What will you in the Introduction to Big Data Course
Understand the Big Data landscape, including real-world applications and challenges
Identify the key characteristics of Big Data, often referred to as the 6 V’s: Volume, Velocity, Variety, Veracity, Valence, and Value
Apply a structured 5-step process to analyze Big Data effectively
Differentiate between Big Data problems and traditional data challenges
Gain foundational knowledge of Hadoop’s architecture, including HDFS, YARN, and MapReduce
Install and execute a simple program using Hadoop for hands-on experience
Program Overview
1. Welcome
Duration: 25 minutes
Introduction to the Big Data Specialization and course objectives
Engagement with the course community through discussion prompts
2. Big Data: Why and Where
Duration: 4 hours
Exploration of the origins and significance of Big Data
Examination of data sources: people, organizations, and sensors
Case studies highlighting Big Data applications in various sectors
3. Characteristics of Big Data and Dimensions of Scalability
Duration: 2 hours
In-depth analysis of the 6 V’s of Big Data
Discussion on scalability challenges and solutions in Big Data systems
4. Data Science: Getting Value out of Big Data
Duration: 3 hours
Introduction to the data science process tailored for Big Data
Steps include data acquisition, exploration, preprocessing, analysis, and communication of results
5. Foundations for Big Data Systems and Programming
Duration: 1 hour
Overview of distributed file systems and scalable computing
Introduction to programming models suitable for Big Data processing
6. Systems: Getting Started with Hadoop
Duration: 5 hours
Detailed look into Hadoop’s ecosystem and its components
Hands-on assignment involving the installation and execution of a simple Hadoop program
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Job Outlook
Aspiring Data Professionals: Build a strong foundation in Big Data concepts and tools
Business Analysts: Enhance analytical skills by understanding Big Data applications
IT Professionals: Gain insights into Big Data infrastructure and processing frameworks
Researchers: Leverage Big Data methodologies for data-driven research
Students: Prepare for advanced studies in data science and analytics
Specification: Introduction to Big Data
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