWS Bedrock . Hugging Face Inference API . API Integration . Authentication
Lab
Build AI Applications Using Cloud APIs
Module 16

AI Security & Responsible AI

AI Risks . Prompt Injection . Jailbreaking . Hallucinations . Model Bias . Privacy & Security . AI Governance . Ethical AI Development
Module 17

Industry Projects

Capstone Projects — Enterprise AI Chatbot, AI Resume Analyzer, AI Interview Coach, AI Medical Assistant, AI Legal Assistant, AI Customer Support System, AI Document Summarizer, AI Research Assistant, AI Sales Forecasting, Computer Vision Attendance System, Intelligent Traffic Monitoring, Predictive Maintenance System, AI Voice Assistant, AI Financial Advisor

Assessment & certification

Module assignments and labs
25%
Internal assessments
15%
Mini projects
20%
Capstone project and review
40%

Learners who complete all modules, submit the capstone project and clear the final review receive a course completion certificate from Kaizen Infinities Private Limited. Project work is documented for the learner's portfolio and GitHub profile, and interview preparation is included in the final week.

Career outcomes . Roles this programme prepares for
Data ScientistMachine Learning EngineerAI EngineerComputer Vision EngineerNLP EngineerData AnalystMLOps EngineerGenerative AI Developer
Enquire or apply →Programme sheet (PDF)Institutions can commission a cohort

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