Introduction to Big Data

This is an introductory module welcoming students to the Big Data Specialization. It includes information about the course structure and expectations.

This module explores what makes data "big" and the sources of big data. It covers various applications of big data, success stories, and the three key sources: machine-generated data, people-generated data, and organization-generated data. The module emphasizes the value of integrating diverse data types.

This module examines the characteristics of big data through the lens of the "Big Vs". It covers the traditional 5 Vs (Volume, Variety, Velocity, Veracity, and Valence) and introduces a sixth V (Value). Students learn to understand these dimensions and how they affect data collection, storage, analysis, and reporting.

This module introduces a 5-step process for approaching data science problems to extract value from big data. It covers building a big data strategy, the five components of data science, asking the right questions, and the detailed steps in the data science process: acquiring data, exploring and pre-processing data, analyzing data, communicating results, and turning insights into action.

This module provides the foundational knowledge for big data systems and programming frameworks without requiring programming experience. It covers distributed file systems, scalable computing over the internet, and programming models for big data, providing a grounding in key concepts necessary for understanding big data technologies.

This module explores the details of Hadoop and MapReduce, including hands-on experience with performing a simple MapReduce task using a Docker container. It covers the Hadoop ecosystem, the Hadoop Distributed File System (HDFS), YARN resource manager, MapReduce programming model, cloud computing as a big data enabler, and practical implementation. Students learn by doing through diagramming a MapReduce task as a peer review assignment.