Big Data - Capstone Project

This module provides an overview of the Eglence, Inc. Pink Flamingo game, including various aspects of the data which the company has access to about the game and users. It covers the game concept, data model, and project design to establish the foundation for the capstone project.

This module begins working with the simulated game data by exploring and preparing it for ingestion into big data analytics applications. Students learn to download game data and associated scripts, understand CSV file formats, and explore data using Splunk for data exploration, aggregate calculations, and filtering.

This module focuses on data classification using KNIME. It reviews classification techniques using decision trees in KNIME, provides guidance on interpreting decision trees, and presents a workflow overview for building a decision tree. Students learn to classify data to identify big spenders in the Catch the Pink Flamingo game.

This module covers clustering techniques using Spark. It focuses on informing business strategies based on client base analysis and provides practice with PySpark MLlib clustering. Students learn to recommend actions from clustering analysis and discuss clustering methodologies, optimal cluster numbers, and criteria for actionable business information.

This module applies knowledge from the 'Graph Analytics With Big Data' course to simulated chat data from the Catch the Pink Flamingos game using Neo4j. Students analyze player chat behavior to find ways of improving the game, understand the simulated chat data format, and perform graph analytics using Neo4j.

This short module focuses on bringing all the previous analyses together. It provides guidance on final project preparation and creating cohesive reports that integrate the insights gained from various big data analytics techniques applied throughout the course.

The final module focuses on submitting the completed capstone project. It includes congratulatory remarks, guidance on connecting the project video to LinkedIn profiles, and the submission of both the final project and an optional 3-minute video for a Splunk opportunity. This module represents the culmination of all the big data analytics skills applied throughout the course.