Big Data

Module 1: Introduction to Big Data

• Overview of Big Data

  • Definition and Importance of Big Data
  • The 5 V's of Big Data: Volume, Velocity, Variety, Veracity, and Value
  • The Role of Big Data in Business and Society

• Historical Context

  • Evolution of Data Management
  • Traditional vs. Big Data Analytics

Module 2: Big Data Technologies

• Big Data Ecosystem

  • Introduction to Big Data Architecture
  • Key Components: Data Sources, Data Storage, Data Processing, and Data Analytics

• Storage Solutions

  • Overview of NoSQL Databases: MongoDB, Cassandra, HBase
  • Introduction to Hadoop and the Hadoop Distributed File System (HDFS)

Module 3: Data Processing Frameworks

• Hadoop Framework

  • Understanding MapReduce
  • Setting Up and Configuring Hadoop

• Apache Spark

  • Overview of Spark: Components and Architecture
  • Spark Core and Spark SQL
  • DataFrames and Datasets in Spark

Module 4: Data Analytics Techniques

• Types of Data Analytics

  • Descriptive Analytics: Techniques and Tools
  • Predictive Analytics: Introduction and Techniques
  • Prescriptive Analytics: Understanding Decision-Making

• Analytics Tools

  • Overview of BI Tools: Tableau, Power BI
  • Introduction to Programming for Data Analysis: Python and R

Module 5: Data Visualization

• Importance of Data Visualization

  • Principles of Effective Visualization
  • Tools for Data Visualization: Tableau, Matplotlib, Seaborn

• Creating Visualizations

  • Interactive Dashboards and Reporting
  • Best Practices for Data Presentation

Module 6: Big Data in Various Industries

• Applications of Big Data
  • Big Data in Healthcare: Patient Analytics and Predictive Modeling
  • Big Data in Finance: Fraud Detection and Risk Management
  • Big Data in Retail: Customer Insights and Inventory Management

Module 7: Big Data Challenges and Solutions

• Data Quality and Management

  • Issues with Data Quality: Accuracy, Completeness, Consistency
  • Data Governance and Compliance

• Scalability and Performance

  • Strategies for Optimizing Big Data Performance
  • Managing Data Privacy and Security

Module 8: Future Trends in Big Data

• Emerging Technologies

  • The Role of Artificial Intelligence and Machine Learning
  • Big Data and the Internet of Things (IoT)
  • Data Lakes vs. Data Warehouses

• Future Directions

  • Trends and Predictions for Big Data Analytics

Module 9: Capstone Project

• Practical Application of Big Data Knowledge
  • Development of a Big Data Solution for a Real-World Problem
  • Presentation and Defense of the Project

Assessment:

• Quizzes and Assignments: Regular assessments to evaluate understanding of theoretical concepts
• Hands-on Labs: Practical exercises using big data tools and technologies
• Final Capstone Project: A comprehensive project demonstrating knowledge and skills in big data