Data Science & Artificial Intelligence

A rigorous, industry-aligned 3-month immersive program designed to build strong theoretical foundations and practical expertise in modern AI systems.

Duration: 12 Weeks
Mode: Live Online + Guided Practical Labs
Format: Structured Modules + Capstone Project

Program Architecture

The curriculum is designed in progressive layers — from mathematical foundations to advanced AI system development.

I. Computational Foundations – Python & Data Handling

Develop strong programming skills essential for data-driven problem solving.

• Python programming (core & advanced concepts)
• Scientific computing with NumPy
• Data manipulation using Pandas
• Data visualization techniques
• Working with structured & unstructured datasets

II. Data Management & Query Systems – SQL

Understand relational databases and real-world data retrieval systems.

• Structured Query Language (SQL)
• Complex joins & nested queries
• Data aggregation & optimization
• Database integration with Python
• Applied database case studies

III. Statistical Foundations & Exploratory Data Analysis

Build mathematical intuition behind predictive modeling.

• Probability theory & distributions
• Inferential statistics
• Hypothesis testing
• Data preprocessing methodologies
• Feature engineering techniques

IV. Machine Learning Systems

Design, train, and evaluate predictive models.

• Supervised learning (Regression & Classification)
• Unsupervised learning (Clustering)
• Model evaluation & performance metrics
• Bias–variance tradeoff
• End-to-end ML pipeline development

V. Deep Learning Architectures

Explore neural network-based AI systems.

• Artificial Neural Networks (ANN)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN, LSTM)
• Transfer learning & pre-trained models
• Practical implementation using modern frameworks

VI. Natural Language Processing

Develop intelligent language understanding systems.

• Text preprocessing & tokenization
• Part-of-Speech tagging
• Named Entity Recognition
• Sentiment analysis
• Chatbot development

VII. Generative AI & Large Language Models

Work with modern AI architectures shaping the future.

• Large Language Models (LLMs)
• Prompt Engineering
• LangChain framework
• Retrieval-Augmented Generation (RAG)
• AI-powered application development

VIII. Capstone & Applied Research Project

Each participant will complete a comprehensive project involving:

• Problem formulation
• Data acquisition & preprocessing
• Model development
• Evaluation & optimization
• Presentation of findings

Professional Development

• Technical resume refinement
• Structured mock interviews
• Portfolio building
• Industry readiness mentoring

Learning Outcomes

Upon successful completion, participants will be able to:

• Design scalable ML & AI systems
• Apply statistical reasoning to real-world problems
• Build and deploy AI-powered applications
• Demonstrate job-ready competence in Data Science roles