Program Structure

Module 1: Introduction to Big Data Analytics and AI

• What is big data and why is it important in the modern world?
• Overview of AI and machine learning and how they can be applied to big data.
• The role of data engineers, data scientists, and AI practitioners in big data analytics.
• Real-world use cases of big data analytics powered by AI.

Module 2: Working with Big Data Frameworks

• Introduction to big data frameworks like Hadoop, Spark, and NoSQL databases.
• How to set up and use Hadoop Distributed File System (HDFS) for storing and managing large datasets.
• Using Apache Spark for distributed data processing and analysis.
• Hands-on project: Process and analyze a large dataset using Apache Spark and Hadoop.

Module 3: Data Preprocessing for Big Data

• Data cleaning, transformation, and feature engineering for big data applications.
• Handling missing data, outliers, and noise in large datasets.
• Using Pandas and PySpark for data manipulation and cleaning.
• Hands-on project: Preprocess a large dataset and prepare it for AI model training.

Module 4: Machine Learning Algorithms for Big Data

• Introduction to machine learning algorithms for big data analytics: regression, classification, and clustering.
• Implementing supervised and unsupervised learning algorithms using big data frameworks.
• Exploring deep learning techniques for large-scale data analysis.
• Hands-on project: Train machine learning models on big data using Spark MLlib.

Module 5: AI for Predictive Analytics

• How AI can be used for predictive analytics in big data: forecasting, anomaly detection, and trend analysis.
• Building predictive models using time-series data and AI algorithms.
• Evaluating model performance using appropriate metrics and techniques.
• Hands-on project: Build a predictive analytics model on big data using machine learning algorithms.

Module 6: Big Data Visualization and Insight Extraction

• Data visualization techniques for big data: using Matplotlib, Seaborn, Tableau, and Power BI.
• Techniques for creating interactive visualizations to extract insights from large datasets.
• How to communicate complex data analysis results using data storytelling.
• Hands-on project: Create an interactive data dashboard using real-world big data and AI insights.

Module 7: AI and Big Data in Real-World Applications

• How big data and AI are transforming industries like healthcare, finance, e-commerce, and manufacturing.
• Use cases of AI-powered big data solutions in real-time decision-making and operational improvements.
• Integrating AI with big data platforms for end-to-end data-driven solutions.
• Hands-on project: Build an AI-powered big data application for a real-world use case (e.g., customer segmentation, demand forecasting, etc.).

Module 8: Ethics, Privacy, and Governance in Big Data

• Ethical challenges and privacy concerns related to big data and AI applications.
• Understanding data governance and compliance regulations (GDPR, CCPA, etc.) in big data systems.
• Ensuring fairness, transparency, and accountability in AI-driven big data applications.

Final Project

• Design and implement a complete big data analytics solution using AI algorithms and frameworks.
• Build an end-to-end machine learning model that processes and analyzes large datasets, extracts insights, and provides actionable recommendations.
• Example projects: Build a recommendation system, predictive maintenance solution, or customer behavior analysis tool using big data and AI.