Big Data Analytics & Artificial Intelligence: From Fundamentals to Integration

• Understand Concepts: Learn the fundamentals of Big Data, AI, and their integration.
• Build Skills: Gain hands-on experience with tools like Hadoop, Spark, and AI algorithms.
• Apply Knowledge: Solve real-world problems using Big Data and AI techniques.
• Explore Trends & Ethics: Discuss emerging technologies and address ethical considerations like privacy and bias.
• Boost Careers: Develop in-demand skills for roles in Big Data, AI, and related fields.

🔹 Day 1: Foundations of Big Data & AI

• Key Characteristics of Big Data (5Vs)
• Fundamentals of AI and Machine Learning
• Real-world Applications Across Domains
• Hands-On: Data exploration & visualization using Python (Colab)

🔹 Day 2: Tools, Techniques & Integration

• Overview of Hadoop, Spark, Kafka (Conceptual)
• AI Techniques: Clustering, Classification, NLP
• Big Data-AI Integration Strategies
• Hands-On: ML model development using PySpark or scikit-learn

🔹 Day 3: Advanced Topics & Implementation

• Emerging Areas: Edge AI, AutoML, Federated Learning
• AI Governance: Fairness, Explainability, Deployment Challenges
• Scalable Architecture on Cloud Platforms
• Hands-On: End-to-end mini project – Ingest → Train → Visualize

1. Understand the fundamentals of Big Data and AI, including their integration.
2. Gain hands-on experience with tools like Hadoop, Spark, Python, and R.
3. Apply AI algorithms to real-world datasets for practical problem-solving.
4. Explore industry applications and emerging trends in Big Data and AI.
5. Earn a certificate to validate their skills.
6. Build a foundation for careers in Data Science, AI, and Big Data.

• Students: From computer science, data science, engineering, and related fields.
• Professionals: Data analysts, IT experts, and AI enthusiasts.
• Academicians & Researchers: Incorporating Big Data and AI into their work.
• Entrepreneurs: Leveraging data for growth.