Introduction to Big Data Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview (80-120 words) describing structure and time commitment.
Module 1: Welcome
Estimated time: 0.4 hours
- Introduction to the Big Data Specialization
- Course objectives and learning outcomes
- Engaging with the course community
Module 2: Big Data: Why and Where
Estimated time: 4 hours
- Origins and significance of Big Data
- Data sources: people, organizations, and sensors
- Case studies in healthcare, business, and technology
- Real-world applications of Big Data
Module 3: Characteristics of Big Data and Dimensions of Scalability
Estimated time: 2 hours
- The 6 V's of Big Data: Volume, Velocity, Variety, Veracity, Valence, and Value
- Understanding scalability challenges
- Solutions for scalable Big Data systems
Module 4: Data Science: Getting Value out of Big Data
Estimated time: 3 hours
- Introduction to the data science process
- Data acquisition and exploration
- Data preprocessing and analysis
- Communicating results effectively
Module 5: Foundations for Big Data Systems and Programming
Estimated time: 1 hour
- Distributed file systems overview
- Scalable computing concepts
- Programming models for Big Data processing
Module 6: Systems: Getting Started with Hadoop
Estimated time: 5 hours
- Hadoop ecosystem and architecture
- Components: HDFS, YARN, and MapReduce
- Hands-on: Installing Hadoop and running a simple program
Prerequisites
- Basic computer literacy
- No prior programming experience required
- Access to a system capable of running virtual machines
What You'll Be Able to Do After
- Understand the Big Data landscape and its real-world applications
- Identify and explain the 6 V's of Big Data
- Apply a structured process to analyze Big Data challenges
- Differentiate between Big Data and traditional data problems
- Install and run a basic Hadoop program for hands-on experience