Program Structure

Module 1: Introduction to Big Data and AI

• Understanding the characteristics of big data: Volume, Velocity, Variety.
• Introduction to AI and Machine Learning in big data.
• Real-world applications of AI in sectors like healthcare, finance, and IoT.

Module 2: Big Data Tools and Frameworks

• The Hadoop Ecosystem: Overview of HDFS, MapReduce, and YARN.
• Using Apache Spark for fast data processing.
• Introduction to cloud platforms for big data: AWS, Azure, GCP.

Module 3: Data Storage and Management

• Handling structured, semi-structured, and unstructured data.
• Overview of NoSQL Databases: Cassandra, MongoDB, HBase.
• Techniques for data warehousing and distributed storage systems.

Module 4: Data Ingestion and ETL Pipelines

• Collecting and integrating data from multiple sources.
• Real-time data streaming using Kafka and Flume.
• Building ETL (Extract, Transform, Load) pipelines to prepare data for AI analysis.

Module 5: AI and Machine Learning on Big Data

• Introduction to distributed machine learning.
• Implementing scalable machine learning algorithms using Spark MLlib.
• Using PySpark and Dask to process big data in Python.

Module 6: Deep Learning for Big Data

• Handling large-scale datasets with neural networks.
• Using TensorFlow and Keras for deep learning on big data.
• Distributed deep learning on Spark and Kubernetes for efficient training.

Module 7: NLP on Big Data

• Processing large textual datasets using AI-driven NLP techniques.
• Implementing BERT and GPT models for large-scale text analytics.
• Applications like sentiment analysis and topic modeling in big data systems.

Module 8: Big Data Analytics with AI in Computer Vision

• Handling large-scale image data and training distributed CNN models.
• Applications of image classification and object detection on big data systems.

Module 9: Big Data Visualization and Insights

• Visualizing large datasets using Tableau and Power BI.
• Building big data dashboards and reporting tools for decision-making.
• Real-time data visualization with AI-driven insights.

Module 10: Big Data and AI Ethics

• Addressing ethical concerns in AI and big data, including bias.
• Understanding data privacy and security issues.
• Case studies on the impact of bias and privacy challenges in big data analytics.

Module 11: Big Data Case Studies and Applications

• Use cases in healthcare, finance, and retail.
• Case studies on AI applications like fraud detection and predictive maintenance.
• Practical hands-on implementation of AI techniques on large datasets.

Final Project

• Students will complete a large-scale project applying AI techniques to real-world big data.
• Example projects: Customer behavior prediction, medical data analysis, or predictive maintenance models.