Big Data Analytics with AI

1. Introduction to Big Data and AI
   • Overview of Big Data and Its Characteristics (Volume, Velocity, Variety)
   • Introduction to AI and Machine Learning in Big Data
   • Key Big Data Applications in AI (e.g., Healthcare, Finance, IoT)
2. Big Data Tools and Frameworks
   • Hadoop Ecosystem (HDFS, MapReduce, YARN)
   • Apache Spark for Big Data Processing
   • Introduction to Cloud Platforms (AWS, Azure, GCP) for Big Data
3. Data Storage and Management
   • Structured, Semi-Structured, and Unstructured Data
   • NoSQL Databases (Cassandra, MongoDB, HBase)
   • Data Warehousing and Distributed Storage Systems
4. Data Ingestion and ETL Pipelines
   • Data Collection and Integration
   • Real-time Data Streaming with Kafka and Flume
   • Building ETL (Extract, Transform, Load) Pipelines
5. AI and Machine Learning on Big Data
   • Introduction to Distributed Machine Learning
   • Scalable Machine Learning Algorithms on Spark MLlib
   • Working with Big Data in Python (PySpark, Dask)
6. Deep Learning for Big Data
   • Handling Big Data with Neural Networks
   • Using TensorFlow and Keras for Large-Scale Deep Learning
   • Distributed Deep Learning on Spark and Kubernetes
7. Natural Language Processing (NLP) on Big Data
   • Text Analytics with Big Data (Processing Large Textual Datasets)
   • Large-Scale NLP with BERT, GPT on Big Data Systems
   • Applications of NLP in Big Data (e.g., Sentiment Analysis, Topic Modeling)
8. Big Data Analytics with AI in Computer Vision
   • Handling Large-Scale Image Data
   • Distributed CNN Training with Big Data
   • Image Classification, Object Detection on Big Data Systems
9. Big Data Visualization and Insights
   • Visualizing Large Datasets with Tools like Tableau, Power BI
   • Big Data Dashboards and Reporting
   • Real-Time Data Visualization with AI-driven Insights
10. Big Data and AI Ethics
    • Ethical Concerns in AI with Big Data
    • Data Privacy and Security Issues
    • Bias in Big Data Analytics and AI
11. Big Data Case Studies and Real-World Applications
    • Use Cases in Healthcare, Finance, Retail
    • Case Studies on AI Applications (e.g., Fraud Detection, Predictive Maintenance)
    • Hands-On Implementation in Real-World Scenarios

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